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Machine learning for enterprise use is exploding. From improving customer experience to developing products, there's almost no area of the modern business untouched by machine learning.

Machine learning is a pathway to creating artificial intelligence, which in turn is one of the primary drivers of machine learning use in the enterprise. There is some disagreement over the exact nature of the relationship between AI and machine learning. Some see machine learning as a subfield of AI, while others view AI essentially as a subfield of machine learning. In general, AI aims to replicate some aspect of human perception or decision-making, whereas machine learning can be used to enhance or automate virtually any task, not just ones related to human cognition. However you view them, the two concepts are closely linked, and they are feeding off each other's popularity.

The practice of machine learning involves taking data, examining it for patterns and developing some sort of prediction about future outcomes. By feeding an algorithm more data over time, data scientists can sharpen the machine learning model's predictions. From this basic concept, a number of different types of machine learning have developed:

From these four main types of machine learning, enterprises have developed an impressive array of techniques and applications. Everything from relatively simple sales forecasting to today's most cutting-edge AI tools run on machine learning models. This guide to machine learning in the enterprise explores the variety of use cases for machine learning, the challenges to adoption, how to implement machine learning technologies and much more.

Machine learning for enterprise use is accelerating, and not just at the periphery. Increasingly, businesses are putting machine learning applications at the center of their business models. The technology has enabled businesses to perform tasks at a scale previously unachievable, not only generating efficiencies for companies but also new business opportunities, as technology writer Mary Pratt explained in "10 common uses for machine learning in business." The growing use of machine learning in mission-critical business processes is reflected in the range of use cases where it plays an integral role. The following are examples:

These are just some examples, but there are countless more. Any business process that either produces or uses large amounts of data -- particularly structured, labeled data -- is ripe for automation that uses machine learning. Enterprises across all industries have learned this and are working to implement machine learning methods throughout their processes.

It's not hard to see why machine learning has entered so many situations. Enterprises that have adopted machine learning are solving business problems and reaping value from this AI technique. Here are six business benefits:

The question is no longer whether to use machine learning, it's how to operationalize machine learning in ways that return optimal results. That's where things get tricky.

Machine learning is a complicated technology that requires substantial expertise. Unlike some other technology domains, where software is mostly plug and play, machine learning forces the user to think about why they are using it, who is building the tools, what their assumptions are and how the technology is being applied. There are few other technologies that have so many potential points of failure.

The wrong use case is the downfall of many machine learning applications. Sometimes enterprises lead with the technology, looking for ways to implement machine learning, rather than allowing the problem to dictate the solution. When machine learning is shoehorned into a use case, it often fails to deliver results.

The wrong data dooms machine learning models faster than anything. Data is the lifeblood of machine learning. Models only know what they've been shown, so when the data they train on is inaccurate, unorganized or biased in some way, the model's output will be faulty.

Bias frequently hampers machine learning implementations. The many types of bias that can undermine machine implementations generally fall into the two categories. One type happens when data collected to train the algorithm simply doesn't reflect the real world. The data set is inaccurate, incomplete or not diverse enough. Another type of bias stems from the methods used to sample, aggregate, filter and enhance that data. In both cases, the errors can stem from the biases of the data scientists overseeing the training and result in models that are inaccurate and, worse, unfairly affect specific populations of people. In his article "6 ways to reduce different types of bias in machine learning," analyst Ron Schmelzer explained the types of biases that can derail machine learning projects and how to mitigate them.

Black box functionality is one reason why bias is so prevalent in machine learning. Many types of machine learning algorithms -- particularly unsupervised algorithms -- operate in ways that are opaque, or as a "black box," to the developer. A data scientist feeds the algorithm data, the algorithm makes observations of correlations and then produces some sort of output based on these observations. But most models can't explain to the data scientist why they produce the outputs they do. This makes it extremely difficult to detect instances of bias or other failures of the model.

