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Tag: machine learning

  • Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    The relationship between computers and humans has undergone a significant paradigm shift over several decades. From the emergence of the web in 1995 to applications or apps in 2005, technology has grown by leaps and bounds as the years progressed. This growth has brought technology to the modern era, where artificial intelligence (AI) has ultimately begun to come of age.

    Engineers and scientists have invested a great deal of effort, in terms of both energy and time, over the past few decades, which has allowed AI to be adapted into nearly every technological innovation in recent years. One such innovation is the intelligent virtual assistant (IVA).

    An IVA is a conversational AI system that has the capability to emulate human interaction in order to carry out and optimize tasks—customer service, for example. Digital assistants are gaining widespread attention across the industrial landscape, owing to a plethora of technological advancements, including machine learning and deep neural networks, among others. What makes these virtual assistants intelligent is their ability to emulate three key elements that enable customer service agents to solve problems—learning, reasoning and understanding.

    How did these conversational AI technologies achieve these capabilities? To answer this question, it is essential to delve into the evolution of the IVA market.

    Evolution of IVAs

    Although the terms “chatbots” and “intelligent virtual assistant” are often used interchangeably, and despite the fact that these technologies share a core commonality—i.e., their ability to facilitate consumer-enterprise interaction—these conversational AI technologies have some distinct differences. These differences lie mainly in complexity, scope and abilities, which IVAs’ possess more of than chatbots.

    In simple words, IVAs are the result of a decade-long chatbot evolution.

    Chatbots were first introduced in the late 60s and early 70s. These technologies were programmed with rudimentary capabilities, including scripted responses based on user inputs in the form of specific phrases and keywords.

    However, with conversational flows becoming increasingly more complex over time, these chatbots began facing limitations in their scope, mainly owing to their lack of learning process and pre-programmed responses. With no memory or context retention capabilities, the bots were unable to comprehend relevant user information between sessions or even during the conversation.

    However, conversational AI evolution reached a turning point with the emergence of two novel technologies, machine learning (ML) and natural language understanding (NLU).

    NLU gives the digital assistant the ability to comprehend and interpret inputs from users in natural language, which can enable the bot to converse with a customer naturally, as a customer service agent would.

    Meanwhile, ML allows the bots to self-train based on data such as previous customer service interaction transcripts or by observing live customer-agent interactions. This helps the IVAs to cater to customer needs in a more efficient and seamless manner.

    The technological advantages provided by these technologies, therefore, facilitated the gradual evolution of simple chatbots into IVAs, which possess in-depth domain knowledge that they can leverage to understand customer preferences and intents, fulfill their needs and enhance performance over time by gaining new insights from each interaction.

    As a result of these advanced capabilities, automation and customer satisfaction are assured outcomes of IVA adoption. Thus, brands that make use of this technology can make sure that they refocus on their primary objective: ROI.

    The Gradual Shift of IVA Technology from a Personal to a Commercial Landscape

    Starting out as an ideal solution for virtual personal assistants, such as Apple’s Siri which allows users to use voice-technology to perform myriad tasks like texting, scheduling meetings or making phone calls, IVAs are now gaining prominence as crucial tools for brands to implement effective customer engagement.

    AI assistants are rapidly making their way out of personal applications and entering a more business-oriented landscape, for instance, Oracle’s Digital Assistant which is designed to be an ideal platform for sales, marketing and other industry verticals by interfacing with a vast software app portfolio.

    IVA technology is used across a range of industrial applications, for example, taking up the role of sales agents or customer service executives, which makes it a scalable and appealing option for enterprises. Businesses predominantly adopt digital assistants to drive efficiency, enhance ROI, optimize processes and help reduce workloads.

    As the industrial space grows increasingly more competitive, customer engagement has emerged as one of the most significant assets for a successful brand. In fact, studies have shown that almost 96% of customers across the globe believe that customer service is a significant factor contributing to their loyalty to a brand.

    IVAs help brands deliver positive customer experiences by covering a number of different aspects. For instance, AI assistants can quickly solve customer problems through customers’ preferred channels, ensuring speed and convenience. They can also integrate with various systems and databases of brands, thus offering a robust knowledge base to provide tailored solutions to customers. Additionally, they are able to adapt their tone and language to context, which helps them emulate human conversations. All these factors help digital assistants play the role of an efficient and helpful customer service representative.

    How Brands Are Leveraging Conversational AI Assistants to Drive Customer Engagement

    Given the strong capacity of conversational AI technology to engage actively with customers and optimize customer service, more and more companies are adopting the technology as an integral part of their customer experience activities.

    To illustrate, Volvo has plans to develop conversational assistance to guide drivers in the use of various in-car apps to deliver a more effortless connected car experience.

    Likewise, Spotify has also earned positive reviews for its AI-based technology to track user patterns and make accurate recommendations and predictions based on preferences of individual customers, in turn elevating the overall user experience.

