8 Reasons Enterprises Are Slow to Adopt Machine Learning

As CTO of Data Science at AppDynamics, and in my previous role as co-founder of Perspica, I’ve seen machine learning make huge strides in recent years. ML has helped Netflix perfect binge watching, taught Siri how to sound more human and made Amazon Echo a fashion consultant. But when it comes to machine learning use cases for the enterprise, it gets a whole lot more complicated. It’s easy to apply an algorithm to a one-off use case, but comprehensive enterprise applications of machine learning don’t exist today.

Here are the top 8 challenges standing in the way of widespread adoption of machine learning in the enterprise.

1) Confusion Over What Constitutes Machine Learning

Part of the problem is a lack of understanding around what machine learning is. Machine learning is an application or subset of AI, which is generally thought of as higher-order decision-making intelligence.

Machine learning is really about applying mathematics to different domains. It locates meaning within extremely large volumes of data by canceling out the noise. It uses algorithms to parse the data and draw conclusions about it, such as what constitutes normal behavior.

2) Uncertainty About What Machine Learning Can Do

Machine-learning algorithms don’t enter chess tournaments. What they are really good at is adapting to changing systems without human intervention while continuing to differentiate between expected and anomalous behavior. This makes machine learning useful in all kinds of applications—think everything from security to health care—as well as classification and recommendation engines, and voice and image identification systems.

Consumers interact daily with dozens of machine learning systems including Google Search, Google ads, Facebook ads, Siri and Alexa, as well as virtually any online product recommendation engine from Amazon to Netflix. The challenge for enterprises is understanding how machine learning can add value to their business.

3) Getting Started Can Be Daunting

Machine learning is usually introduced into an enterprise in one of two ways. The first is that one or two employees start applying machine learning to gain insight into data they already have access to. This requires a certain amount of expertise in data science and domain knowledge—skills that are in short supply.

The second is by purchasing a solution, such as security software or application performance management solution, that uses machine learning. This is by far the easiest way to begin to realize some of the benefits of machine learning, but the downside is an enterprise is dependent on the vendor and is not developing its own machine learning capabilities.

4) The Challenge of Data Preparation

Machine learning can sound deceptively simple. It’s easy to assume that all you have to do is collect the data and run it through some algorithms. The reality is very different. Once you collect the data then you have to aggregate it. You need to determine if there are any problems with it. Your algorithm needs to be able to adapt to missing data, outlying data, garbage data, and data that’s out of sequence.

5) The Lack of Public Labelled Datasets

In order for an algorithm to make sense of a collection of data points, it needs to understand what those points represent. In another words, it needs to be able to apply pre-established labels to the data.

The availability of publicly labelled datasets would make it much easier for companies to get started with machine learning. Unfortunately, these do not yet exist, and without them, most companies are looking at a “cold start.”

6) The Need for Domain Knowledge

At its best, machine learning represents the perfect marriage between an algorithm and a problem. This means domain knowledge is a prerequisite for effective machine learning, but there is no off-the-shelf way to obtain domain knowledge. It is built up in organizations over time and includes not just the inner workings of specific companies and industries, but the IT systems they use and the data that is generated by them.

7) Hiring Brilliant Data Scientists Is Not a Panacea

Most data scientists are mathematicians. Depending on their previous job experience, they may have zero domain knowledge that is relevant to their employer’s business. They need to be paired up with analysts and domain experts, which increases the cost of any machine learning project. And these people are hard to find and in high demand. We are lucky at AppDynamics to have a team of data scientists with broad experience in multiple fields who are doing ground-breaking work.

8) Machine Learning Lacks a Shared Vocabulary

One of the challenges encountered by organizations with successful machine learning initiatives is the lack of conventions around communicating findings. They end up with silos of people, each with their own definition of input and their own approach to sampling data. Consequently, they end up with wildly different results. This makes it difficult to inspire confidence in machine learning initiatives and will slow adoption until it is addressed.

At AppDynamics we’re excited to apply our machine learning expertise to solving enterprise IT problems. And you may be interested in my insights on how the arrival of AI and machine learning in the enterprise will have a profound impact on IT departments.

