Tag: artificial intelligence

  • Designing a Future of Innovative UX, Without UI

    Designing a Future of Innovative UX, Without UI

    The long-fabled future of a world without traditional human-to-computer interactions is no longer science fiction. It’s a reality. Consumers all across the globe are wearing computers on the wrists, talking to artificial intelligence to check the weather and monitoring body functions through micro-devices. This is just the tip of the iceberg of what is possible through creative technology design, and yet the masses still see holograms and implantations as far-off science fiction, rather than the very close and possible future.  

    Designers and developers are leading the charge into the future, but not without their fair share of challenges and hurdles. With a blank slate of endless potential, how can we design for a simple UX while still having a visually appealing product? Moreover, what if, in the near future visuals are not required at all?

    The New Frontier of Design

    Holograms, audio controls, wearables … new technologies have the potential to disrupt the screen entirely. Society is teetering on the edge of the uncanny valley of a world without traditional UI, and designers must take the reigns. 

    Take audio design, for example. Voice technology and virtual assistants are already rendering many screen functions moot. Technology leaders such as Apple, Google, Samsung, Amazon and beyond are pouring resources into voice, creating a screenless interface simple and intuitive enough for any consumer to use. Google, for example, has made creating an app on the Google home quite simple though Google Console. Designers can have fun building the tone, language and personality for the app easily in as little as five minutes. While it’s true that we live in a world where some still battle this technology, it’s also the case that audio assistants are carving out a place for themselves in our home and our lives. The technologies powering audio controls are being developed and refined quickly, so learning how to design for it now is key. 

    Itcher, a recommendation engine that helps users discover movies, music, television shows, bestselling books, games and more, requires no UI at all. The voice-powered platform allows users to ask their voice assistant for a recommendation, prompts them with questions to better understand his or her tastes and preferences and leverages artificial intelligence to provide optimal recommendations. 

    Designing for audio navigation is not necessarily fully intuitive. More so than with desktop screens or handheld devices, interactive audio treads on the brink of the uncanny valley—imitating humans in many respects but still not quite convincingly human. Audio designers like the team at Itcher must consider factors such as lexicon, pitch, volume, direction and most importantly, value for users to want to interact with a voice-controlled device. 

    One contributing factor for the rise of audio is the rise of wearable technology such as smartwatches, AR smart glasses and beyond. Designed to be connected to consumers at all times, wearables are blurring the lines between technology and its users. As the wearables become better optimized for everyday use, the screens will shrink, and likely disappear altogether. This will make audio design more valuable than ever before. 

    While people are still going to be very dependent on traditional screen interfaces for quite some time (especially for complex tasks), there is no doubt the world is hurtling towards a future where screens are no longer a necessity, but an option. Designers and developers no longer have the luxury of waiting for the next big thing to enter mainstream—it’s here. 

    Focus on the U (User)

    No matter if you are designing for a wearable, a voice assistant or a mobile app, customer feedback is an important aspect of innovation and design that often gets overlooked. Of course, the first thing a designer must consider should always be the end user—know the business and customer requirements so they are making the best product that will fill the needs of both. A designer that does not approach a project by asking many “why” questions of both the company and the target audience will not have the information they need to build the best and simplest interface she or he can.

    Innovative thinking is important at all points of the design process, but to ground design in reality rather than getting trapped in science fiction aspirations, designers must have both quantitative and qualitative research to better understand not only the functionality success of a product or platform, but also the user sentiment. This requires heavy interaction with stakeholders (both internal and external) at every stage of the design process. 

    Bring customers in to soundboard ideas, test products and platforms and engage on a deeper level with the business. At Shutterstock, we speak to customers on a regular basis, hold focus groups, and use user testing platforms to ensure we have customer feedback at many steps along the way. It’s vital for customers to be able to interact with products and platforms along with designers. This allows for greater creativity and most importantly, simplicity in UX. 

    Focus on Simplicity

    Shakespeare’s quote “Brevity is the Soul of Wit,” from Hamlet, is typically interpreted for works of writing, encouraging authors to be concise. However, this is also true of all creative endeavors, writing, art and design. Making things simple (concise) takes hard work, it takes time, it takes skill and giving yourself the space as a designer to do that work is critical to finding success in creating a “simple” design.

    Designing something simple is one of the hardest things every designer faces. Sure, it’s easy for the designer to come up with a simple interface that answers the customer’s needs at a cursory level, but often there are additional requirements or stakeholder feedback that one also has to take into account. It’s important to remember that product, UX and engineering are all parts of an equal trifecta. Designer and developer questions, as well as conversations with real customers about how to solve a user’s need, will only make a product better in the long run. The challenge for designers is balancing immediate feedback from their company and users with forward-thinking innovation. 

    It is often not intuitive for both businesses and consumers alike to imagine a future that pushes the boundaries of what exists. Designers are uniquely positioned to bring abstract innovation and technology to the masses through simple and intuitive UX.

    — Erin Essex

  • CIOs See Dramatic Change Ahead

    CIOs See Dramatic Change Ahead

    Driven by heightened competition and a rapidly changing business environment, many enterprises now find themselves faced with decisions that could make or break their future.

    Consider this: Within three years, 44% of respondents to the 2019 Harvey Nash / KPMG CIO Survey expect to fundamentally change their product, service offerings or business model. It’s a finding that spans enterprises both large and small, the survey said.

    This could explain why IT leaders reported their technology budget to be increasing the most in 15 years. The report found 45% of respondents favor budget increases for money-saving technology efforts, such as task automation. Interestingly, almost two-thirds, or 63% of organizations, report that considerable amounts of their enterprise technology efforts are managed outside of the control of their IT department.

    However, the survey showed those organizations where the IT team is formally involved in business technology decision-making have better business outcomes. These enhanced outcomes include improving new product time-to-market and employee experience to be better than their competitors. Still, four in ten companies are not formally involving IT in those business-led IT decisions.

    Increased investments in automation and artificial intelligence (AI) are expected to place considerable churn on the workforce. CIOs are expecting to witness one in five jobs being replaced by AI/automation within the next five years. However, 69% of CIOs also expect enough new jobs to replace those positions lost to automation.

    “Boards are asking their CIO and technology team to prioritize automation of jobs. How organizations adapt to automation will increasingly become a priority, and many are not at all ready,” said Albert Ellis, CEO of Harvey Nash.

    Despite CIOs enjoying a rising influence within their organizations, fewer CIOs have seats on the board of directors at their organization—having fallen significantly from 71% who held board seats just two years ago to 58% today.

    The 2019 Harvey Nash/KPMG CIO Survey, conducted between December 2018 and April 2019 across 108 countries, is based on the responses of 3,645 CIOs and technology leaders.

    While automation and AI may, or may not, displace workers in the future enterprises are counting on them today to help close their perceived skills shortage. According to respondent’s, enterprises continue to find it a challenge to obtain the talent they need. In fact, respondents reported skills shortages at their highest level since 2008.

    The employees most difficult to find are those in data analytics (44%), cybersecurity (39%) and artificial intelligence (39%).

    Finally, respondents surveyed who consider their organizations to be digital leaders (those who see themselves as very effective or extremely effective at using digital technologies to advance their business strategies, performed better than their competitors on every aspect surveyed.

    According to the survey, digital leaders outperformed in time to market (53% versus 34%), customer experience (65% versus 49%), revenue growth (55% versus 43%) and profitability in the previous year (50% versus 37%). Digital leaders, the survey said, are also more likely to introduce ‘major new changes to products and services’ in the next three years (55% versus 39%) and focus on profitability.