    The IVA industry has witnessed several prolific advancements over the years. The future outlook for the technology seems positive, with even more exciting avenues as the years progress, and AI researchers investigating the prospect of enhancing the emotional intelligence of assistants. The ability to comprehend not just the user’s preferences but also their emotions would revolutionize the way customer service is delivered, thus positioning AI assistants as a pivotal component of the future customer experience landscape.

    — Saloni Walimbe

  • AIoT and Intelligence on the Edge

    AIoT and Intelligence on the Edge

    Recently, one of the biggest convergences is between the fields of AI/ML and IoT, which enables the rise of Artificial Intelligence of Things (AIoT). AIoT combines data collected by sensors and other devices with the intelligence offered by AI/ML to enable real-time, intelligence on the edge. By converging AI/ML and IoT capabilities, AIoT solves two problems by merging them together into one solution. The goal of AIoT is to enable more efficient IoT operations, improve human-machine interactions and enhance how data is managed and analyzed. 

    Edge intelligence allows a high level of data to be processed and analyzed, and for decisions to be made locally, without being sent to the cloud. Take for example a self-navigating drone, instead of relying on a service hosted on the cloud to tell the drone where to go next, the drone itself is now able to decide its own path in the field, even when connections to cloud hosted services are not reliable.

    The evolution of IoT into AIoT progressed in three distinct stages: 

    1. Enabling core capabilities on the edge, including basic sensor development, integration with available devices, etc.
    2. Collecting the data generated from these sensors and storing them in a structured form on a central data store, typically on the cloud.
    3. Realizing the synergy between AI/ML and IoT and combining them together into AIoT.

    In other words, while IoT provided access to a large base of information, AI/ML has brought in intelligence and decision making.

    While great advancements toward AIoT have occurred, there are three main challenges in enabling intelligence on the edge to consider:

    1. Latency: When decision making occurred on the cloud, the only available option for an IoT device was to send the data (say, a captured image from a camera) to a service running on the cloud and get a selected decision from the service. This meant large volumes of data were transferred over slow or unreliable networks, resulting in delays–in many scenarios these delays could be extremely critical.
    2. Cost of computing and data transfer: Apart from the delay itself, the sheer volume of requests would greatly increase the cost of both transferring data back and forth and the cost of computing on the cloud–decreasing the feasibility of these implementations.
    3. Design divergence: While initial AI/ML solutions leveraged the cloud for vast amounts of computing power for training models, advances in the ability to move around or run a trained model (to calculate a decision) caused a divergence between the design of the solution and the hardware needed for the training and deciding components.

    In parallel, AI/ML learning methods also evolved–these became a lot more efficient with regards to computing power usage, and the type of hardware needed for a machine to learn or to decide. Combining AI and IoT was the next logical stage in this evolution.

    Design of an AIoT Solution

    The physical components of an AIoT solution are not that different from a standard IoT implementation; the design of the AI/ML backend also does not change drastically. The changes are to a few specific parts of the core devices on the edge–that are now more powerful–and enable a huge range of flexibility with the scenarios that can be handled, and how solutions are designed using these devices. 

    Impact on Enterprises

    Enterprises looking to benefit from AIoT may need to re-align roadmaps or make a few incremental changes based on their current state of adoption and based on the nature of their solutions.

    For CXOs

    There are four suggested, broad strategies for CXOs based on their level of investment today in the areas of IoT and AI/ML that are depicted in the following graphic.

    Strategies to follow for AIoT adoption depends on current levels of investment.
    • Group 1: Current level of investment in AI/ML and IoT are both high. The best option for these enterprises would be to evaluate the current roadmaps to identify incremental additions/upgrades to edge devices. This would leverage the existing investment in AI/ML and at the same time start demonstrating benefits early without increasing risk.
    • Group 2: Level of investment is higher in IoT compared to AI/ML. These enterprises stand to gain the most by shifting the ratio of investment toward AI/ML while making small changes to the device sets on the edge. They may also need to re-evaluate their current process to consider moving the decision part of the step to the edge.
    • Group 3: Level of investment in AI/ML is higher than investments in IoT. These enterprises would probably need minimal re-alignment with their roadmaps–a quick look at the implementations of the steps that train the ML models would provide the list of actions to take–epics/stories that needed intelligence on the edge can move higher up in the product backlog.
    • Group 4: These enterprises have the maximum freedom with the roadmap their implementations can take. Any product or platform roadmap planning can easily include AIoT epics and stories, with minimal impact to tasks already underway.

    For Architects/Program Managers

    For architects and program leads working on such initiatives within the company, it’s mainly a mindset change in regards to how the solution is designed, including capabilities of the devices on the edge and where the decision-making step in a process happens. Feasibility for scenarios such as the drone automatically calculating its own path instead of relying on a cloud-hosted service are now better than before, and a few demos or proof-of -concept attempts could now move many of these stories from the backlog and bring implementation dates forward.