AI’s Arrival in the Enterprise Will Have Profound Implications for IT

At the end of Twentieth Century in the wake of Deep Blue’s triumph over Garry Kasparov, it was popular for computer scientists to speculate about when human begins would begin to interact with artificial intelligence. It was generally believed that machines with true reasoning capabilities were decades away. In an interview with author Michio Kaku, published in Visions: How Science Will Revolutionize the 21st Century, AI expert and Carnegie Mellon professor Hans Moravec predicted that robots would be able to model the world and anticipate the consequences of different actions sometime between 2020 and 2030.

Twenty years later, we are no longer wondering about how artificial intelligence (AI) will first appear in our lives. It has arrived in the form of virtual assistants like Alexa and self-driving cars. But this can give a misleading impression of what we can expect from AI in the next few years. AI software is not going to evolve human-like reasoning capabilities anytime soon.

Indeed, most of what is described as AI is really machine-learning algorithms that act largely as detectors. These algorithms analyze massive amounts of data and learn to discriminate between normal and anomalous behavior. AI, where it exists, is similar to a decision-support system for reacting to behavior as it changes. But even in these early stages, machine learning and AI are changing the game for IT operations. In the next few years, the impact of machine learning and AI will be profound.

The problem enterprises are facing is that computing environments have simply grown too large and too complex for human beings to monitor alone. To effectively monitor enterprise systems, IT must track millions of metrics per second. This is not a challenge that can be met by putting another screen on the wall of the network operations center. There are already too many screens, and just contemplating the number of screens that would be required is overwhelming. Even more daunting is figuring out the five or ten metrics that matter the most out of five or ten million as every new millisecond brings the system to a new dynamic state.

The company I founded, Perspica, which was was acquired last year by AppDynamics/Cisco, solved this problem for our customers by applying machine learning and AI to massive amounts of streaming telemetry data generated by applications and IT infrastructure. What Perspica did was surface all the relevant metrics and then use those metrics to accelerate root cause analysis and reduce the mean time to repair. But Perspica’s ability to grow beyond that was limited by the data that we had access to. In fact, everyone involved in machine learning and AI at that time faced the same limitation. We lacked a source of truth on which to train our algorithms to go beyond what they had already achieved.

But this limitation is rapidly being overcome. Increasingly, data scientists are gaining access to new sets of what we call labelled data—sets of numbers or strings of text that a computer can understand as a true representation of something else. Data scientists who work with IT data, in particular, are finding that enough labelled data exists that we can realistically begin talking about automating large parts of IT in the next two or three years. And that is only the beginning.

In the future, every enterprise is going to have some combination of machine learning and AI to monitor its computing environments, and, equally important to understand how changes to those environments affect business goals. As these systems are deployed, they will become smarter and more sophisticated. Every application, every server, and every port on that server will have its own unique AI model, which means if you have 50 applications running on 10,000 servers you will need to train 500,000 models. This is not something that is going to be created overnight. But once these models are put in place, self-healing systems will become standard.

We’ll see AI playing a role in everyday IT and business events. For example, imagine a large airline that is planning on holding a worldwide promotion. The airline’s IT department rolls out new application code as a canary deployment. But the monitoring system soon reveals the new release is performing worse than the old code. While the business owner and IT staff are realizing that the code push has failed, the airline’s AI system is determining the root cause is a disk space issue and taking steps to address the problem.

For many years, Perspica and others were doing detection. Today, as we broaden the libraries, increase the sets of problems that have solutions and bundle those solutions together, we’ll be able to start doing remediation. Moravec, it seemed, had the timeline correct.

What will happen in the next twenty years? The media sometimes promotes “fear of AI.” But I see AI making business more profitable and people more productive. It will improve service quality, reliability, and availability not only in IT but across all industries. And in this way, AI will not only have profound implications for IT. It is also bound to improve the human condition.

Accelerate Your Digital Business with AppDynamics Winter ‘17 Product Release

Last month at AppD Summit New York, we unveiled the latest innovations in our Business iQ and App iQ platforms, paving the way for a new era of the CIO and digital business. Delivering on this vision, we’re excited to announce the general availability of AppDynamics’ Winter ‘17 Release for our customers.

As application and business success become indistinguishable, enterprises are increasing their investment in digital initiatives. According to Gartner, 71% of enterprises are actively implementing digital strategies, and IDC predicts that companies will spend $1.2 trillion on their digital transformation in 2017 alone.