    — George V. Hulme

  • DevOps Chats: The Future of Farming with Aerobotics

    DevOps Chats: The Future of Farming with Aerobotics

    Spinnaker Summit 2019 Preview: Never underestimate an entrepreneur looking to solve a real-world problem. Aerobotics is bringing drones, imagery, mapping, containers, Kubernetes, Spinnaker, data pipeline processing and machine learning to agriculture. Sounds interesting, right?

    Aerobotics uses aerial drones and sophisticated image processing to help growers manage yield, pest and disease in their farms through artificial intelligence.

    Speaking at the upcoming Spinnaker Summit in San Diego, Aerobotics’ Head of Software Nick Coles, shares with us how his company evolved from making drones to map orchards into a software company using raw image data to create high resolution, georeferenced maps of orchards. Aerobotics technology is comprised of a map engine and tree engine. It’s both a fascinating problem space and exciting application of cloud-native technologies, including Spinnaker.

    This episode of DevOps Chats features a preview of Nick’s talk, “The Future of Farming with Aerobotics.” Nick’s talk is on Sunday, November 17 12:30 PM, at Spinnaker Summit 2019 in San Diego.

     

    Transcript

    Mitch Ashley: Hi everyone. This is Mitch Ashley with devopsy.kinsta.cloud, and you’re listening to another DevOps Chat podcast. Today I’m joined by Nick Coles, head of software at Aerobotics. This is a special edition of DevOps Chat, because we’re talking about talks at the Spinnaker Summit 2019, which is in San Diego this year.

    Our next topic is the future of farming with Aerobotics, aka drones. So you’re gonna be really interested to hear about this. This talk is on Sunday, November 17th at 12:30 p.m. Nick, welcome to DevOps Chat.

    Nick Coles: Hey Mitch. Thanks for having me. Really excited to be on this podcast.

    Ashley: Happy to have you here. Tell us a little bit about yourself. Tell us about what you do at Aerobotics and a little bit about the company.

    Coles: Cool, so I guess just from the very beginning, born and raised in Cape Town, South Africa, as you can probably hear by the slightly strange accent. [Laughs]

    Ashley: Detected. We detected a slight accent there. [Laughter]

    Coles: Yeah. And but yeah, born and raised Cape Town, South Africa. Studied at the University of Cape Town, where I did a electromechanical engineering degree, which was kind of, you know, embedded systems, microcontrollers, processes and stuff. And that’s kind of why I got interested in drones and robotics. And, yeah, just over four years ago joined Aerobotics and sort of–at the time it had just been funded, so there were essentially two people, and I kind of joined in a software engineering role, not really knowing what I was doing.

    Ashley: [Laughs]

    Coles: And yeah, kind of been there ever since heading up the software engineering team. Which obviously a huge component of it has been DevOps and back-end processes. And so that’s kind of why I sort of have been exposed to Spinnaker and, yeah, kind of what led me to this today.

    Ashley: Excellent. So it’s the true startup experience, which means your job is you do whatever needs to get done, and you kind of evolve into your current role, it sounds like.

    Coles: Yeah, pretty much. I mean I joined–back in about four years ago when we joined it’s basically we were a company that was building drones, building the hardware, sort of soldering the motors to the propellers. And essentially drones kind of transformed throughout our journey, and we ended up having to move out of the hardware space and peel into the software space, because companies like DJI came up with these commoditized perfect drones. And it was quite hard to compete with them when you’re, what, five engineers in a garage soldering drones on a piece of [Crosstalk]–

    Ashley: There are a lot of drones available and you can differentiate in the software. So I read from the description of your talk that Aerobotics uses aerial drone imagery to help farmers, growers, manage their yield, pest, disease, that kind of stuff on farms, using some AI. Is that a problem that you started initially with, or did the company evolve to that kind of problem? How did you get started, and the company get started around that area, specifically?

    Coles: So, I mean, the company has actually had quite an interesting sort of journey in that regard. So the idea in the beginning was we are drones. We build drones. We use drones for agriculture. We use drones for security, game counting, mining. We tried to essentially just throw drones at any sort of problem.

    And sort of as we progressed as a company and more and more players sort of entering the field, and we sort of started honing in on our value proposition, and started specifically focusing on tree crops within the agriculture space because it’s such a high value. And so it actually then makes sense for the cost for specific tree crops.

    But originally we weren’t using any AI. We were kind of just trying to use raw sort of computer vision and object detection. The data that we were seeing is so vast, and case-by-case is so unique, that we kept having to change the parameters, slightly tweak things. And so the only sort of long-term solution could’ve been machine learning. And so that’s kind of what we embarked on about two to three years ago and sort of been, yeah, what we’ve been using ever since to try to essentially generalize the imagery we were receiving and sort of try to replicate the human behavior to the best of its ability.

    Ashley: Okay, excellent. So I know your software staff appreciate that about the company in kind of the broadened space. I know your software stack is a combination of, what, Kubernetes, of course you’re using Spinnaker, I think Circle CI for your CI/CD. Tell us a little bit about how you got involved with Spinnaker initially. Was that something that you chose or kind of evolved there kind of by accident? How did you happen down that path?

    Coles: So, I mean, it is–it did happen by complete accident. Traditionally when we were processing all this data we were just spinning up EC2 instances. And so we were essentially just sending SQS messages and spinning up instances. And instances would essentially be able to sort of, I don’t know, kind of retrieve these messages and then figure out what sort of processing to do. You know, it was an absolutely terrible solution for instance _____ a drone with a code. We’d have to SSH into the instance, patch it, figure out what was going on.

    Ashley: Oh boy.

    Coles: At this time also it wasn’t–like, DACA hadn’t really sort of like exploded. And or maybe it had in sort of like San Francisco and everything. But in Cape Town we weren’t really exposed to all these sort of advanced kind of tech solutions. So we had quite an archaic little approach going on, which was working, I guess, with R Scale.

    And then in the beginning of last year we got accepted into Google Launchpad, which was really exciting. Four of us went across to San Francisco, and we basically went on this launchpad program where they kind of helped us all the way from technical to business. And it really was inspiring to kind of go to San Francisco for the first time and sort of see like just the scale of things and just like the intensity of the tech companies there and really what they were doing.

    And we kind of got put in touch with a couple of mentors. And one of them, based on sort of what the problems we were trying to solve were and the problems that we were facing, recommended Spinnaker. And at that time we were kind of on AWS’ stack. And so we kind of then got incentivized to sort of try, set up a system on Google. And you know, we had like Spinnaker set up, Google’s Kubernetes’ engine, and kind of we just took it from there.

    And just it was an interesting journey, ‘cause there was quite a lot of iteration within like Spinnaker itself and in terms of just how the community’s progressed over the last sort of two years, but also just sort of like where we were at in terms of not really understanding how Spinnaker worked, the containers spiraling out of control, realizing we needed to stabilize our Redis pods. Like our Redis pod basically caused so many problems early on.

    But yeah, so that kind of was the journey in terms of how we got involved in Spinnaker. So it was basically just by chance somebody mentioned it, and we kind of were like, wow, this shows the problem we’ve been having.

    Ashley: Like a great solution.

    Coles: And we kind of just ended up, yeah, just going with it.

    Ashley: Fantastic. So from what I understand is the drones that–just giving our audience a little bit of context–the drones that are flying around the orchards, for example, are collecting all of this data. And that goes up into some data processing in the cloud, correct? And it does, I guess, splits into two areas. One is a map engine that creates–takes that raw imagery and creates multilayered geomaps of the orchard. And then the tree–a tree engine, which also then looks at specifically where exactly are the trees. And that’s how you do your processing. Is that a correct description of how this works at a high level?

