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Tag: machine learning

  • Gotta Fix ‘Em All: AWS BugBust 2021

    Gotta Fix ‘Em All: AWS BugBust 2021

    Software bugs are vexing. Code reviews and error finding aren’t a particularly fun part of a developer’s job description. Finding bugs is tedious, time-consuming and hard on your eyes. Bugs not only wreak havoc on your code, they wreak havoc on your day. But what if bashing bugs could earn you a payoff greater than pride? That’s where AWS BugBust comes in.

    AWS BugBust is the world’s first global competition for Java and Python developers. The contest has two overall goals:

    1. Fix 1 million bugs and
    2. Reduce technical debt by over $100M, globally

    Code reviews and monitoring are common practice; some organizations even hold bug bashes where members come together to reveal bugs and fix them. However, fixing code is still an arduous process and there is more that could be done to make bug-bashing efforts fun and fast. AWS BugBust does both by adding in gamified points/competition/prize components!

    How does AWS BugBust work?

    The more bugs you fix, the more points you earn and the higher up in the leaderboard you get. Using Amazon’s CodeGuru, developers will compete to fix the most bugs (identified by CodeGuru Reviewer) and the most expensive lines of code (highlighted by CodeGuru Profiler). Amazon CodeGuru eases the pain of code review and error finding by automating the process. Using Machine Learning, Amazon CodeGuru will scan code quickly, identify errors and provide fix-it recommendations.

    Here is more info about the contest:

    How do you earn points?

    AWS BugBust uses Amazon CodeGuru Reviewer to analyze applications’ code to identify bugs and CodeGuru Profiler for performance issues. The bugs and profiling groups are available on the challenge dashboard and are available for participants to claim them, fix, and score points. Points are earned based on the severity of the bug (low, medium, and high)

    To score points, participants make code changes in their repositories, create a code review request, and trigger a code build. CodeGuru Reviewer will analyze the code changes as part of the build process and notify whether the code that was changed addressed the bug.

    The collective number of bugs fixed and dollars saved will be stored in the AWS Global BugJar. The totals for both metrics will show on the global leaderboard.

    Participants will receive digital badges and bespoke swag at three key milestones: 100, 2,000, 10,000 bugs fixed. Swag includes pins, t-shirts, BugJars, backpacks, and hoodies/jackets.

    When will AWS BugBust take place?

    The contest will launch Thursday, June 24. You can get started by setting up an AWS BugBust for your team.

    What’s the ultimate prize?

    The top ranking developers of the global leaderboard by the end of September 30, 2021 will receive an all-expense-paid trip to re:Invent 2021. AWS will also award the top 10 enterprises who collectively contribute the most bugs to the global BugJar through their private events with two tickets to re:Invent 2021 for their top BugBusting developers. If you’re a Java/Python developer, this competition will be like doing your job while enjoying a game-like atmosphere!

    Here are important rules:

    1. An AWS account is required to participate in the challenge (participants can create one at the beginning of the challenge or any time during the contest period).
    2. Customers who have their code bases in Java and/or Python and stored in supported GitHub, GitHub Enterprise, Bitbucket and AWS CodeCommit repositories are able to participate in the AWS BugBust.
    3. Challenge registration and console experience, overall, are much better on the desktop. Links in the console will direct to code repositories which are more easily accessible on the desktop, as well. Mobile placements will be considered for awareness, but desktop should be prioritized.

    AWS BugBust isn’t your grandma’s bug hunt. You now have machine learning capabilities on your side, including the ability to automatically find bugs for you and present the list of bugs to you – even before the challenge begins. You can make the arduous inspection process fun, win some prizes, be the hero at busting bugs and find them, starting June 24!

    We have also provided additional information about the AWS BugBust competition on devopsy.kinsta.cloud.

  • SREs Say AIOps Doesn’t Live Up to the Hype

    SREs Say AIOps Doesn’t Live Up to the Hype

    What can you expect when investing in artificial intelligence for IT operations (AIOps)? Real-time visibility across huge volumes of information. Lightning-fast event correlation and anomaly detection. Automated remediation and self-healing, without Ops personnel having to lift a finger.

    It all sounds amazing—at least, if you believe the marketing hype. But do those kinds of AIOps benefits actually translate to the real world? According to a recent survey of nearly 300 site reliability engineers (SREs), not so much.

    Among SREs surveyed—including engineers for some of the world’s largest, most innovative digital companies—just 12% use AIOps as a day-to-day part of their monitoring toolkit. Nearly 40% never use it at all.

    Why aren’t current AIOps solutions living up to the hype? And what can this gap tell us about the state of IT monitoring in general? Let’s take a closer look.