    Realistic Predictions for AIoT

    While AIoT in its re-imagined, converged form may be new, the two original fields (AI and IoT) that merged to create AIoT are both mature and well into mainstream adoption. The benefits offered by AIoT for enterprises today, combined with the low risk involved with the small incremental changes outlined above and the cost savings that result in moving the decision making to the edge, create a lethal combination that positions AIoT to revolutionize business processes.

    IDC has predicted that the global spend on IoT will reach $1.1 trillion in 2023, up from $726 billion in 2019. Moreover, IDC predicts there will be around 40 billion IoT devices by 2025, resulting in a significant influx of data that will make it increasingly challenging for enterprises to drive operational insights and optimize their analysis of the large sums of data available. AIoT can help solve this problem by bringing increased intelligence to data management and thorough analysis. 

    If it isn’t already, now is an ideal time for AIoT to be a top priority for C-suite IT leaders.

    — Rajamani Saravanan

  • DataOps and Beyond: How DevOps Methodology Transformed Our Approach to Data Science

    DataOps and Beyond: How DevOps Methodology Transformed Our Approach to Data Science

    The DevOps methodology has become synonymous with forward technical thinking–a workplace culture that reinforces best cultural practice and promotes more, better quality output by synchronizing the functions of development and operations teams. As a testament to its popularity, the term has expanded in reach to refer to a broader set of transformational software-related practices. The list continues to grow: DevTestOps, DevSecOps, GitOps, DataOps. 

    In 2020, organizations are drawing upon thousands—if not tens of thousands—of data sources, which exist in hundreds of different formats and fall under unique sets of restrictions and compliance regulations. Figuring out where all this data comes from, what it means and how to ensure that the right teams can access it when they need to, are common data management challenges. To address these challenges, some solutions were borrowed from effective practices in software development; automating analytics for improved quality, and allowing data scientists to shift away from time-consuming tasks to focus on value-adding projects instead. 

    What happens when software development and deployment methods are applied to processing and integrating data? Does that mean that DataOps is exactly like DevOps, but for data? And what benefits do organizations expect from adopting this approach to data management?

    The xOps Mindset

    At their core, the ops practices are linked by fundamental characteristics that shape their goals and practices. Let’s explore some of these commonalities:

    • xOps ties together workflows from create to run: In an ideal environment, production and distribution don’t function as separate entities, but are synchronized to meet target metrics that can include hundreds, or thousands, of daily deployments. Culture changes have rewritten rules around internal communication and feedback loops in a development environment where the conditions or requirements for success are constantly changing.
    • xOps automates the mundane (non-creative) tasks: Automation not only increases an organization’s agility, but when executed correctly can eliminate communication gaps between teams—for instance, automating traceability and issue tracking within code changes. Within larger, more complex projects, automation can save on time and cost by executing and managing CI/CD—ensuring that software builds and changes reach their designated target environment with little to no human involvement.
    • xOps builds in best practice: Equally as important to the functions they perform, xOps tooling plays a key role in building in best practice from the top down and bottom up. Best practice might not look identical between organizations, but the common goal is delivering value for a range of stakeholders through continuous production, in an environment where it’s encouraged to learn through experimentation and to respond to problems as they occur.

    In short, DataOps loosely applies the DevOps process to data pipelines—using automation and Agile methodology to reduce the amount of time spent fixing issues in pipelines, and to get data science models into production quicker. There are also some obvious differences between DataOps and DevOps. While DevOps refers to the collaborative process between two technical teams, DataOps facilitates cooperation between data analysts, engineers, scientists and any members of an organization who use data. This makes DataOps a much more multifaceted process than its DevOps cousin. 

    xOps for the Big Data Age: Benefits of Running DataOps 

    We know that for many organizations, converting raw data into usable, accessible and relevant (that is, profitable) business intelligence has given rise to a plethora of data management issues. The sheer volume of most enterprise level datasets has simply outgrown many of the inadequate older systems and processes built to manage it, and we continue to produce new data at stunning rates. There is a general consensus among data scientists today that the amount of data in the world doubles every 12 months.  

    • Automation and Continuity: Within an organization, business data moves through a set process—akin to the software lifecycle for the DevOps analogy—where data is ingested in one form and exits in another. Engineers must build data pipelines, test them and change them before they are deployed. By adopting the standards for automation and best practices outlined above, you’ll ideally have a constant stream of data flowing through a pipeline. This unlocks the potential to obtain real-time insights from data, shortening the length of time it takes to turn raw data into valuable business information.
    • Cultural Shift: When completed correctly, DataOps changes who within an organization becomes a data user. A higher level of transparency and an assurance of quality means that data is more openly available to decision makers within non-technical teams. Consider the business value-add for a sales team who wants to expand into new markets and are able to access a wealth of customer data.
    • Machine Learning: We talked about the role of feedback loops in the xOps mindset. When machine learning modelling meets this mindset, you improve the quality of the model being produced because you’re unlocking a higher quality of data through version control, continuous integration and continuous deployment. Improved insights in machine learning offer nearly unlimited potential for extracting value from DataOps.