    Coles: So it is basically quite a good description. So just to maybe give like slightly more detail on like the map engine side, we are–I don’t know if you mentioned–but we are using multispectral cameras attached to the drones. And from that data we can get information that isn’t necessarily visible to the human eye. And that kind of gives us all a health and powerful readings from the plots.

    Ashley: Is that like using infrared kind of things, or what?

    Coles: Exactly. Exactly. Infrared sensors. And so, yeah, so the map engine side is essentially like the big data processing side of things where we’re taking thousands of thousands of images and stitching them into these high resolution maps of individual orchards. I mean, just to give sort of some context on kind of what the data is there, we generally produce about like these five gigabyte high resolution images, which is of a specific orchard. And the kind of process around getting the raw imagery to that five gigabyte sort of image, we use about 64 gig RAM and 16 CPU instances. So that’s kind of just like the scale of what you’re running in. It can take anywhere between two hours to 24 hours to form that stitching process.

    And then moving onto the tree engine, this is sort of where our machine learning models are running in production. And that’s essentially taking these high resolution maps and segmenting the image to get individual trees from that image. And from that we can then calculate additional metrics, such as the height, the health, and the area of the individual trees.

    And one thing that, I guess, we left out because we are still developing it, is we now have a fruit engine, which is essentially now like kind of our new product focus area where we’re going from the drone, which is kind of taking a high level of the orchard, and now we’re going right up to the individual trees. And from those trees we’re starting to detect fruits and sizes from the fruits. And the goal here is to give the farmers an accurate sort of size distribution of the fruit in the orchard so they can plan accordingly when it comes to harvest time.

    Ashley: They’re all–it’s all about yield, of course, and quality of the yield as well. And by the way, I come from–I’m not a farmer, but I grew up in the middle of Nebraska in the U.S., so I’m around a lot of farmers. So I have some idea of what you’re talking about, at least an appreciation for it.

    Well how did–tell us a little bit about why you chose to do this talk at Spinnaker Summit and some of the things that you want to communicate beyond kind of the problem set or the solution that you’ve come up with.

    Coles: Okay, so one of the reasons why I thought it would be quite cool to speak at the summit was, I guess, just based off like chatting to some of the Spinnaker guys on the Slack channels and everything, it feels like we’re using it in quite like a unique sort of way. I mean, we’re using it very much to manage essentially our data pipelines. And I guess we’re almost using it in a similar way to how someone may use something like Apache Airflow or something.

    And I thought that the combination of the uniqueness of how we’re sort of using it, and sort of just to kind of explain how it has completely changed the way we’ve been able to develop and iterate super quickly, I thought it’d be worth kind of sharing that with the community and sort of seeing how other people were using it in comparison. And so I guess, yeah, that was kind of just the main thing, just the type of data that we’re sending through the system is quite different. It’s quite unique, in comparison to the rest of the world.

    You know, we process these large images which come up with a–which have a whole bunch of different problems with them. You know, we have to ensure that our containers are responsible, they’re not using too much RAM, they’re not spiking in terms of, you know, being out of memory because they’re loading an image all at once. And there’s just been a lot of interesting things that we’ve had to sort of develop to kind of handle our case. So I thought it’d be cool to share that with the community and take it from there.

    Ashley: You know, I think it’s super relevant, and not everyone may have an agricultural problem domain, but with a massive quantity of data that we’re collecting through sensors, whether it be drones or IoT and home or IoT and the business or any device, any systems that are collecting all this data, one of the real challenges is how do you process this much information? How do you combine it together? How do you take different sources like your map engine versus the tree engine, look at it, and then do that analysis and produce valuable, interesting results?

    I think a lot of businesses, whether they be startups or enterprises, have those kinds of projects and challenges, or at least they’re thinking about it, if they’re not already doing that. And I bet you’ve got some valuable lessons that you’ve learned over time about why you set up the pipelines the way you did or how to handle some of the production challenges.

    Coles: Yeah, you know, it’s also we had an interesting space, also, because we’ve learned so much over the course of the last two years. Like when you’re doing things in production, you also very quickly start figuring out what it should be. And so there’s even more and more of an end-goal, if that makes sense.

    Ashley: Mm-hmm.

    Coles: So like as a small example, kind of, you know, we’ve set up all of our Spinnaker pipelines, and it’s been amazing. And the devs have been able to iterate really fast. And now we’re starting to see that we want to maybe start introducing some stability to our pipelines. And so to achieve that you want to start setting them up as infrastructure code. And like just starting to build that whole process into the development cycle is obviously quite challenging and takes time. But it’s allowed us to sort of think forward and continually improve on what we’ve been doing.

    So you always feel like you have kind of found the best solution at a point in time. You’re like, wow, this is absolutely incredible, and it’s going so well. And then six months from then, you’re kind of like, wow, it could be so much better. There’s so much more we can still keep doing to improve again. So I guess that’s what’s been quite an awesome part about using Spinnaker. It’s like at every sort of milestones it’s kind of like you can keep iterating and making it better and using it more efficiently.

    Ashley: That’s excellent. When you use a tool like Spinnaker open source to a software like that, that you feel like you’re just getting started sometimes. You thought you knew what you could do with it, and you find out there’s eight or ten different things that it evolves to from where you started.

    Coles: A hundred percent.

    Ashley: Well good. I wish you the best of luck on your talk. You know, I think you may get some folks attracted just to come hear about the drones. I hope you’re gonna have some pictures of maybe some of the orchards and the maps and some of the output that you do, is that correct, just to give folks an idea of kind of how this works?

    Coles: Yeah, we’ll definitely, I think, do a very brief sort of couple minutes on the product and just kind of explaining what we’re trying to do, and for sure. ‘Cause I guess it’s always nice to sort of give context of what Spinnaker is actually producing at the end of the day.

    Ashley: Mm-hmm. Well very good. Well it’s been great talking with you, and I wish you the best with the talk. And hopefully you get a lot of folks coming that are excited to hear about what you’re doing. Is this the first talk that you’ve given at a conference like this, or you’ve done that before?

    Coles: No, this would be the first talk, so I’m quite excited for that.

    Ashley: Really? Excellent. Well you know it’s–there’s lots of ways to give back to an open source community, and of course, writing code is certainly one of ‘em. But most people don’t write code. And I think one of the great ways of giving back is doing exactly what you’re doing, is going and talking, not just in the community and online forums and Slack channels or whatever they are–that, too–but participating in talks like this.

    Because it–I’ll predict you’ll have a lot of folks come up to you after the talk and want to know more or talk about what they’re doing and tell you about their idea or ask your advice on something. So it’s a great way that advances the software and the use of it, because you’re doing things that probably other people never expected or predicted. So congrats. That’s awesome you’re doing it.

    Coles: Thank you. Yeah, I guess that’s one of the things I’m most looking forward to is just sort of meeting a whole bunch of new people and kind of, I guess, like we learn so much, like especially kind of being in South Africa, not kind of in the tech hub of the world, I would guess, is when you go there and you start speaking to some people who are kind of leading the way in terms of the industry, it’s so valuable just to kind of hear what they have to say and what they’re using and how they’re solving problems. Because it’s so relevant and it can translate so nicely back home. And so, yeah, it’s always so much value in just meeting the people and just chatting through things.

    Ashley: Well excellent. Nick, thank you for joining the podcast today.

    Coles: Thanks Mitch. Will you be at the summit in a month’s time?

    Ashley: I don’t think I’m gonna be able to be there. I’ve had several invitations [Laughs]. If I can I will be.

    Coles: [Laughs] Okay.

    Ashley: But I’m like back-to-back for the next month going to conferences. So we’ll see. Hopefully. If I make it I will definitely look you up.