But without effective tools to correlate application and business performance – and lack of end-to-end visibility across customer touchpoints, application code, infrastructure, and network – customer experiences and employee productivity are degraded, and executives can’t analyze or justify technology investments. In fact, according to McKinsey, the digital promise still seems more of a hope than a reality, with only 12% of technology and C-level executives confident that IT organizations have been effective in this shift.

Winter ‘17 Release is Here

Business iQ just got better. Bridging the gap between the app and the business, BiQ capabilities have expanded to include:

Business Journeys

With AppDynamics Business Journeys, application teams can link multiple, distributed business events into a single process that reflects the way customers interact with the business. Business events can include transaction, log, mobile, browser, synthetics, or custom events and are long-running, from hours to days.

Application teams can create performance thresholds and quickly visualize where performance issues are impacting the customer experience. KPIs for each Business Journey inform technology investments and effectively prioritize code development and release.

In the two figures below, you can see how easy it is to set up a new Business Journey for loan approvals and visualize the impact of delays through the lens of the business.

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Fig 1: Author an end-to-end Business Journey by joining multiple distributed events.

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Fig 2: Quickly and easily create custom dashboards visualizing business performance.

Experience Level Management (XLM)

With XLM, enterprises can establish custom service-level thresholds by customer segment, location, or device. For example, the CIO of a major retailer may deliver tailored experiences to its top customers by setting performance thresholds across its customer channels — including website, mobile apps, in-store wireless, and in-store checkout. XLM also provides an immutable audit for service-level agreements with your customers or internal business units. The product images below show the service levels setup for a connected streaming device, giving an instant view on how services are performing against set SLAs.

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Fig 3: Service levels setup for a connected streaming device.

Network Visibility

Application developers, IT Ops  and network teams often work in silos using a myriad of different monitoring tools. To troubleshoot application performance issues, war rooms are created, and the lack of a common language and visibility across different tools results in finger pointing, endless debates, and slower Mean Time to Resolution (MTTR).

With the introduction of AppDynamics Network Visibility, a capability AppDynamics is uniquely positioned to address now as part of Cisco, enterprises will be able to understand the impact that the network is having on application and business performance. Network performance measurements are automatically correlated with application performance in the context of the Business Transaction. IT teams will be able to triage network issues with one single pane of glass and provide the right information to network teams before there is an impact on the end-user experience. Finally, an answer to end-to-end visibility from customer, to code, to network is here.

AppDynamics automatically discovers network devices such as reverse proxy load balancers deployed on-premises and in cloud environments and eliminates the need to use expensive network tools such as SPAN/TAP to capture and analyze network traffic.

The animation below shows out-of-box visibility into network flow maps, network metrics such as latency, throughput, retransmission rates, and critical errors, enabling IT Ops to quickly identify and isolate root cause without the need to engage network teams.

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Fig 4: Correlated and out-of-box view of network performance in context of application performance.

AppDynamics IoT

IoT devices create another channel to engage with customers, and if properly measured and optimized, can create game-changing business benefits. With new IoT visibility, businesses can convert rich and invaluable insights into consumer behavior, buying patterns, and business impacts. IoT visibility includes:

Device analytics  — Together with Business iQ, IoT visibility provides an unprecedented insight into how IoT devices are driving business impact. And because these insights are delivered through a single platform, IoT visibility is the first and only solution that maps and correlates entire customer journeys — from the device to customer touchpoint, to business conversions.

Device application visibility and troubleshooting — AppDynamics’ new IoT visibility provides an aggregated view into device uptime, version status, and performance, enabling drill-down views into the device to simplify the troubleshooting of IoT applications. The screenshot below shows a list view of all active devices. A simple double-click on a specific device takes you to the device details.

Custom dashboards — Every company measures success differently. With custom dashboards in IoT visibility, companies from any vertical can quickly build new visualizations to measure the business impact of IoT devices — from the revenue impact of a slow checkout for a brick and mortar retailer, to the customer impact of a software change in a connected car.

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Fig 5: Consolidated list view of all active smart-shelf  IoT devices and key KPIs.

Synthetic Private Agent

AppDynamics Winter ‘17 Release brings Browser Synthetic Monitoring to your internal network. By running Synthetic Private Agent on-premises, you can monitor the availability and performance of internal websites and services that aren’t accessible from the public Internet. You can also test specific locations within your company and set alerts when performance issues occur and fix them before end-user experience is impacted.