    — Hannah Leslie

  • Enterprise AI Books to Read This Spring

    Enterprise AI Books to Read This Spring

    If you are anything like me, and curious about how new enterprise tools will transform our relationship with work and business-technology, then you are probably reading a lot about artificial intelligence. There’s likely no other topic that’s been written about in more depth in recent years, yet much of it is unrealistic or poorly reasoned hype.

    This is why I’ve been reading more books from trusted authors on the subject. In previous years, I’ve read books such as Erik Brynjolfsson’s and Andrew McAfee’s Race Against the Machine, and Martin Ford’s book Rise of the Robots: Technology and the Threat of a Jobless Future. Both were great books that brilliantly framed AI. Last year, I wasn’t as fortunate.

    Last year, my reading included The Master Algorithm by Pedro Domingos. It was an interesting look at machine learning algorithms, and specifically the quest for the algorithm—the master algorithm—that would create all future algorithms without the need for humans. This book proved a disappointment because the author seemed as convinced, almost dogmatically so, that general AI success was as close around the next corner as the AI pioneers of the 70s and 80s believed success for them was around the next corner.

    The next book up for me was Life 3.0 by Max Tegmark. While this was a good read, it proved to be too much of an analysis of what has been already written than a book that significantly pushed the subject of AI forward in a new or unique way.

    Hopefully, my next batch of AI reads will prove better. I realize I’m playing catch-up here, as most of these aren’t new books. But they do look to be important books on the subject.

    Here’s what I currently have lined up.

    AI Super Powers

    Author: Kai-fu Lee.

    Following the defeat of the world’s top player of the game Go, to Google’s AlphaGo AI, the government of China set ambitious plans to become the global AI hub by 2030. In his book, Kai-fu Lee contends that China has quickly caught up to the U.S. and that dramatic changes from AI developments are happening much more quickly than many expect.

    In his book, Lee examines universal basic income, and examines what jobs may be enhanced with AI the possible solutions to the biggest changes AI promises to bring to us all.

    Artificial Intelligence: A Modern Approach 4th Edition

    Authors: Peter Norvig and Stuart Russell.

    At 1152 pages, Artificial Intelligence: A Modern Approach (due out this April) isn’t a light read. The latest edition of this book looks across the entire field of AI, and a deep dive into machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming and more.

    I have high hopes for this book, as it’s treatment appears to be exactly what I’m looking forward to reading: an overview of the state of AI without going too light on the treatment of each topic.

    Applied Artificial Intelligence: A Handbook For Business Leaders

    Authors: Mariya Yao, Adelyn Zhou and Marlene Jia.

    This book is made the list because it’s AI as it can be applied to business-technology. It promises to be a roadmap on how to use  data, technology, design and staff to solve enterprise business problems.

    “We teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments and consciously designing your solutions to benefit both your organization and society as a whole.”

    That’s exactly what enterprises need to do to succeed at integrating AI into their companies in order to get value from this growing enterprise technology.

    Machine Platform Crowd

    Authors: Andrew McAfee and Erik Brynjolfsson.

    I learned and enjoyed both Race Against the Machine and The Second Machine Age. The latest book from MIT Principal Research Scientist Andrew McAfee and Director of the MIT Center for Digital Business Eric Brynjolfsson, looked at the impact of machine intelligence and big data. In this book, the authors look at another form of augmented intelligence: our collective intelligence, and what it means for transportation, medical research, financial services and more.

    That’s it for the reading list for now. I’d appreciate hearing what AI-related business books you are reading this spring and summer.

    — George V. Hulme

  • Why Enterprise Survival Depends on a Context Driven Approach to Data

    Why Enterprise Survival Depends on a Context Driven Approach to Data

    Enterprises are sitting on an enormous amount of data that could be fueling their automation journey. Much of the information needed to run a business is existing data. However, much of an organization’s data is inaccessible and unsearchable. This so-called “dark data” is hidden away in paper forms, spreadsheets, emails and literally billions of PDF documents. This dark data is locked away and mostly unusable by the systems and AI required to power the autonomous business of the future. Enterprises are trying to achieve 21st-century outcomes while being shackled to 20th-century data practices. 