    So I’d like to thank Nick Coles, head of software for Aerobotics, joining us today. Again, he’s speaking at the Spinnaker Summit 2019, the dates are November 15th to the 19th in San Diego. By the way, Nick, you’ll enjoy San Diego, too. It’s a great city like San Francisco is. His topic is the future of farming with Aerobotics, aka drones. And on Sunday November 17th at 12:30 p.m. So please check out his talk.

    And thanks to all you for joining us today on this podcast and listening to Nick and I chat about his talk. This is Mitch Ashley with devopsy.kinsta.cloud, and you’ve listened to another DevOps Chat. Be careful out there.

    — Mitchell Ashley

  • DevOps Chats: Autonomous Test Innovation Using AI/ML with Functionize

    DevOps Chats: Autonomous Test Innovation Using AI/ML with Functionize

    At the speed of DevOps, automated testing is essential for QA to maintain pace with that of software creation. Automated testing is ripe for innovation, too. How do we know we are performing the most relevant tests, that new functions in the software aren’t being missed or overlooked, or that highly dynamic applications aren’t outpacing static test cases and logic?

    Functionize aims to bring innovation to achieve autonomous testing, making testing, the creation and maintenance of tests more efficient. Founder and CEO Tamas Cser joins us on this episode of DevOps Chat to share several innovations his company brings to DevOps teams.

    Join us as we discuss how writing test cases in English help capture intent and result in less brittle tests over time, and about the ability to reach into Functionize during run time and programmatically change object models and internals. Learn how AI improves visual detection of web pages and changes in web page behavior, and how AI can root out test cases that may no longer be valid as software functionality changes.

    As usual, the streaming audio is immediately below, followed by the transcript of our conversation.

    Transcript

    Mitch Ashley: Hi, everyone. This is Mitch Ashley with devopsy.kinsta.cloud, and you’re listening to another DevOps Chat podcast. Today, I’m joined by Tamas Cser, who is Founder and CEO of Functionize. Our topic today is a very interesting one—AI machine learning for automated DevOps testing. Tamas, welcome to the DevOps Chat podcast.

    Tamas Cser: Hey, Mitchell, it’s great to be here. Thanks for having me.

    Ashley: Well, absolutely. It’s my pleasure. Thank you for joining us. Would you start out by introducing yourself, tell us a little bit about you and also about Functionize?

    Cser: Absolutely. So, my name is Tamas Cser, and I’m the founding CEO of Functionize. I’ve been in technology for about 15 years, started as a consultant running my own company here in the Bay Area focused around development and we did, also, a lot of DevOps and building infrastructures kind of initially just saw posted and then cloud hybrid.

    And during those years, I bumped into testing a lot of times and really being at the front of the house and running the company and dealing with customers. It really become very apparent that testing is lacking in a lot of areas, and as DevOps got more and more initially mature and got traction, then we got into it more and, again, testing is a huge problem.

    So, really, that’s when I got into testing, tried to look at the problem, understand what opportunities we have now with cloud and big data that have not been explored and how that could be applied to solve some of these problems and this is really how Functionize was born about four years ago.

    Ashley: Mm-hmm.

    Cser: And ever since, it’s been an amazing ride. And now, you know, four years in, we have really acquired a great team and we have a very exciting product and customers and just, it’s been an incredible journey.

    Ashley: Great. Well, I definitely wanna hear more about the product and et cetera. Let’s start maybe with, in a software deployment world, especially in a DevOps world where we’re automating and we’re shifting things left, testing is only as good as the actual testing that happens. Bad testing automated is still bad testing.

    What are some of the hindrances that you see as we’ve changed to more of a DevOps style container, cloud? What kinds of things has that introduced into the testing criteria and how do you approach solving that differently than maybe a traditional, like with a Selenium style tool or something like that?

    Cser: I think that the largest change that comes with all of the incredible progress that we have made in software development, if you think about the entire DevOps toolchain and cloud and a lot of the automation that happens including containers is speed.

    And so, as with speed, software gets deployed much more frequently, and companies are able to iterate very, very fast around their product. And that provides a huge challenge for regression testing as well as getting testing on any of the new features that you’re releasing, and the maintenance especially around it is a huge challenge.

    So, I would say that, really, that speed that we have seen recently with some of these newer methodologies and then agile, multiple releases per day is really putting a huge pressure on the quality departments. And so, there’s huge opportunity to improve around that and really bring that up to speed, if you will, versus QA that has been kind of sitting stagnant for a long time, not seeing a lot of innovation if you look back the past 15 to 20 years.

    Ashley: Well, you know, it stands to reason, if you’re producing a lot more software much quicker, you need to test, obviously, much more quickly. So, automation, of course, is very important.

    What do you also think about the problem of how do you know to test the right things when you’ve got the right kind of coverage, and keeping in mind, you know, you may have developers, DevOps, engineers as well as QA people that are defining what those test criteria are?

    Cser: So, I think coverage is a very interesting topic and it’s an important topic that a lot of our customers have, how much coverage should I have, do I have the right coverage? And this is also an area where we can collect data and understand and analyze data from the application itself and even from the live user usage, in order to see where the patterns are, what’s being used, what potential impact it might have on the software if a particular feature or a functionality would break.

    And so, again, I think there’s a huge opportunity in bringing that—this is certainly one area that we’re focused on with our autonomous next generation capability where we’re trying to close the gap on that and have an impact on companies’ abilities to analyze and understand what, really, they need to test.

    Ashley: I believe, if I recall right, with your technology, your product, you actually write the tests in kind of an English format, right, rather than code? Is that correct?

    Cser: So, yes, we do have an English version of the desk creation capability. That’s not the only one, but it’s one of the key areas we’re innovating around is to understand user intent and what the test case needs to do and be able to take modeling around that, and that becomes really interesting and really important in how your test cases are maintained over time.

    So, these test cases become a lot less brittle for the very specific implementation and particular button or HTML element interaction. And so, that’s a really exciting area that we’re innovating in.

    Ashley: Excellent. So, I would imagine also, being part of a CI/CD process, you have to integrate with a lot of different tools or at least fit into a framework where, you know, you obviously don’t own the process end to end. Tell us a little bit about how you’ve approached that.

    Cser: So, the way we approach that—and again, this is a super important part of DevOps and building a piece of technology, these days especially, because the toolchain is incredibly important. And so, the way we approach it is two ways: One, we have generic APIs and give out of the box solutions to integrate with kind of your usual suspects—GitHub and Jira—

    Ashley: And Jenkins.

    Cser: And various different CI tools like Jenkins.

    Ashley: Mm-hmm, and Agira, yeah.

    Cser: The other piece that we’re doing that’s really interesting is, we’re—we have an ecosystem within Functionize that allows our users, basically, to program our run time environment that actually executes the tests.

    Ashley: Mm-hmm.

    Cser: And this opens up all of the object models and kind of hidden internals of the actual test case running. This is really, really interesting, because it gives you the capability to do a really, really deep level integration even in the middle of an execution with third-party applications or any other external functionality that you might have to perform that’s kind of a complex and advanced use case.

    Ashley: Now, am I correct in reading into what you’re saying in that while the tests are running, you can programmatically control Functionize and alter or adjust its behavior of what it’s testing or how it’s testing or something related to that?

    Cser: Precisely, precisely. So, this is kind of a first in the market capability and beyond just controlling sort of the outcome of the test case or the behavior, you also get access to the rich data sets that we’re collecting, including all of the visual screenshots or videos that we’re collecting doing execution.