    Sprawling Complexity

    In theory, AIOps should be a perfect fit for SREs. These are, after all, the people with one foot in the world of operations, one in development, whose job is to make distributed software systems ultra-scalable and reliable. A solution that can turn mountains of data about those systems into actionable insights should be exactly what SREs need. And, in fact, 53% of SREs cite lack of unified visibility across the stack as their biggest cloud application monitoring challenge.

    And yet, if we gauge by utilization, today’s AIOps tools don’t seem to be delivering on that promise. Asked about the value of AIOps, just 7.5% of SREs, on average, view AIOps as “high-value”—even less than those SREs (9%) who perceive its value as low.

    Why is AIOps so slow to catch on? Ultimately, the barriers facing these tools are the same as those facing human engineers: massive and growing complexity in IT environments. As digital products become more dependent on third-party cloud services, as the number of things businesses want to track grows (from infrastructure to application to experience), the sheer volume, velocity and variety of monitoring data has exploded.

    Diverse Data Sources

    Compounding the problem, enterprises increasingly rely on multiple “same-service” providers for IT services. That is, they use multiple cloud providers, multiple DNS providers, multiple API providers, etc. There are sound business reasons for doing so, such as adding resiliency and drawing on different vendors’ strengths in different areas. But even when two providers are doing basically the same thing, they use different interfaces and instrumentation, and their data sources often employ different metrics, data structures, and taxonomies.

    Whether you’re asking a human being or an AI-driven tool to solve this problem, this heterogeneity makes it extremely difficult to visualize the complete picture across the infrastructure. It also creates gray areas around how best to take advantage of each vendor’s different rules and toolsets. All of which makes automating anything beyond the most superficial operations tasks hugely challenging, if it’s possible at all.

    Optimizing AIOps

    AIOps really is the solution to this problem—or at least, an important part of the solution. Machine learning is, after all, tailor-made to analyze large volumes of observational and engagement data and react in real-time, as well as to continually identify opportunities for improvement. But before we can get there, we need to change the way we think about AIOps.

    Don’t try to do everything at once.

    It would be great if we could deploy AIOps, let it learn about our environment and watch it magically start remediating issues on its own. But we’re not there yet. (And if a vendor tries to convince you we are, you should definitely be skeptical.) If we break down our expectations into smaller, more manageable chunks, however, AIOps can accomplish quite a lot.

    Event correlation, topology analytics and smart alerting, for example, are all areas where AIOps can provide significant value right now. For these more limited objectives, AIOps can absolutely reduce tedious manual effort for human operators, reduce mean time to repair, improve visibility and more, and digital businesses should be leaning on AIOps to do that.

    Invest in AI and ML training.

    Part of the reason adoption of AIOps tools remains sluggish stems from the fact that many teams still just don’t fully understand how they can use them. The more SREs are exposed to AI/ML training, the more opportunities they will identify for concrete, meaningful applications of AIOps. For example, only 27% of SREs said, “Experimenting or receiving training to expand knowledge or skills” was a major activity as part of their job.

    Embrace “Platform Ops.”

    The biggest step we can take is to rethink how we use AIOps within an organization. Today, AIOps tools—like SRE resources themselves—are often dedicated to a specific digital product or service. As a result, enterprises end up trying to apply their AIOps tools, and the SREs using them, to support everything associated with that product (DNS, API, CDN, cloud monitoring, etc). As IT environments continue to get even more complex, though, businesses will likely find that shifting to shared platform tools and teams yields better results.

    Instead of trying to apply AIOps across every part of a digital service, imagine applying it to one part of that service (for instance, focusing on just DNS, or just APIs). In this way, the AIOps tools, as well as the teams using them, gain in-depth expertise across all the different interfaces and instruments used for that function and get continually better at normalizing multi-provider data sources across them. At that point, this platform-specific AIOps capability can then be scaled and reused across the company—providing more advanced DNS AIOps (or API, or CDN, etc.) for multiple digital products and teams.

    Now, you’ve got an advanced, highly specialized toolset for optimizing an important platform function, regardless of which digital product happens to be using it. You’ve narrowed the scope of how you’re using a given AIOps implementation while going much deeper into that function than a more generic, broad-based implementation could achieve. All of a sudden, even more advanced automation and self-healing becomes possible. And the loftiest marketing claims of AIOps start to look less like hype and more like viable real-world outcomes for your business.

  • WSO2 Applies AI to API Management

    WSO2 Applies AI to API Management

    WSO2 today unfurled a Choreo cloud platform through which DevOps teams can create and manage application programming interfaces (APIs) more efficiently by leveraging machine learning algorithms and other forms of artificial intelligence (AI).

    Eric Newcomer, WSO2 CTO, said the integration platform-as-a-service (iPaaS) eliminates all the tasks associated with setting up a platform for and managing services based on APIs.

    Choreo, available in beta, employs AI to learn historical activities and performance behaviors in order to anticipate the needs of a developer. As developers build an API, the platform will provide performance feedback, identify anomalies, map data and even complete code, said Newcomer.