    I recently read a report from NewVantage Partners on a survey they did concerning how big data and AI were driving digital transformation. While 92% of the survey respondents said they were increasing investment in big data and AI, there were some interesting findings that suggest organizations are not realizing the ROI they had hoped. For instance:

    • Only 31% have a data-driven organization.
    • Only 28% stated they have a data culture.
    • 53% are not treating data as a business asset.
    • 38% have not realized any measurable outcome from their investment.

    Now, there’s a whole list of reasons and hurdles that are preventing organizations from realizing better value and outcomes from their efforts to become data-driven. But, could the underlying approach to becoming data-driven be flawed? Could organizations be struggling because they continue to apply a legacy mindset and approach to data acquisition and storage? Could these challenges be a contributing factor in 70% of digital transformation projects failing?

    Just take a look at the growing number of stories, market reports and surveys on digital transformation and you’ll see a graveyard of failed projects, lack of success and high turnover rates. The current approach to digital transformation is not working. 

    In our push to become data-driven, we capture data and attempt to transform it into computable bits and then stuff the data into rigid structures. This legacy approach to capturing and representing information outside the human brain results in a proliferation of single-use data that doesn’t scale with the business. We always lose some of the meaning, relationships and context. Despite all the advancements in technology, we’re still trying to model the information based on how we’ve historically stored data. We’re still producing flat data.  

    In the autonomous enterprise of the future, and the digital workers that will run them will need rich, contextual data. The true power and value of digital transformation will only be unlocked by the adoption of a semantic data-driven approach. The future of work will be built on digital workers powered by machine learning, artificial intelligence and knowledge graphs. It’s not enough for an organization to be data-driven anymore–they need to become contextually-driven. We need to start capturing the meaning to achieve a better understanding of the data.

    “I liked the idea that a piece of information is really defined only by what it’s related to, and how it’s related. There really is little else to meaning,” said Sir Tim Berners-Lee, the inventor of the World Wide Web.

    Context is all about the meaning. Context is more than just a piece of data. Context is the background in which an event, task or process takes place. It is the deep, multi-dimensional understanding of data and the relationships between the entities. Understanding the context can lead to accelerated insight through AI and machine learning. To be competitive, enterprises need more than data; they need context to enhance productivity and efficiency. But our current approaches to capturing and storing data have historically failed to capture this valuable context. As the amount of data grows exponentially, organizations will realize semantic data is key to survival.

    — Kevin Harbauer

  • Three Keys to Successful AI Adoption

    Three Keys to Successful AI Adoption

    Over the past several years, we have begun to see the emergence of artificial intelligence (AI) in businesses. According to a study for the AI Index 2019 Annual Report, more than half of respondents report their companies are using AI in at least one function or business unit. Thirty percent report they have AI embedded across multiple areas of their business. As businesses continue to develop their understanding of what is possible with AI, we can expect to see a continued increase in AI adoption.

    People used to say “software is eating the world.” Moving forward, people will be saying “AI is eating the software world.” You can expect to see standard enterprise and cloud software programs become indistinguishable from AI. Currently, the success rates of AI projects fall well short of what we see in enterprise software success rates. A 2019 study by Pactera found that 85% of AI projects fail to deliver on their intended promises. As software and AI bind together, expect to see a dramatic increase in the success rate of these AI projects. AI adoption will begin to align more with traditional success rates associated with enterprise software as a category.

    As software and AI begin to merge together and enterprises look to invest in AI, there are a few keys to success. 

    Increase Your Organizational IQ with AI 

    Human factors are public enemy number one as it relates to AI adoption. For companies wanting to invest in AI, it starts with education. Companies need to become better educated on the options available to them through AI. We need to move away from viewing AI as a black box where magic happens. Through better education, the average business consumer will be able to understand the various disciplines of AI, such as natural language processing, vision, unsupervised learning and machine learning. As companies increase their IQ relative to AI, businesses will begin to understand the difficulty of AI projects. While this will create short term challenges, it ultimately will help the success of AI adoption in the future. 

    Commit to a Journey with Frequent Recalibration

    Enterprises must move beyond merely being educated about AI in order to remain competitive. Business leaders will have to step past the exploratory and proof of concept phases and begin delivering real value to the business. This will mean the continuous delivery of intelligent solutions across several areas of the business. AI is not like traditional software projects where you measure twice and cut once, deploy and then observe for several months. It is a continuous journey of discovery that will require consistent adjustments. AI is different and requires you to make changes to existing predictive models based on your real-time findings. 

    If the Insight Is Not Actionable, It Doesn’t Matter 

    We have already seen AI become more and more embedded into analytics solutions and this is only going to continue. The convergence of AI and analytics is crucial to the success of AI projects. Over the past 20 years, analytical reporting has been provided to all users. With the addition of AI, machine learning models become analysis engines providing prescriptive actions to business users rather than the business uses having to interpret the analytics themselves. Again, this is a huge component in the success of AI adoption. It will not be enough for businesses to gain insights from their AI investment, success will be measured on what they do with those insights. Leveraging the insights from AI starts by providing actions directly to the users that can act on them within their existing workflow.