    Ashley: Mm-hmm. And I understand, too, you use some AI machine learning in some pretty unique ways with screen captures, maybe other ways in the product. Talk a little bit about that.

    Cser: Absolutely, I’ll be happy to. So, as applications run again and again, obviously, how the application behaves and looks is really important. So, there’s one area that customers care about just around visual testing, which is, “Is my application displaying the way that I’m expecting it to and customers would expect it to?” So, it’s one key area that we’re working on and, with template recognition capabilities that can detect breakages on the page and this is based not on traditional kind of, you know, pixel by pixel computation, looking at the difference, but actual, deep learning that can understand how your page normally looks and where we normally see changes versus maybe change sets that looks like anomaly.

    Ashley: Mm-hmm.

    Cser: And then there’s another area—also, this is really interesting. Part of this that can be used in the root cause analysis, for example, of a page to understand what changed, what new elements were introduced and potentially have a test case that now is no longer valid could be automatically updated.

    Ashley: Oh, so, certain scenarios, maybe a page has changed sufficiently that the test no longer makes sense any more to run, that kind of a situation?

    Cser: Exactly. You mentioned that somebody added a couple of new form fields to a form and now those are required and you can’t complete the test. So, that’s something that we can do with analysis, visual analysis, as well as some DOM analysis can recognize an automatic usage and some solutions around it.

    Ashley: Wow, we’ve come a long way from the screen scraping vector graphics days of testing UIs, haven’t we? [Laughter]

    Cser: Oh, definitely. [Laughter] There’s a lot of exciting work that’s gone into it. And again, we are obviously drawing down on incredible open source capabilities as well and the advances that we’ve seen over the last year in an LP as well, so it’s really exciting progress that’s being made in the industry in general.

    Ashley: Excellent. So, talk a little bit about the cloud environment and, you know, we’re living in a world of, you may be a cloud-native application, but you may also be in a hybrid private cloud, maybe even not even a cloud data center, you know, private data centers and also multi-cloud. What are some of the challenges those environments bring and how might you tackle that?

    Cser: So, that’s a great question. Cloud environments in general are wonderful to work with for us and customers that are in the cloud and utilizing the cloud are good to work with for us. It does not represent any kind of a major challenge. It actually makes it much easier for us to work together.

    I would say that the cloud hybrid environments or on-prem environments certainly pose a challenge and there’s various different ways that we work with customers like that to still be able to bring the value of the cloud and the scale and kind of a lot of the modeling that we do and be able to still test applications that are hidden behind a firewall.

    Ashley: Mm-hmm. Is that also because of just the environments, maybe the testing tools, et cetera, that are in a traditional data center, private data center environment just are unique to that environment versus a cloud environment is more standardized if you’re in Azure or Google or AWS—is that why?

    Cser: I would say it’s more, it has to do with the access. It’s the way that we can access the application. It’s the client’s ability to provision environments that might be dedicated for testing, the way those environments can get the right resources, compute resources so it can handle the load that we will put on it as you go into agile testing. Versus in a traditional environment where the hardware is limited and potentially, you know, you have to deal with some secure tunneling into the system which is gonna slow it down. And so, overall, your speed decreases and the flexibility around those environments decrease. And the customer’s ability to provision new hardware, let’s say, because they would like to move faster for testing is greatly diminished.

    Ashley: And one of the things awesome about entrepreneurs is, oftentimes, they take a problem that they’ve had in their career, maybe their last job and say, “There’s a better way to do this. Let me go create a product, create a company, go do that,” since that’s part of your story, too.

    What was it that, in your experience with software and testing being a hindrance in and of itself? Were there specific problems or just, there’s gotta be a better mousetrap, or how did you—what did you bring with you from that experience to spin off and crate Functionize?

    Cser: That’s a great question. It really has to do with the pain, right? Getting the call from an angry customer when a particular bug regressed for the third time that week because your processes are not there—it’s really, really painful. And so, that’s really the initial starting point.

    The second piece that I would say that makes it a little bit unique is that—and we talk about this is, as well as shift left is really important so you can start testing early on. You’re taught it’s really important to shift right. It’s really important to test production environments because there’s a lot of things that happen these days when the application is literally compiled in real time in the browser with lots of real time dependencies, third parties and APIs and whatnot. So many things can go wrong in production that may not be actually code defects, they might have to do something with some other defect or some other dependency problem.

    So, that’s certainly one area that I am bringing Functionize in a heavy way that, and kind of the way that I see the future going, going both ways. We’re shifting left at the same time we’re also thinking about how do we test production.

    Ashley: Interesting. You know, you certainly hear a lot of shift left in test environments, not so much things happening in production environments. Are there certain, you mentioned applications kind of assembling, if you will, being brought together inside the browser at run time in a production environment.

    Are there other use cases like this for testing and production? I’m really curious about this.

    Cser: Oh, absolutely. There is many, and a few that I can think of that would be really interesting is a lot of the personalization that also is happening these days. So, we see customers struggle also with kind of these new style marketing capabilities that are operationalizing and these experiences that you would display to the user. It’s very difficult and challenging to test, difficult to see from the production environment. You have kind of the right data and the right experience, if you will, showing up.

    And so, that also provides opportunities for us to create specific products and features within Functionize to attack that problem.

    Ashley: Mm-hmm. It seems also like—this is, you know, sort of an edge case maybe today, we’ll see more and more of it, but serverless applications that are more event driven, you know, not traditional transaction driven might also be a good production test case for you.

    Cser: Oh, I agree. So, certainly, that would be an early, let’s call it corner case, rush case, but I think that we’re gonna see more and more of these coming as companies mature and these technologies become more mainstream.

    Ashley: Mm-hmm. Well, kinda going in a little bit of a different direction here, a little birdie told me that you all are up for some type of AI machine learning award—what’s happening with that?

    Cser: Well, we were just recently nominated for the AIconics Award here in San Francisco. So, we’re excited to be part of that. We’ll be attending and seeing how it goes, obviously. We’re honored to be there and being nominated. So, that’s very exciting to see a company being recognized for the work that we’re doing.

    Ashley: That’s awesome. Well, I wish you best with that and good luck. Also, I know there’s some things happening on the partnership for Functionize—what’s happening there?

    Cser: Yes, absolutely. We have a lot of great traction, and so, we are about to announce shortly, probably at the end of the quarter or early Q4 a very strategic partner, one of the largest global service innovators who are partnering with Functionize and we’re going to market together which is, again, I’m really proud of the team, and it shows the work that we’re doing and the work that marketing is doing to raise the awareness of the power of AI and machine learning and how that’s gonna change testing.

    Ashley: Mm-hmm. So, we’ll look forward to maybe some announcements coming up in the fourth quarter, then. Gotta keep an eye out for that. Great.

    Cser: Absolutely.

    Ashley: Yeah. So, I’ll kinda put you on the spot here, a little bit. If you had to say what some of the best practices that you’ve learned, not only at Functionize but also before when it comes to automated testing, you know, DevOps, cloud world—if you could boil that down to two or three best practices, where would you start? What would you tell people?

    Cser: I would say that finding the right tool and building the right toolchain is definitely incredibly important. The second I would say, like everywhere else, I would say you do need the right people and so, training and understanding of how do we apply these tools is absolutely critical.

    And then lastly, I would say that your strategy in that application, obviously, going back to the earlier conversation of what to test and how to test is gonna be critical for your success. And so, the tool, obviously, is primarily the area and certainly education is the second area that we’re very focused on, too.

    Ashley: I’m curious [Cross talk]—I’m curious about that, too. What are the kind of things that you look at or look for in people from testing skill or kinda the skills that you invest in current staff for training and skill development? What are some of those things that you look for?