    The Choreo platform also comes with built-in observability tools for monitoring API usage and troubleshooting APIs, added Newcomer.

    In addition, Choreo provides access to the open source Ballerina programming language the company developed to enable developers to create APIs using both low-code and procedural code as they see fit, noted Newcomer. The low-code diagram that developers create generates source code that developers can then run wherever they want, said Newcomer. Developers can close the entire repository Choreo provides, Newcomer added.

    Developers can also trigger low-code integration logic to control hundreds of software-as-a-service (SaaS) APIs via pre-built connectors and templates provided by WSO2 or ones they create themselves.

    WSO2 is also making it possible to deploy APIs via a single click in Kubernetes environments, which eliminates the need to manage cumbersome YAML files. That capability is based on technology that WSO2 gained via the acquisition of Performer that the company revealed today.

    Finally, WSO2 is making available an API marketplace through which organizations can share APIs, event streams, data sources and templates.

    With the rise of microservices-based applications, API management has become a lot more challenging. Each additional microservice adds an API that needs to be managed and maintained. As more APIs are employed, it becomes increasingly difficult for DevOps team to keep track of the dependencies that exist between all those APIs without the aid of machine learning algorithms.

    It’s not likely machine learning algorithms will replace the need for DevOps professionals anytime soon. However, it’s becoming apparent that many of the manual tasks that DevOps teams routinely perform today are becoming automated by machine learning algorithms. In other cases. DevOps teams are gaining new capabilities that would have either been impossible for them to do on their own or would simply have taken too much time to complete.

    Of course, it takes time for any AI platform to learn the DevOps environment. IT teams need to invest a significant amount of time training an AI model to recognize relevant issues in much the same way they might train any new member of a DevOps team. The only difference is, once a machine learning algorithm learns something, they never forget it. Nor do they take a day off or announce one day they are leaving the company because they got a better offer somewhere else.

    It may take a while before DevOps teams are able to strike the right balance between man and machine, but one thing that is certain is that AI in DevOps environments is here to stay.

  • Harness Employs ML to Streamline DevOps Workflows

    Harness Employs ML to Streamline DevOps Workflows

    At the online {Unscripted} 2021 conference, Harness today announced an update to its namesake DevOps platform that includes support for feature flags that expose new capabilities to a select number of users for testing purposes along with the ability to prioritize the running of those tests based on the likelihood an application is likely to fail them.

    In addition, Harness is adding a unified pipeline capability that enables DevOps teams to streamline the management of multiple tools that make up a workflow and has updated its cloud cost management module to enable DevOps teams to automatically stop idle workloads. That capability is based on technology the company gained with its recent acquisition of Lightwing.

    Steve Burton, chief marketing officer for Harness, said Harness is now employing machine learning algorithms to identify which tests an application is most likely to fail. That test intelligence module enables DevOps teams to run those tests first versus testing an application only to discover close to the end of that effort that an application will need to be updated and then retested. The amount of time spent on testing is then sharply reduced, noted Burton.

    Overall, Harness is reducing the amount of time and effort spent maintaining DevOps pipeline and workflows, said Burton.

    The cost of maintaining a DevOps platform will naturally vary as the overall environment becomes more complex. It may not be clear how much time a DevOps team is spending on testing, but the faster that process becomes the more likely an application will be thoroughly tested before it is deployed in a production environment. Fixing applications after they are deployed in a production environment is, of course, considerably more expensive.

    At the same time, as the number of applications being built simultaneously increases, the amount of cloud resources being consumed also increases. Developers, however, don’t typically pay close attention to what workloads are consuming which cloud resources, which can result in an unpleasant surprise when the monthly bill from the cloud service provider comes due.

    In general, Burton says machine learning algorithms and other forms of artificial intelligence (AI) will soon become table stakes for any provider of a DevOps platform. The expectation is the platform will routinely employ AI to streamline processes and reduce total costs, noted Burton.

    Many of the manual tasks that DevOps teams regularly perform to maintain a DevOps platform will increasingly become automated as usage of machine learning algorithms becomes more pervasive. It’s not clear to what degree organizations may abandon a legacy DevOps platform to take advantage of those capabilities. However, over time it will become apparent that some organizations are spending a lot more time building and deploying applications than others as the next generation of DevOps platforms, infused with machine learning algorithms, are installed.

    In the meantime, DevOps teams might want to make a list of the manual tasks they perform today as part of an effort to determine how much time they are wasting that might otherwise be spent on tasks that are more valuable to the organization.