Cross-Controller Federation

As application teams start using microservices architecture, the scalability requirements have exploded, necessitating APM scale. With Cross-Controller Federation, AppDynamics is taking unified monitoring to the next level. Our customers can achieve limitless scalability and flexibility to deploy application components across multiple public and private clouds.

Only with AppDynamics, customers get complete correlated visibility and quick drill-down into the line of code, irrespective of where the application components and controllers are deployed, because controllers can participate in a federation. Another important use case is keeping APM data isolated by deploying multiple controllers yet maintaining correlated visibility for compliance, architecture, and business reasons.

KPI Analyzer

KPI Analyzer applies machine learning to automate root cause analysis. With the KPI analyzer, customers can isolate the metrics that are the most likely contributors to poor performance, and identify the likely degree of impact on the KPI for each metric, automatically. The KPI analyzer makes troubleshooting root cause as simple as clicking a prompt to surface the underlying issue most likely to be the root cause of degraded performance.

The following figure shows KPI Analyzer in action. KPIs such as average response time are displayed with metrics that are automatically identified as the root cause and scored in ranked order for quick resolution.

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Fig 6: Key application KPIs and automatically-detected root causes in ranked order.

Learn More

AppDynamics’ Winter ‘17 Release is rich with other important features such as Universal Agent to simplify agent installation and configuration, Enterprise Console for streamlined controller lifecycle management, and Node.js flame graph for deeper visibility, among several other features.

Join us for a webinar on November 16th to get an in-depth look into the latest innovations and features in our Winter ‘17 Release. You can also get started with the free trial of AppDynamics Winter ‘17 today!

A Look Back Before Leaping Forward: How We Got Here and Where We’re Heading

Blackberries, Blockbusters and AS400s ruled supreme back when we were building the company nine years ago. At this time, most phones didn’t have GPS, most shopping was in person and most computing happened in one place. It seemed like much simpler times, right? Yet massive disruption was on the horizon that would completely shake up how people used and thought about technology.

Brace yourself – SaaS is coming. This radical movement proved that abstracting complexity can not only free businesses from fretting over the nuts and bolts of infrastructure, but it can also free them to think bigger about what they can accomplish through technology. This was a huge wake up call for our entire industry, and became my obsession, and our inspiration for AppDynamics.

What if the principles of SaaS were applied to monitoring solutions? What if stripping away complexities of reporting and alerting about applications could free businesses in a similar way?

After many late nights working out how software can be more of a business enabler and less of a management burden, came the invention of our machine-learning powered Business Transaction, the foundation of AppDynamics.

It’s hard enough for businesses to stay on top of the latest trends and shifting needs of consumers, managing applications should come secondary to hitting business goals. So, we engineered our product with business performance as the top priority. By pairing the right business metrics with the noise-cancelling abilities of machine learning, the root cause of business-impacting problems are brought to the forefront and many intricacies of related symptoms are collapsed underneath. As a result, enterprises get a straightforward, dynamic baseline that intelligently evolves with the business. And, for the first time, the world’s most complex systems can transform into real competitive advantages.

As time went on, digital strategies became synonymous with business strategies and consumer expectations rose to a point where “next-day” isn’t fast enough. To keep up with the pace set by titans like Apple, Google and Amazon, enterprises entered uncharted territories in cloud, DevOps and IoT causing new levels of strain on technical teams. On top of that, these developers, IT pros and CIOs were challenged to defend these changes to business colleagues who are asking if it’s worth their time and money.

The evolution of enterprises’ needs have always been the fire for our innovations and today’s announcement is no exception. With systems continuing to sprawl, businesses need a way to make sense of it all – from the depths of networking to the edge of multi-cloud. So we’re widening our scope to capture exactly how devices and the network impact the business. Another side effect of distributed systems are blind spots in customer interactions, which make it harder for CIOs to map customer journeys. To help provide a more complete view, our vision for the next generation of Business iQ is to link various distributed business events for a fuller picture that can boost opportunities to stay competitive in customer experience.

With the unrelenting rush of data coming in from countless sources, we see machine learning as the next big disruptor on the horizon. Machine learning, which sounded like science fiction not too long ago, has reached critical mass in its abilities to spot patterns for predictive analytics and automation. It can also be found in our latest announcement simplifying troubleshooting to a click. With new devices coming out daily, we don’t see data slowing down anytime soon, so expect to see more developments in machine learning from us that will help enterprises achieve the scale and speed needed to take on whatever is next in this on-demand, data-driven world.