    Cser: Can you clarify the question? In terms of look for in skill sets we’re hiring or skill sets—[Cross talk]?

    Ashley: So, your second item that you mentioned was the kind of capabilities and skills of your staff in testing and hiring those kinds of folks, also training for testing folks. What are some of those things that you’ve learned to look for that would be good advice for potential customers or current customers of Functionize?

    Cser: Yes, that’s a great question. So, I would say that, you know, obviously, like in any hiring, you want to hire bright people who are very motivated, so I’m assuming that’s the baseline.

    As far as the skill set goes, I think we definitely are looking at, obviously, professionals that have a lot of experience, but also, we can really enable and help people that are primarily manual or, let’s say, much less technical today and bring them into the world of automation and really drive a lot of value for our customer base. Because we see a lot of customers struggling with scaling and finding the right technical talent. So, I would say that that’s absolutely a critical point on being able to spread this to a wider audience, but at the same time I think that having technical users in part of the project, I think, is still very important.

    Ashley: Okay. Very good. Well, as always happens on these podcasts, we’ve run out of time. Tamas, thank you so much for being on the podcast today.

    Cser: Thanks for having me. It’s been a pleasure to be here.

    Ashley: My pleasure as well. Thank you to Tamas Cser, Founder and CEO of Functionize for joining us today and also, of course, to you, our listeners for joining us. You’ve listened to another DevOps Chat. This is Mitch Ashley with devopsy.kinsta.cloud. Have a great day. Be careful out there.

    — Mitchell Ashley

  • Building Your Data Science Team from Within

    Building Your Data Science Team from Within

    The global machine learning (ML) industry is increasing at a compound annual growth rate of 42% and will be worth $9 billion toward the end of 2022. Despite this figure, there are many cases of data science teams’ failure to deploy a reliable artificial intelligence (AI) product or service.

    Part of the failings and shortcomings of AI concern the ramp or process for getting there. For an AI project to succeed, it is critical to have your data fully deployed, available, structured and cleaned. You also must have some algorithms (ML/deep learning/natural language processing) ready to pull the insights and focused data, effectively putting the intelligence into AI.

    This requires a very specific skill set and talent pool.

    Empower Your Data Analysts

    With so many different technology fields colliding, machine learning is a difficult area to master. Deep reinforcement learning (DRL), natural language processing (NLP), AutoML tools, ML Ops, neural networks and model-based reinforcement learning are just some of the subjects required to master ML.

    With a shortage of talent and shortfall of data scientists, this is easier said than done. Part of this is because the demand far outweighs the supply, with Bloomberg estimating job postings for data scientists rose 75% from 2015 to 2018. Almost every organization shares this same urgent need.

    When faced with a skills gap in their teams and stiff competition in recruiting, how can organizations effectively build a data science team?

    One way is to start by looking at your data analysts. They are often swimming in disparate, federated data sources, operating on legacy databases, working with mixed data sources and using Excel to do things it was never intended to do. There is an enormous opportunity to empower data analysts to become data scientists.

    From Data Analyst to Data Scientist

    Companies looking to hire data scientists from within need to think about how to upskill, reskill or preskill their data analysts to perform the roles needed to implement AI and ML fully. I’m a believer that if you provide prescriptive and progressive curricula around the essential topics a budding data scientist needs, you can equip them with the skills and knowledge to make a difference and move them forward in the organization.

    However, it’s not enough to simply equip these employees with a curriculum; the program should have numerous twists and turns. Why? When thinking about how you can train the prototypical ML expert of the future, you must consider all the deep tech skills needed. You also want the business and social skills that are required to attain the expert moniker. A well-rounded ML architect will see the business imperatives, understand how to communicate with engineering and business, and have the tech skills to deploy ML models that benefit your business and customers.

    When organizations take roles commonly found in the workplace, such as data analysts, provide them with a sequenced path of instruction that moves them toward an aspirational role, they’ll be well-positioned to realize the full potential of AI and ML.

    — Mike Hendrickson

  • IBM Zones in on DevOps for AI

    IBM Zones in on DevOps for AI

    IBM this week revealed it has added a drift detection capability to the Watson OpenScale platform to govern artificial intelligence (AI) models that will become a foundational piece of IBM’s approach to defining best DevOps practices for building AI-infused applications.

    Rohan Vaidyanathan, program director for IBM Watson OpenScale, said one of the biggest AI issues organizations face today is determining when to update or replace an AI model. Announced at the IBM Data and AI Forum event, the drift detection software added to Watson OpenScale provides a continuous monitoring capability that detects how far an AI model has moved from its original parameters, Vaidyanathan said.

    Drift in AI models usually occurs over time, especially as use cases change in ways that are unexpected. Once that drift is detected, organizations eventually want to either retrain that AI model or replace it with a new one. The process challenge organizations face when building AI applications is first assembling the right data to train the model and then integrating that model within an application as part of a continuous integration pipeline.

    With each interaction with an AI model, the result returned to the application might be different as the machine learning algorithms embedded in the AI model continually learn about the process the application abstracts.

    As it turns out, Vaidyanathan said, only one in 20 of the AI models being built make it into production applications, largely because organizations have not bridged the divide between DevOps teams and data scientists effectively. The drift detection capability IBM has developed is a “missing link” that will enable those two teams to begin collaborating more closely, he said.

    IBM has already defined a set of best practices for building AI models as part of its Watson Anywhere initiative, which seeks to drive adoption of the various elements of the Watson platform on multiple clouds. Rather than forcing organizations to load data into an IBM Cloud to use Watson, IBM is working to bring the various engines and application programming interfaces (APIs) to wherever data resides.

    Naturally, IBM would prefer organizations employ the opinionated DevOps and DataOps processes it defines as best practices for building AI models using its tools. However, Vaidyanathan acknowledged there is likely to be plenty of instances in which organizations will prefer to define their own DevOps processes. The hope is those organizations will still access Watson services and metrics using the APIs that IBM has exposed.

    The challenge IBM is encountering in terms of driving AI adoption are manifold. In addition to trying to get data scientists to appreciate DevOps, IBM recognizes AI models are only as good as the data employed to train them. Unfortunately, many organizations have been lax when it comes to data management. As a result, many of them are now revisiting broken DataOps processes. IBM Watson OpenScale and IBM Cloud Paks for Data, a set of portable data virtualization and management tools deployed as microservices, are part of an effort to bring both explainability and order to the building of AI models.

    Obviously, it will take some time for organizations to extend best DevOps practices to incorporate AI models. But at this juncture, it’s more a matter of when rather than if that goal will be achieved.

    — Mike Vizard

  • What is so Special About AIOps for Mission Critical Workloads?

    What is so Special About AIOps for Mission Critical Workloads?

    As the wave of digitization sweeps more enterprises with each passing day, the threats posed to organizations, particularly IT organizations, have never been more complex and pressing. With cybercrime statistics, including those for more targeted crimes (such as phishing) pointing towards an alarming frequency of attacks, the stakes for securing enterprises have been higher.

    However, many IT organizations have been unable to smoothly transition into pushing the digitization of their organizations forward. This inability to support and drive digital transformation isn’t a result of a lack of effort on behalf of these organizations. Instead, it has everything to do with the unavailability of the proper management tools needed.

    Unfortunately, many IT organizations lack the tactics and tools needed to overlook the smooth shift to digitization on such a grand scale, which often leads to the degradation of critical business applications, along with IT slowdowns and outages. Luckily, however, artificial intelligence for IT operations (AIOps) arrives like a beacon of hope for revolutionizing IT application, along with increasing business agility.