  • Three Ways Machine Learning Can Change Incident Management

    Three Ways Machine Learning Can Change Incident Management

    Incident management for important applications has many connotations, many of them negative. Most are well-deserved. Stress, panic, hurry, urgent, drop-everything, all-hands-on-deck, emergency, to name a few. Typically, there is a critical problem, ranging from complete failure to loss of certain functionality or performance. The problem or outage potentially means loss of revenue, loss of customers, negative social media commentary or bad reviews and more. Time is of the essence, dictating that the primary objective is fixing the incident as quickly as possible. Often, this means that symptoms or partial causes are mainly addressed rather than the underlying root cause.

    Without fully addressing the underlying issue, companies virtually guarantee that the same problem – or a similar one – will reoccur. Not identifying root cause often prevents a durable fix. In addition, companies lose the opportunity to proactively improve application code or infrastructure based on real-world experiences and issues. Postmortems may only result in reviews of monitoring and observability solutions and the inevitable updates to alert rules. Most DevOps professionals not only understand but have lived through these frustrations on an ongoing basis. Management, then, often wonders why their systems are so unstable.

    Changing the model for incident management has been limited by a combination of the overriding urgency combined with short-staffed, overworked teams. Although AI and machine learning has been positioned as the panacea for nearly every kind of technical ill, this is a clear case where “machines” could fundamentally enhance human efforts to improve a situation. The best troubleshooters exhibit a combination of instinct, experience and patience to carefully sift through reams of data, spotting unusual events and their correlation with bad outcomes. This turns out to be a perfect application for machine learning.

    By ingesting all logs and metrics to find meaningful problems or anomalies, systematized machine learning can quickly and comprehensively sift through all data to gain a thorough understanding of the real problem. Using this approach significantly speeds up the root cause analysis. Such an applied solution introduces three game-changers for incident management.

    Three Game-Changers for Incident Management

    First, incident remediation times can be reduced dramatically and with less of an all-hands-on deck, drop-everything frenzy. Rather than SREs and engineers spending hours digging through dashboards, traces and millions of log lines, an automated approach can accomplish it more quickly, more thoroughly and with less impact on human professionals, allowing them to focus on other responsibilities. This instantly relieves stress and panic and alleviates pressure because incidents can be remedied better and faster, restoring the application to its expected service level. The value is, of course, two-fold: faster time to resolution and greater efficiency or productivity of the teams who would otherwise be involved. Ending or mitigating the frustration professionals have to deal with is an additional bonus.

    Second, besides faster MTTR, an automated, machine-learning-driven approach is more comprehensive and gets to the root problem. This means that future incidents may be avoided altogether. This also means a potential end to the alert rule “hamster wheel.” The knee-jerk tendency to continually adjust alert rules and settings to enable better “early warning systems” is far less necessary. Instead of trying to gain an early indication of a similar failure in the future, the cause of such failures can be found and addressed proactively. Eliminating such problems in the first place is far better than trying to evolve alerts to catch unresolved causes. Alerts are still important, but machine learning can shift the basis for them to more root-level issues, and they can be constructed automatically by the system.

    Third, automated, machine-learning-based incident management can help proactively catch silent bugs that may not yet directly be part of an incident. It can also inform developers earlier in the cycle. Not terribly long ago, new releases were tested extensively before deploying to production, allowing for the creation of specific test plans, conducting stress tests and potentially catching bugs that might have potentially terrible consequences in the future. Now with faster, non-stop development cycles, testing mainly occurs during production. Machine learning can surface new or unusual errors, event patterns and patterns in metrics. The approach can proactively surface subtle bugs early, before they impact users or cause widespread failure.

    Changing the DevOps Life Cycle

    Each of these three results significantly changes not only the incident management life cycle, but also DevOps as a whole. Poring over logs and metrics is a perfect application for machine learning and offers the opportunity to fundamentally change incident management, transforming it from a scourge and necessary burden to a competency. An automated system of machine learning applied to incident management advances DevOps rather than encumbers it.

  • Do You Need a Cloud AI Engineer?

    Do You Need a Cloud AI Engineer?

    For all the technological advancements we are witnessing in AI and machine learning, the practical benefits remain elusive in the non-tech business world, frustrating decision-makers as AI investment climbs. For example, over the past several years, many Fortune 500 companies have invested in strong data science teams and AI labs, but still struggle when it comes to producing and scaling their models. Moreover, a recent report by Algorithmia found that, while enterprise budgets for AI and ML are rapidly increasing for 83% of organizations studied, deployment challenges persist.

    These deployment issues often begin when leadership fails to exert enough upfront engineering focus to ensure their proof of concepts (POCs) are “real-world ready.” The challenges are compounded when businesses put these models into action using insufficient, outdated or cobbled-together IT infrastructures. This combination results in POCs that either fall apart under real-world conditions or are rife with inefficiencies. Fortunately, cloud tech today provides the opportunity to overcome these challenges.

    Businesses can enable development, experimentation and scaling of data science and AI solutions with more flexibility and speed than ever through cloud platforms. A design-led AI project that uses the tools available in the cloud can help organizations create business impact and realize ROI much faster.  So while the role of data scientist may have been the ‘sexiest job’ in recent years, we may be seeing a new ‘sexiest job’ title emerging: the cloud AI engineer.