    The power of AI is still largely untapped. It is already creating a massive shift in how we work and that is only the beginning. For those who have worked in IT and software, you need to adopt a whole new approach to AI projects. In the AI world, data is the driving force rather than a necessary evil. The emerging technology will require professionals to expand their skill sets beyond the traditional technical skills and into the disciplines of math and statistics. By doing so, we can expect to see a massive increase in successful AI adoption.

    — Chris Heineken

  • LogicMonitor Acquires Unomaly to Advance AIOps

    LogicMonitor Acquires Unomaly to Advance AIOps

    LogicMonitor this week revealed it has acquired Unomaly, a provider of tools infused with machine learning algorithms that make it easier to analyze large volumes of log data.

    Tej Redkar, chief product officer for LogicMonitor, said the acquisition of Unomaly will advance a previously announced AIOps initiative through which LogicMonitor will embed advanced analytics and automation capabilities into its core IT monitoring platform. Rather than requiring DevOps teams to acquire a separate platform to take advantage of AIOps, LogicMonitor contends AIOps is simply an element of the next logical iteration of an IT monitoring platform.

    Unomaly will help LogicMonitor achieve that goal by identifying patterns in streaming log data that are indicative of anomalies that warrant further investigation, said Redkar. Unomaly works by first establishing a baseline for the IT environment. It then records a profile for each log event that includes the event’s structure, sources, frequency patterns and intervals. Similar profiles are then merged into one aggregated profile, with dynamic parameters to reduce the amount of data that needs to be analyzed by several orders of magnitude. If an anomalous event occurs within a rolling 60-second window on a single log source, Unomaly will bring those events together to identify and score a situation based on the type and number of anomalies.
    Redkar noted this approach not only provides more context about the events but it also greatly reduces the amount of log data that needs to be stored. Unomaly is also designed to accept data in any format.

    As IT becomes increasingly more complex, organizations of all sizes need more observability. However, because all the dependencies within the IT environment are becoming too complex to track manually, IT organizations will need to rely more on machine and deep learning algorithms to observe the IT environment.

    Naturally, that increased reliance on algorithms will alter the role of IT operations teams. Most IT operations teams will find themselves supervising algorithms that are surfacing issues and making recommendations, said Redkar. Rather than eliminating the need for IT operations expertise, AIOps will be employed mainly to manage IT environments operating at a significantly higher scale, he noted.

    It will take some time for this transition to play out as issues such as the explainability of AI models are worked out. Some of the lower-level functions within an IT operations environment undoubtedly will be eliminated, so job roles will change. However, many of those functions will be rote tasks that many IT professionals don’t especially like to perform.

    IT vendors are, of course, involved in something akin to an algorithms arms race to infuse AI into their management platforms. In some cases, they will develop these capabilities themselves. In other cases, they will acquire startups to gain access to AIOps platforms. Most IT vendors will be mixing and matching a variety of AI technologies they developed themselves and acquired. Regardless of the path pursued, it’s no longer a question of whether AIOps will be applied to managing IT, but rather when.

    — Mike Vizard

  • Predictions 2020: Five Real-Time Data Predictions

    Predictions 2020: Five Real-Time Data Predictions

    According to Gartner, 85% of big data initiatives end in failure. In 2020, organizations are out of budget and operational runway, and need to start executing and getting the big data recipe right.

    It is not just about big data; it is about using data differently. Rather than rearward-facing business intelligence, leading companies are driving differentiation and profit by using data to make fact-based decisions in real time. This requires the broader application of real-time information to make a difference in user experience, in prioritization of production and manufacturing schedules, and in spotting problems before they happen.

    Rather than reports for a few people, the new data-driven business processes are engaged continuously, and across all customer engagements, transactions and decision points within business processes. Scale means throughput across millions of transactions and with potentially millions of customers. Database systems have to be engineered differently, with different technology built to deal with real-time demands and high throughput across huge numbers of endpoints.

    As we enter the next decade, here are five predictions, centered on real-time data, that we expect to emerge.

    Machine Learning and AI Models Finally Get the Data They Deserve

    Most AI and machine learning initiatives are niche, nascent and not operating anywhere near scale. According to industry research (O’Reilly), hardware and data are the primary bottlenecks in getting enough data processed fast enough to harness the full promise of AI. That’s changing, with the arrival of new wire-speed hardware (from companies such as Intel) and clever data platforms designed to leverage hardware innovations for real-time efforts on a massive scale. It’s all about the amount of data you can process in real time to make AI and machine learning actually work. This is what is needed, and it’s becoming practical.