    The Failure of Enterprise in Tackling the Burgeoning Complexity of IT Infrastructure

    A primary reason behind the ever-present issue of IT slowdowns in enterprises and organizations that is frequently overlooked is the fact that the customer-facing side of IT (applications) is growing increasingly complex with each passing day.

    As the increasing number of applications continue to occupy more and more space, the IT infrastructure grows into a vast and hybrid structure which often spans across multiple clouds on a grand scale.

    Although the vastness of the IT infrastructure is a byproduct of the digitization drive taking enterprises by storm, it is still worlds away from the security infrastructure of yesterday, which was much easier to manage and analyze.

    From the Internet of Things (IoT) connecting thousands of devices onto an organization’s network to complex issues such as bottlenecks, application slowdowns and outages, along with performance latency and the ever-present threat of phishing attacks via a free host website (000webhostapp, being the most commonly used host), organizations are just setting their systems up for failure at some time in the foreseeable future.

    Owing to the flourishing complexity of the IT infrastructure, an organization’s system fails to cope with new, dynamic workloads for any multitude of reasons. Usually, the system is utilizing extra resources or is under the influence of a recent addition to the system.

    In instances of outages or IT slowdowns, the pressure to fix the IT infrastructure falls on the IT team, who are tasked with the immense responsibility of analyzing and diagnosing the issues as they occur in real-time, along with coming up with a solution that satisfies customers and prioritizes the business aspect of an organization as well.

    Unfortunately, however, without access to the proper tools and techniques, the IT team can only conjure security patches that resolve the problem for the moment but fail to withhold security in the long run. Also, without the ability to effectively predict a pattern for the ever-present security threats, the IT team is operating on a whim rather than basing their solutions on the rationale.

    How Can AIOps Help the Situation?

    To leave the momentous challenge of perfecting an organization’s IT infrastructure solely on the IT team is not only outdated, but it is also impractical. Taking into consideration the complexity of the threats facing enterprises, a solution that arches above each aspect of cybersecurity in an organization should be implemented.

    Today, that solution arises in the shape of AIOps. The hybrid IT infrastructure has become too complex to be handled through traditional tools and tactics, which is why AI, along with machine learning and automation, has to step in to bridge the gap.

    Unlike other security strategies, AIOps integrates a wholly modern and unique approach, which allows for the AI to view systems in terms of business outcomes. Simply put, rather than analyzing and diagnosing identities, infrastructure and applications independently, the AIOps platform follows the path taken by the user into the complex IT infrastructure and back out of it.

    Ideally, AIOps platforms run automated investigations that derive the root cause of existing and recent issues on a regular basis. When it comes to managing a business-critical application, an AIOps platform can detect an unusual response within the system, whether it’s an anomaly in a workload profile or variant response to critical workload.

    Other benefits to incorporating AIOps as a security tool include tapping into the truly unique set of capabilities offered, including predictive capacity management and forecasting/predictive abilities. The predictive ability of AIOps allows for the AI to smartly notify IT teams about any attack or potential outage, or slow down, before they actually happen so they may act accordingly.

    By taking a different approach to the pressing issues faced by enterprises today, AIOps manages to do what several security strategies have failed to accomplish—provide organizations with an end-to-end, app-centric approach to security, which also takes into consideration the context at hand.

    Moreover, AIOps will push organizations to shift their focus from reactive problem solving and cause them to optimize their IT infrastructure on a more proactive basis. Fundamentally, AIOps provides IT teams with the necessary tools to diagnose and combat both internal and external threats to an organization’s security infrastructure.

    After the integration of AIOps within an organization’s infrastructure, enterprises can turn their attention into optimizing the balance of critical workloads, along with prioritizing business functions and improving applications, rather than worrying about security.

    What Hurdles Can Enterprises Expect with AIOps?

    Despite the countless benefits AIOps brings to the table, certain obstacles arise when it comes to the implementation of the security model into organizations.

    Perhaps the most apparent obstruction organizations have to deal with is the extensive amount of time investment AIOps requires. Like every other security tool centered around an AI or machine learning technology, time is absolutely pivotal for the learning to actually take place. A simple way to smoothen the transition, AIOps must be fed with data, statistics and analytics originating from multiple data centers.

    Another common issue that arises, mainly because of a lack of research, is when an AIOps platform fails to perform as per the specific requirements of an enterprise. Keeping this in mind, CIOs must voice their concerns about fine-tuning AIOps before committing to a vendor.

    AIOps abilities in performing an in-depth analysis into the IT and security infrastructure—within the context of the applications it performs—gives the unique chance to stay ahead of the threats they face.

    — Rebecca James

  • How AI and Microservices Create a Reliable Enterprise of the Future

    How AI and Microservices Create a Reliable Enterprise of the Future

    The Rise of Microservices

    Enterprise adoption of microservices is continuing to rise. Its popularity is so strong that 86% of development professionals internationally expect microservices to become the default application architecture within the next five years. Looking out even longer, nearly all professionals see microservices becoming their company’s default application architecture.

    At its core, a microservice approach centers on the idea of breaking up complex enterprise software into smaller, more manageable pieces, containing everything from the operating system to the platform to the framework to run time and dependencies, packaged as one unit of execution.

    By dividing an application into smaller stand-alone services, each performing a single function and communicating over the network to work together, microservices offer increased modularity and make applications easier to scale and faster to develop. In a world of ever-increasing apps, websites and digital experiences, there are immense benefits to deploying microservices.

    However, the use of microservices is no silver bullet and, for all their benefits, they have their own limitations and challenges. When it comes to deploying microservices, scale, complexity and constant change are the new realities. Microservices architecture introduces complexity with respect to network latency, network communication, load balancing, fault tolerance and message formats. With great flexibility also comes a higher risk of something slipping through the cracks. Managing many services requires understanding deep structures, dependency graphs and more.

    Process scalability is another challenge. An enterprise can run one cloud-based service with relative ease, but simultaneously running dozens of services across multiple clouds quickly becomes a complicated juggling act. In fact, with all the moving parts that come with microservices, it is becoming increasingly difficult to manage these processes manually.

    No wonder that, along with the rise of microservices, we have seen the emergence of auto-remediation/self-healing capabilities. To the untrained eye, the terms auto-remediation and self-healing would seem to be the answer, but don’t be fooled. Many of these features, although making the management of microservices easier, use static thresholds for data points such as waiting time, throughput, message queue size and more. When the thresholds are passed, the system will correct or self-heal as necessary. Although helpful, they’re not necessarily smart or intelligent—and that is where AI can help.

    Advanced Self-Healing and Self-Defense

    AI can help solve some of the hardest problems facing microservices today. Below are a few achievements we’re already seeing AI accomplish:

    • Load Prediction: Understanding the load on the system and acting on it before it happens will allow systems to provision more resources before they’re needed. Provision of resources is not just limited to the network and number of tasks running; AI can trigger customer support representatives to be on hand when—and only when—needed.
    • Decay Detection: Anomaly detection is built into a lot of monitoring services and is efficient at detecting spikes, loads and abnormal behaviors; however, detecting a service decay, even gradual or subtle, is difficult. AI can help systems recognize whether services are reacting differently over time and what the reasons are for this decay. It also has the potential to be more effective in identifying bugs based on studying clusters of microservices having continuous issues.
    • Resource Planning: As the scale and complexity of microservices grows, so does the use of the cloud. The ability to plan ahead using smart resource management could save significant costs while maintaining a stable and robust service.
    • Security: Security is already leveraging AI in many places by discovering attack patterns based on known behaviors. Leveraging these capabilities at the network and database level can improve security for actions done by an authorized person. If you can understand the usage patterns and act on them, you can define a more secure system for all users.