    Why do we need a Cloud AI Engineer?

    Traditionally, a successful AI project in the cloud requires a team with different skills and titles, including data scientists, big data engineers, cloud engineers and full stack developers. This has led businesses to look at ways to merge these specialties into one specific title. Enter the cloud AI engineer who, theoretically, would be able to cover all these duties — including knowledge of and experience with the tools and accelerators that cloud platforms now offer, as well as demonstrated aptitude for fast-paced development in AutoML. These are skills and tasks previously spread out across other areas and roles within the organization.

    What is the Job Description?

    So what would this job actually look like? The cloud AI engineers of the future will focus on the deployment of AI and ML models at scale in the cloud, and on integrating them with existing products and IT systems. Additionally, cloud AI engineers would need knowledge of the different types of machine-learning algorithms; familiarity with frameworks like Tensorflow and PyTorch; hands-on experience with data processing; analysis and feature selection using analytic tools and ETL/ELT frameworks (Apache Spark, Hive, Amazon, Athena, etc.); and model deployment experience on cloud platforms like AWS, Azure or Google Cloud.

    Obviously, tackling all of these would be a huge undertaking for one individual; given the vast workloads on both the cloud and engineering fronts, it isn’t possible to replace these workers and their skills with just a handful of cloud AI engineers. Instead, cloud AI engineers will sit between the two disciplines and the workforces and will serve as a key bridge to easily deploy models and integrate them with enterprise systems and tools for consumption.

    Where are We Going to Find Cloud AI Engineers?

    Given that cloud AI engineers will have a robust level of responsibility, they will quickly become some of the most sought-after team members — but that also means talent for the role will likely be in short supply.

    Additionally, individuals with this broad skill set will not appear overnight — at least not en masse. Therefore, to build the cloud architecture capable of delivering the real-world AI results that businesses need today, tech companies need to find ways to break down the internal divides between data scientists and cloud engineers so that they can cross-train, learn and develop skills in each area. If this is done correctly, not only will tech companies have a more well-rounded workforce, but they will be able to deliver the practical AI success that non-tech businesses are looking for today.

    Despite excitement about AI and its benefits, operational challenges remain. By focusing on the cloud and building up specialists in this space, AI can begin to drive the practical results the business world needs far more quickly.

  • Enterprise ML Investments on the Rise

    Enterprise ML Investments on the Rise

    The second wave of enterprise artificial intelligence and machine learning (AI/ML) adoption is building, and is expected to crest in 2021, as many organizations increased and accelerated their investments in tools and people.

    That was one of the unmistakable conclusions of our 2021 enterprise trends in machine learning report: 83% of the companies we surveyed said their AI/ML budgets had increased year-over-year, and we found that the average number of data scientists they employ has increased by 76% year-over-year. These changes came even as many organizations tightened their overall spending and refocused their strategic priorities amidst all of 2020’s disruption and uncertainty related to the COVID-19 pandemic. These companies aren’t simply moving forward with their AI/ML initiatives in spite of the pandemic; they’re doubling down on their investments because of it.

    Many organizations have begun to understand just how critical ML is to remaining competitive in uncertain and rapidly evolving conditions. When we ran a separate survey during the summer of 2020 to find out how businesses adapted to the pandemic’s impacts, it revealed a renewed sense of urgency around AI/ML investments: 43% of respondents said that AI/ML mattered “way more” to their business than they’d previously realized.

    Our independently conducted 2021 report included input from more than 400 leaders and practitioners across a diverse mix of technical and business functions, all of whom have a stake in their organization’s AI/ML strategy. They’re not just increasing their spending and staffing, either – they’re accelerating their pursuit of a growing number of enterprise ML use cases, with customer experience and automation leading the way.

    You need to be doing this, too. The second major wave of enterprise AI/ML adoption is underway, and you can still capitalize on the immense opportunities ahead. But the time to act is now. Organizations that take a “wait-and-see” approach are at risk of being left behind, particularly as so many other companies are scaling up their investments. Not only are your competitors increasing their AI/ML budgets (evident in the 83% YoY increase mentioned above), but they are moving their initiatives to the top of their strategic priority lists. Approximately three out of every four respondents (76%) in our 2021 report said they are prioritizing AI/ML over other IT initiatives, with 28% reporting that it is their top priority overall. These companies have realized that their success is directly tied to their ability to adapt to rapidly changing business conditions — and that AI/ML is the key to doing so.

    This is no longer the sole purview of elite technology companies. Companies of all sizes, across just about every industry, can get up and running with AI/ML, thanks in large part to the spread of easy-to-use tooling. The opportunities are more accessible than ever. You don’t need a ton of time or resources to get started — and it’s absolutely crucial that you do so soon.