    Privacy Prompts a New Era of Data Innovation

    Businesses hoard data. And often, so much of it isn’t used due to infrastructure costs and technology bottlenecks. Now, as new privacy laws (GDPR, CCPA, etc.) show their teeth, companies really need to make all that data worth the risk. It will drive new real-time business processes that are not only safer but squeeze value out of data in new and exciting ways.

    Companies face a delicate balancing act. They must comply with the realities of new regulations, while also being able to use personal data in real time to serve customers what they want and expect. Companies that can satisfy the regulators and appeal to customers will win.

    If an Economic Downturn Occurs, Data Will Drive the Next Round of Economic Gains 

    An economic downturn is inevitable. When it happens, companies will be asked to do more with less. As a result, they will need to completely rethink their solution and delivery strategies.

    In that environment, with fewer markets opening or providing a sufficient return on investment, the next wave of gains is to optimize your existing segments and operations to the fullest extent. These data-driven initiatives require that companies understand customers better, and be able to instantly target them in critical moments—harnessing massive amounts of data to suggest and expand purchases. 

    5G Will Accelerate Customer Expectations

    As bandwidth increases and high bandwidth communication becomes available across devices (on cars, planes, homes, ships, buildings, etc.) and in more situations, we will see data being used differently. What may have been processed intermittently in batches with high degrees of latency will now be processed in real time, driving higher efficiency and removing risk from supply chains, customer interactions and equipment maintenance and performance. It will no longer be OK to know how you’ve done and correct it next quarter; rather, things must be corrected in near real time. New standards will be set by the highest-performing companies, and entirely new businesses will emerge that are leveraging a new level of availability to real-time data.

    Decline of Globalization Will Force Companies to Know Their Customers Better

    As global markets shrink, it becomes imperative for companies to up their game and drive for more market share within the available market. Companies that have been treading water and growing at the rate of the global market expansion are going to start to shrink. Differentiation through better customer experience and better management of supply chains will make the difference between continuing to grow versus stagnation or shrinkage.

    Companies that have invested in instrumenting their supply chains, who have a deep understanding of what comes from where and what the alternatives are, have already begun to optimize around the new web of tariffs. Those that are less agile are seeing reduced profits or reduced sales based upon the elasticity of their pricing options.

    The more real-time data one has, the clearer the picture of what options are available as the global situation changes. The faster a company can process that data means either missing financial targets or outperforming peers.

    Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit  featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

    — Lenley Hensarling

  • Cloudera Calls for MLOps Standards Initiative

    Cloudera Calls for MLOps Standards Initiative

    Cloudera put out a call this week for the IT industry to define a set of open standards for machine learning operations (MLOps) and machine learning model governance that could be universally applied.

    Santiago Giraldo, senior product marketing manager for data engineering for Cloudera, said the idea is to create the equivalent of a set of best DevOps practices to create the equivalent of a set of best DevOps practices for employing machine learning models employed by artificial intelligence (AI) applications that will serve to foster portability, interoperability and explainability.

    In the absence of such standards, most AI models are essentially black boxes optimized for specific platforms that many organizations are reluctant to adopt because no one is quite sure how they operate. Furthermore, it’s not all that feasible right now to integrate multiple AI models built using different classes of tools.

    At the same time, Giraldo said the further down the AI path organizations go, the more they realize the AI models they are building need to be either updated frequently or outright replaced as new data sources become available. Most AI models are trained to respond to specific events based on access to a finite set of data. As new data sources are introduced, organizations are discovering the accuracy of recommendations being made by an AI model start to degrade. In fact, only a limited number of AI models are making it into production environments today largely because they didn’t scale as expected.

    Cloudera is already moving down the MLOps path with Apache Atlas, an open source framework designed to integrate data management across explainable, interoperable and reproducible MLOps workflows. Now Cloudera is inviting the industry, including competitors, to collaborate on a broader set of metadata specifications that Anduago said is more a call to action than a specific proposal.

    It’s unclear precisely how such an initiative might be managed. There is no widely recognized consortium initiative focused specifically on MLOps. Thus far, the closest thing might be The Deep Learning Foundation, an arm of The Linux Foundation that connects organizations working on innovative technical projects focused on AI and machine learning.

    Despite all the hype, it’s apparent that AI is still in its infancy in terms of practical use cases. Most organizations can’t afford to hire the data scientists required to build AI models, let alone find them. Even when they do, the competition for AI talent is so fierce that organizations are also finding it difficult to retain that talent. Giraldo said the goal should be to make it easier not only to build AI models but also slipstream them into both new and existing applications.

    Obviously, many of the best DevOps practices that have already been defined can be applied to AI models. The next big challenge might very well be educating data scientists on how to work more closely with developers to build, test and deploy AI models with applications at scale. In the meantime, the existing DevOps community might want to do as much as possible to help prevent a nascent MLOps community from reinventing some wheels that might already exist.