    Benefits aside, using AI is not without pitfalls. For example, despite all its potential, the so-called state-of-the-art AI models are still quite primitive when compared to what they have the potential to achieve. Another issue is how to manage the possibility of bias creeping into AI models to avoid unintended consequences. Technology professionals are already working on ways to solve these complications. However, it will take time to develop the right models, data sets and algorithms.

    The Future

    If the future of application architecture is truly to be dominated by the ever-increasing use of microservices, then those architectures will need to also be dominated by AI. The reality is microservices are simply becoming far too complex for humans to manage on their own effectively. Because of the dynamic nature of their structure, their complex dependencies and their sheer scale, machine learning must be applied to assist with monitoring and management tasks. AI is best suited to meet this challenge and tackle it head on.

    — Martin Henley

  • The Barriers to Robotic Process Automation, Part 2

    The Barriers to Robotic Process Automation, Part 2

    Enterprises are rushing to embrace robotic process automation (RPA) but, as we covered in The Barriers to Robotic Process Automation, Part 1, they’re also hitting considerable challenges along the way. While we cited prominent barriers to the effective implementation of RPA in part one, here, we will provide practical ways organizations can overcome these barriers.

    One of the biggest causes of RPA failure is the lack of executive-level sponsorship for such initiatives. When it comes to RPA success, getting management’s attention–and keeping it–is essential. One way to do this is to focus on RPA quick wins and efforts that have a measurable return on investment.

    Next, IT leaders need to understand how significant digital transformation is to AI technology.

    “An efficient digital transformation strategy will eliminate internal IT silos through integration and automation, leading to opportunities for AI projects,” said Marcel Shaw, AI expert and Federal Systems engineer at Ivanti. Shaw pointed to how organizations that interface with customers will see opportunities for chatbots that leverage natural language processing technology, thereby reducing demand on operators while enhancing the user experience.

    “IT leaders will be able to take advantage of machine learning technology that will support and enhance complex automated processes that will fulfill customer requests from beginning to end, without intervention from a human analyst,” said Shaw.

    Such benefits could make RPA an easy sale to executive leadership. Other benefits that can be used to sell RPA to leadership include cost savings, more efficient processes, improved process quality and free staff to focus on higher-value tasks.

    “The only thing holding AI back at this time is a lack of experience with the technology. AI will take many years to develop. Some estimates point to a 75-year maturity lifecycle for AI. AI technology is designed to learn. As it learns, it will correct mistakes and mature, much like a human has to learn,” said Shaw.

    Next up: The high level of investment needed for effective, automated and intelligent robotic automation.

    “There’s a great deal of catching-up to be done before most companies can even consider building out their own advanced developments with AI,” said Salvatore Stolfo founder and CTO at Allure Security. “It may be more cost-effective and faster to partner with a software vendor that has already put the time into dedicated research and development to build an AI-powered solution, and then customize that solution for your particular use case.”

    Simha Sadasiva, CEO at Ushur might agree. “Many intelligent automation solutions on the market today require a heavy IT investment. This can include hiring an outside third-party just to manage the platform they’ve invested in,” said Sadasiva.

    “Those attempting to manage the technology in-house are finding that it requires personnel with sophisticated developer-level knowledge, which is in short supply today. These issues are causing some companies who were initially interested in exploring intelligent automation solutions to abandon their projects altogether,” added Sadasiva.

    Sadasiva said one possible way to solve this is to evaluate potential zero code platforms. “These are SaaS-based intelligent automation solutions born in and live in the cloud,” said Sadasiva.

    Finally, and while perhaps self-evident is often overlooked: know what needs to be achieved with robotic process automation.

    “An organization needs to identify what the problem is that they are trying to solve with AI. This needs to be the most important pain point to them at that point in time. Inherently AI requires data to be able to train the machine to learn,” said Ben Mercer, co-founder at personalized web experience provider Personify XP.

    “The challenge for some organizations, when it comes to accessing data, is that valuable data is stored on legacy systems and difficult to extract. In cases like this, it may make more sense to train the AI on new data, than fighting with legacy systems that may delay or reduce the ROI of the deployment,” said Mercer.

    Effectively managing data, AI and the resulting automation requires staff with strong skills and experience in these areas.

    “Regarding skillset development, most of the skills needed can be learned, but you need to find different types of team members who have a great comprehension of data and database management and secondly a development background,” said Jesse Tutt, CEO at mattress company Gotta Sleep.

    Finally, chances are if an enterprise is planning to do something there’s already someone who has tried and has lessons to share. “Every automation project has likely been done by someone else already. Tips and tricks are freely available from others who have more experience so networking on Linkedin, meetups, and conferences are key,” adviced Tutt.

    — George V. Hulme

  • Fujitsu: State of Digital Transformation Is…Mixed

    Fujitsu: State of Digital Transformation Is…Mixed

    According to the Fujitsu Future Insights Global Digital Transformation Survey Report 2019, nearly 90% of companies in a recent survey are already deep into their digital transformation efforts. While the survey did find broad movement toward digital transformation, there existed wide differences in the progress of transformation across industries.

    If digital transformation progress were a horse race across industries, financial services and transportation would be running neck and neck. While, overall, companies reported 36% progress in their digital transformations, 47% of financial services firms and 45% of transformation companies said they have moved forward with digital transformations and have delivered positive outcomes. From there, the government reported 35%, manufacturing 32%, healthcare 31% and wholesale/retail 29%.

    Fujitsu’s findings are based on 900 respondents from nine countries. Interestingly, only 13% of those surveyed said their companies have yet to get started with their digital transformations.

    Not surprisingly, online businesses are moving along more quickly with their digital transformations than brick-and-mortar businesses. While online businesses comprised 33% of the survey respondents and brick-and-mortar 67%, online businesses have made more progress in digital transformation. Of the online companies, 91% are implementing a digital transformation, with more than half achieving positive outcomes.

    One of the more concerning findings from the Future Insights Global Digital Transformation Survey Report was the lack of trust toward online data. A considerable 70% of respondents found it difficult to judge if online information is trustworthy, and 59% were concerned that the data they use may have been falsified.

    “Concern about personal data is also high, with 72% of respondents saying that they are worried organizations exploit personal data without their permission,” the report said. “However, more than half of respondents are content to provide personal data to an organization if they can trust the organization and in cases where personal data is used to enhance products and services.”

    In addition to a lack of trust toward data, respondents are also unsure about AI-made decisions. “Sixty-three percent of respondents think that decisions made by AI are fair because AI is not subject to bias. However, 52% of respondents claimed they couldn’t trust AI because data can be incorrect or biased.”

    However, 60% of respondents said they would trust decisions made by AI if the AI shows substantial reasons for reaching the decisions, and 66% indicated they would trust a company that published a code of ethics governing the use of AI.

    “Companies in which management places an emphasis on long-term perspectives, practices empathic leadership by sharing their messages and passion with employees, and empowers their staff tend to achieve greater success in their journeys toward digital transformation,” the survey found.

    While respondents to this survey indicate moving forward toward their digital transformation objectives, as we recently covered in Digital Transformations Fall Short, Survey Claims, the Kony Digital Experience 2019 Index Survey concluded that businesses are “still missing the mark” with consumers despite nearly $5 trillion in digital investment. The report didn’t distinguish how the $5 trillion was being spent and in what areas of the business the investments were made.

    That report found that said organizations in banking (54%), retail (59%) and utilities (51%) are keeping pace with their expectations but are not delivering differentiated digital experiences. The big laggard was health care, with only 48% keeping pace and 20% being behind respondents’ expectations.

    — George V. Hulme