    Your competitors are actively pursuing AI/ML initiatives today, regardless of your specific industry. We’re seeing many longstanding enterprises begin to drive huge transformations of their businesses and industries with AI/ML. More and more use cases are being pursued in legacy industries, from insurance to agriculture, healthcare to financial services and more. The bellwether financial firm JPMorgan Chase, for example, has even built an entire AI research program and data platform. There are virtually no industries where this is irrelevant. So, you need to act with urgency, as this second wave of enterprise adoption intensifies.

    You also need to build for operational efficiency and scale. We think of this as industrializing your machine learning operations (MLOps). While easy-to-use tools reduce the barrier to entry, they only get you partway to your destination. Invest in repeatable, automated processes or you’ll hit a wall of technical debt and other potential issues. MLOps paves the road to tightly integrated, well-aligned technology stacks, processes and teams that make it easier, not harder, to deploy more models in the future. This isn’t something you can wait on; if you’ve manually bootstrapped the deployment of even a handful of models to production, you need MLOps. Without it, you’ll quickly accrue unnecessary technical debt that will inevitably hinder your organization’s ability to scale and deliver meaningful business value.

    This is already a significant issue. Our 2021 report found that, while many organizations are increasing budgets and prioritization, it’s taking them longer to get models into production. Data scientists are still spending too much of their time on operational tasks, a problem that worsens as the number of models in production increases. This tells us that even as many organizations increase their spending and hiring, they’re actually allocating more of their growing resources toward manually scaling their AI/ML initiatives instead of investing in the operational efficiency that will maximize results.

    There’s a better way. Organizations no longer need to take a DIY approach to MLOps. There are robust solutions that can simplify many of the complex issues involved in scaling your AI/ML initiatives in production. Our report found that companies that use third-party MLOps solutions see improved outcomes compared with companies that go it alone and build and maintain everything themselves. They spend less on infrastructure, deploy models to production faster and tie up less of their data scientists’ time on model deployment.

    The pattern here is clear. Regardless of your particular industry, more and more businesses are identifying a growing list of use cases for AI/ML that can help transform their businesses and boost their competitiveness. They’re industrializing their AI/ML initiatives with MLOps to ensure their technology stacks, processes, and people are well-aligned. And, by doing so, they’re positioning themselves to rapidly adapt to and act on changing signals in their businesses.

    Don’t make the mistake of thinking AI/ML doesn’t apply to you or your industry. Doing so would mean missing out on potentially massive strategic opportunities, from new or growing revenue streams to increased efficiencies throughout your business operations. Your competitors are doing it; it’s easier than ever to get started and third-party MLOps solutions can help minimize technical debt and other operational complexity that will otherwise limit the positive business impacts of your initiatives.

    You can absolutely get in on the second wave of enterprise adoption, but the time to act is now. You can’t afford to wait.

  • The Evolution of Technology Driven Banking

    The Evolution of Technology Driven Banking

    History repeats itself with every technological revolution, sparking the same old fears about job loss and human redundancy. Much of this is unfounded because each time, when routine and repetitive jobs got mechanized, those who were displaced often were elevated into bigger, better roles. The pattern was consistent across industries; whether it was manufacturing, retail, entertainment or banking, the technology of the day – from the steam engine to the mainframe – augmented and amplified, rather than eliminated, the work of human beings.

    This time around, the debate is about cloud-driven banking shaking up employment in the financial sector. But the reality is the same, as always.

    A Look at Banking Functions

    To understand what is happening, let us segregate various banking functions from an inside-out perspective. Strategy definition occurs at the top, at the level of the board and senior management. Lower down the order, the strategy is converted into plans, which are then executed by various teams. All these activities involve data operations such as collection, comparison, tracking, monitoring and course correction. There is no interaction with the end customer just yet.

    The second set of functions is aimed at achieving strategic and financial goals. These functions relate to business execution – expanding the corporate, retail or investment banking business, adding branches, acquiring customers and so on. The desired outcomes are defined as KPIs for individual managers, expressed as growth targets and budgets.

    The moment of truth is when these internal functions flow out of the bank to the last mile – in other words, when a frontline employee, such as a branch manager or relationship officer, comes in contact with the end customer. Not much has changed in the way banking has been transacted since 14th century Greece. The counter has been replaced by the teller window, but the interactions are still about facilitating lending, borrowing and payments. What has changed, however, is the number and type of participants. In addition to the money lenders and joint stock companies of old, today there are banks, of course, but also fintech firms and big tech companies. The mode of service delivery also has changed (perhaps beyond recognition) to include branches, mobile phones, websites, wearable devices and even social media applications.