    — Mike Vizard

  • Predictions 2020: Ushering in 2020 Data Predictions

    Predictions 2020: Ushering in 2020 Data Predictions

    2019 brought us more data organizations running more advanced analytics, AI and ML workloads than ever before. 2020 is the year where we’ll see a spike in both the number of technologies and data teams that support these types of workloads internally. We’ll see AI and analytics teams merge into one as the new foundation of the data organization, focused on areas such as moving to the cloud while maintaining on-prem Hadoop, “Kubernetifying” the analytics stack and Hadoop compute. These are the trends I believe we’ll see come about in 2020.

    Rise of the Hybrid Cloud

    We’ve been hearing people talk about the hybrid cloud for the past three years now. For the most part, that’s all it’s been talk—but in 2020 it gets real. We are seeing large enterprises refusing to add capacity on-prem to their Hadoop deployments and instead invest in the public cloud. But they are still not willing to move their core enterprise data to the cloud. Data will stay on-prem and compute will be burst to the cloud, particularly for peak demands and unpredictable workloads. Technologies that provide optimal approaches to achieve this will drive the rise of the hybrid cloud. 

    One Machine Learning Framework to Rule Them All

    Machine learning with models has reached a turning point, with companies of all sizes and at all stages moving towards operationalizing their model training efforts. While there are several popular frameworks for model training, a leading technology hasn’t yet emerged. Just like Apache Spark is considered a leader for data transformation jobs and Presto is emerging as the leading tech for interactive querying, 2020 will be the year we’ll see a front-runner dominate the broader model training space with pyTorch or Tensorflow as leading contenders. 

    Kubernetifying the Analytics Stack

    While containers and Kubernetes works exceptionally well for stateless applications such as web servers and self-contained databases, we haven’t seen a ton of container usage when it comes to advanced analytics and AI. In 2020, we’ll see a shift to AI and analytic workloads becoming more mainstream in Kubernetes land. Kubernetifying the analytics stack will mean solving for data sharing and elasticity by moving data from remote data silos into K8s clusters for tighter data locality. 

    Hadoop Storage (HDFS) is Dead but Hadoop Compute (Spark) Lives Strong

    There is a lot of talk about Hadoop being dead, but the Hadoop ecosystem has rising stars. Compute frameworks such as Spark and Presto extract more value from data and have been adopted into the broader compute ecosystem. HDFS is dead because of its complexity and cost and because compute fundamentally cannot scale elastically if it stays tied to HDFS. For real-time insights, users need immediate and elastic compute capacity that’s available in the cloud. Data in HDFS will move to the most optimal and cost efficient system, be it cloud storage or on-prem object storage. HDFS will die but Hadoop compute will live on and live strong.

    AI and Analytics Teams Will Merge Into One as the New Foundation of the Data Organization

    Yesterday’s Hadoop platform teams are today’s AI/analytics teams. Over time, a multitude of ways to get insights on data have emerged. AI is the next step to structured data analytics. What used to be statistical models has converged with computer science to become AI and ML. So data, analytics and AI teams need to collaborate to derive value from the same data they all use. This will be done by building the right data stack—storage silos and computes, deployed on-prem, in the cloud or in both, will be the norm. In 2020 we’ll see more organizations building dedicated teams around this data stack.

    Talent Gap Will Inhibit Data Technology Adoption

    Building the stacks that enable data technology into practice is hard, and this will only become more obvious in 2020. As companies discuss the importance of data in their organizations, they’ll need to hire the data, AI and cloud engineers to architect it. But there aren’t enough engineers who have expertise in these technologies to do that. This super-power skill is the ability to understand data, structured and unstructured, and pick the right approach to analyze it. Until the knowledge gap closes, we’ll continue to see a shortage of these types of engineers—many companies will come up short on their promises of “data-everywhere.”

    China Is Moving to the Cloud on a Scale Much Larger than the US and Will Leap Frog From On-Prem to Massive Cloud Deployments for Advanced Workloads

    Over the past five years, while enterprises in the U.S. have been moving in leaps and bounds to public clouds, enterprises in China have been investing mostly in on-prem infrastructure primarily for data-driven platform infrastructure. 2020 will be the inflection point where this changes. China will leapfrog into the cloud at a scale much larger than the U.S. by adopting the public cloud for new use cases, bursting in the cloud for peak loads and over time move existing workloads. Public cloud leaders in China will see dramatic growth that might outpace the growth of the current cloud giants.

    2020 is the year where the rubber meets the road when it comes to advanced analytics and AI. Companies that back the types of technologies that enable and support this kind of data and workloads will emerge as leaders in the space. On the other side, companies that structure their data teams to meet the requirements of the new data stack will emerge as leaders as well. I’m excited to see how advanced analytics and AI opens up new and innovative applications and use cases.

    Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit  featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

    — Haoyuan Li