    The Role of Machines

    Let us examine the role of machines (which includes software and digital technology, including cloud) in each set of functions. Strategy visioning and implementation clearly need human intelligence; however, machines can provide rich, accurate and timely data so the people in charge can make better decisions. Similarly, while planning activities – involving interpretation, visualization and application of past experience – too cannot be handed over to machines, they can certainly leverage machine learning to do what-if analyses and simulate various scenarios in real-time.

    Business execution functions offer more potential for humans and machines to actually work together, by farming out repetitive and routine work, such as data collection, comparison and analysis, to the latter. Following that, the human workers will apply their wisdom to machine-generated insights to take course-corrective actions to, say, improve revenue, lower cost, enter new markets, etc. In each case, far from taking over the role of human workers as feared, the machines are actually elevating them.

    Several operational goals are set up as part of business functions, with managers at the head office, zonal or branch level assigned responsibility for achieving them. While the managers make the decisions – for example, which channels to employ – implementing those decisions at scale is only possible with the help of machines – think origination, onboarding, document verification, KYC checks, credit rating analyses etc. This is how Paytm onboarded 200 million customers when India announced demonetization, and RBL acquired massive digital deposits during the pandemic lockdown. Without support from machines, their human employees would only have been able to handle a fraction of the business volume.

    That being said, there are some jobs that can, and should, be given to machines. A good use case is technical resolution – attending to issues in databases, networks, servers or applications – that can be performed proactively, preemptively and efficiently by AI-driven systems at a scale that is out of reach of human workers. What’s more, the systems will continue to learn and improve over time, so humans need only step in to resolve complex or exceptional issues. When a customer calls a contact center, call tracking, tracing and auditing, as well as caller identification, are best left to the machines, which will also route the call to the best available agent; the system can also suggest the best solution based on past history. Note that while the machines take over these jobs, no roles are actually lost. The agent remains in charge of making the final decision and communicating it to the customer.

    Cloud-Driven Banking

    Now let’s look specifically at the impact of cloud-driven banking on human workers. When it comes to lowering cost while improving agility, the cloud is unbeatable. Today, when banks are under immense pressure to reduce cost from both a low-interest environment and new competitors operating at half their cost-to-income ratios, it is unviable for them to maintain expensive physical infrastructure, such as data centers. Cloud service providers and hyperscalers are the answer for managing everything from servers and systems to databases and data centers. Outsourcing these functions relieves the banks’ operators but does not make them redundant, because they can be redeployed in higher roles, such as planning for growth. This means that instead of providing services to the business organization, they will demand services from the cloud providers; in other words they will become “service seekers” instead of “servicers.” In this role, they will require higher-level skills like negotiation, critical thinking and decision making.

    To sum up, whether it is artificial intelligence or machine learning, when technology takes over a routine banking function, it always opens the doors for human workers to elevate themselves into a more rewarding role. Cloud-driven banking is no different.

  • 5 Best Machine Learning Use Cases

    5 Best Machine Learning Use Cases

    Many industries are struggling with the ongoing COVID-19 pandemic, and the IT industry itself and the broader trend of transition to remote work over the last year revealed many areas where traditional approaches to managing businesses created unnecessary waste. Data science and its counterpart, machine learning, revealed that expansion in the ways technology can facilitate new ways of working is nearly limitless.

    Machine learning uses powerful algorithms to discover insights based on real-world data. These insights can then be used to make predictions about future outcomes. As new data becomes available, machine learning-enabled programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

    Here are five of the most common use cases where machine learning can make a big difference:

    When engineers can’t code rules for certain problems. Many human-oriented tasks (such as recognizing whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend multiple factors and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

    When you need to scale a solution to millions of cases. In finance, for example, you might be able to manually categorize a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organizations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little to no human intervention.

    When you can do it manually, but it’s not cost-efficient. There are situations in which in-house experts could process many requests quickly and accurately, but at a high cost. For instance, imagine you assess DMV forms for in-state and out-of-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimized and serialized. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use of your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

    When you have a massive dataset without obvious patterns. Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem you’re trying to solve. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like interpreting tracers can even describe how machine learning models arrived at their conclusion(s).

    When you live in an ever-changing universe (adaptive). The world, and its problems, are always changing. A problem you solved yesterday can, today, easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organization processed medical appointment records to extract diagnoses, procedure information and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time, 24/7. Meanwhile, incorrectly labeled items could lead to insurance rejections, huge fines and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire life cycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

    It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are, essentially, advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent on error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

  • AI Automation Won’t Replace Jobs – It Will Replace Processes

    AI Automation Won’t Replace Jobs – It Will Replace Processes

    As Software 2.0 takes shape, how are AI processes—and the jobs that go with them—changing?

    When it comes to new AI automation, the Software 2.0 movement can be seen as akin to the auto industry’s recent evolution. Beginning in the 1990s and rapidly progressing into the 2000s, the auto industry saw a huge rise in AI automation such as digital vehicle diagnostics.

    Throughout this, people worried that digital transformation might take jobs away, when instead it led to “greater profits, productivity, and competitiveness,” according to a 2008 study by the journal of Technological Forecasting and Social Change. In short, these innovations didn’t replace jobs, per se; they simply changed the processes of jobs from the manufacturer’s assembly line down to the mechanic’s garage.

    An auto diagnostic specialist used to perform tedious tasks such as counting flashes and converting them to error codes on printed-out tables. Computerized diagnostic tools made this process faster, more reliable and, you might imagine, far less tedious. According to diagnostic specialists Helmut Frank and Uwe Schmidts, digital transformation went from being “a necessary evil to being a key to new, interesting and innovative functions.”

    You might liken the above example to the innovations that AI automation and machine learning (ML) have offered to software developers, who until recently, were tasked with a much more hands-on approach to deploying and maintaining applications. In many ways, DevOps practices have heroically come in to break them free of this minutiae, automating many of the repetitive tasks involved in application management. By design, it’s increasing efficiency and security while decreasing tedium and the chance for human error.

    Speaking to tedium, Mike Loukides and Ben Lorica, in their article “The road to Software 2.0” noted, “Up until now, we’ve built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. The process is slow, tedious, and error-prone; most of us have spent days staring at a program that should work, but doesn’t.”

    In short: Much like digital transformation brought efficiency to the life cycle of automobiles, AI automation is offering efficiency to every stage of the software development cycle.

    Don’t Change the Players, Change the Game

    Indeed, a new generation of AI automation and ML tools is now emerging, shaping what some call a Software 2.0 movement. But before you begin to fear that these tools will lead to a “they took our jobs!” revolt, consider that they may be celebrated instead as a means of not only augmenting the work of software developers but also helping offset the ongoing developer drought. According to a 2019 Stack Overflow survey, over a third of developers named “not enough people for the workload” as their primary challenge to productivity. Rather than replacing jobs, automation should be viewed as offering efficiency in an industry that desperately needs it.

    As an example, the process of writing code, though more streamlined today than it was 25 years ago, has remained a mostly manual process. This has presented an excellent opportunity for the automated processes we are beginning to see, which bring in AI to automate aspects of code development such as program synthesis, smart code completion and static code analysis.

    Assaf Araki spoke to this in the article, “Software 2.0 takes shape.” Araki described how automation is filling gaps in many other areas, such as regression testing, code analysis, AI for vulnerability detection and observability tools to monitor software health.

    From Monolith to Microservices

    Just like the recent, mysterious disappearing monolith in the Utah desert, the relevancy of monolithic development may be on its way out.

    In the past, software cycles were longer. Teams updated monolithic-style deployments only monthly or yearly. Nowadays, companies are under pressure to push much more rapidly, often turning to microservices and progressive release strategies. This new paradigm requires much automation to increase release efficiencies and enable a seamless CI/CD.

    As Araki pointed out, “Automation in software is best viewed through the lens of Continuous Integration (CI) and Continuous Deployment (CD).”

    How to Know When AI Automation is Worth Its Salt

    It’s smart to consider the investment of both money and time when deciding whether an AI solution is worth adopting. “AI systems need to add value that exceed any new costs and requirements (such as the need for labeled data, hiring new staff, etc.),” Araki said.

    You should also be asking if the solutions are wide-ranging enough to meet the needs of your developers and anyone else on the team who may be employing them. Key considerations for AI automation, Araki noted, are focusing on tasks and systems that are frequently used, adjusted or tuned.

    4 Examples of Versatile AI Automation and ML:

    1. Data management systems from simple systems that employ ML to more complex systems that enable self-driving data management.
    2. Dashboards that are automatically created, ideally with intuitive and visually appealing UX, that use AI automation and ML to bring up the right elements—more progress is expected in these areas in the coming years.
    3. Websites and mobile apps that use AI automation to become highly personalized interfaces, such as those that employ real-time data for online and reinforcement learning—these are also expected to progress in the coming years.
    4. UX and design tools that use simple automation to design and revise digital media, with ML being a promising way to kick the automation up a notch, such as deep learning for game developers.

    AI Automation and ML to Grant Opportunities

    In short, Software 2.0 doesn’t currently appear poised to threaten jobs or decrease developer need. Instead, it seems to be changing the processes engineers are using and creating efficiencies in their work, at a time when their expertise is in increasing demand. Demand for novel roles, like the Cloud Economist, demonstrates the emergence of new opportunities for humans with cloud knowledge who work alongside AI.

    The new evolution of AI and ML also could create a unique subset of developers with skills keeping par with these new innovations—much like digital transformation in the auto industry led to evolving specialties in computerized vehicle systems.

    Not to mention that just as low-code has paved the way for citizen developers and AutoML led to citizen data scientists to fill large gaps in demand, AI and ML solutions have the potential to further broaden and democratize software and code development.