Technical complexity is one of the biggest challenges to enterprise use of machine learning. The basic concept of feeding training data to an algorithm and letting it learn the characteristics of the data set may sound simple enough. But there is a lot of technical complexity under the hood. Algorithms are built around advanced mathematical concepts, and the code that algorithms run on can be difficult to learn. Not all businesses have the technical expertise in house needed to develop effective machine learning applications.

Lack of generalizability prevents machine learning from scaling to new use cases in most enterprises. Machine learning applications only know what they've been explicitly trained on. This means a model can't take something it learned about one area and apply it to another, the way a human would be able to. Algorithms need to be trained from scratch for every new use case.

To learn more about machine learning, here is a list of nine books ranging from a concise introduction for beginners to advanced texts on cutting-edge techniques by AI's leading experts.

Implementing machine learning is a multistep process requiring input from many types of experts. Here is an outline of the process in six steps.

The management and maintenance of machine learning applications in the enterprise is one area that's sometimes given short shrift, but it can be what makes or breaks use cases.

The basic functionality of machine learning depends on models learning trends -- such as customer behavior, stock performance and inventory demand -- and projecting them to the future to inform decisions. However, underlying trends are constantly shifting, sometimes slightly, sometimes substantially. This is called concept drift, and if data scientists don't account for it in their models, the model's projections will eventually be off base.

The way to correct for this is to never view models in production as finished. They demand a constant state of verification, retraining and reworking to ensure they continue to deliver results.

Machine learning operations, or MLOps, is an emerging concept aimed at actively managing this lifecycle. Rather than an ad hoc approach to verifying and retraining when appropriate, MLOps tools put each model on a schedule for development, deployment, verification and retraining. It seeks to standardize these processes, a practice that's becoming more important as enterprises make machine learning a core component of their operations.

When we look to the future of machine learning, one overarching trend predominates. Enterprise adoption will continue to increase, bringing the technology from cutting edge to mainstream.

The trend is already well underway.

A 2019 survey from analyst firm Gartner found that 37% of enterprises have adopted some form of artificial intelligence. That's up from 10% in 2015. At its current trajectory, machine learning is on a path to become a ubiquitous technology in the next few years. In its ranking of the top 10 data and analytics trends for 2020, the analyst firm named "smarter, faster and more responsible AI" as the year's top trend. The report, noting the vital importance of machine learning and other AI techniques in providing insight into the global coronavirus pandemic, predicted that by 2024, 75% of organizations will have shifted from piloting to operationalizing AI. As a result of high rates of adoption of machine learning in the enterprise, the market for machine learning tools is growing rapidly. The analyst firm Research and Markets predicted that the machine learning market will grow to $8.8 billion by 2022, from $1.4 billion in 2017.

The reasons for this are clear. Today's most successful companies, like Amazon, Google and Uber, put machine learning applications at the center of their business models. Rather than viewing machine learning as a nice-to-have technology, industry-leading enterprises are using machine learning and AI technologies as critical to maintaining their competitive edge, as technology writer George Lawton explored in "Learn the business value of AI's various techniques."

Advances in deep learning -- a type of machine learning based on neural networks -- have played a huge role in bringing AI to the fore in the enterprise. Neural networks are relatively common in enterprise applications today. These advanced deep learning techniques enable models to do everything from recognize objects in images to create natural language text for product descriptions and other applications. Today, there are a number of different types of neural networks, which are designed to perform specific jobs. As technology writer David Petersson explained in "CNNs vs. RNNs: How they differ and where they overlap," understanding the uniqueness of different types of algorithms is key to getting the most out of them.

It is now viewed as inevitable that a large amount of knowledge work will be automated. Even some creative fields are being infiltrated by machine learning-driven AI applications. This is raising questions about the future of work. In a world where machines are able to manage customer relations, detect cancer in medical images, conduct legal reviews, drive shipping containers across the country and produce creative assets, what is the role of human workers? Proponents of AI say automation will free people up to pursue more creative activities by eliminating rote tasks. But others worry that an incessant drive for automation will leave little room for human workers.

Enterprises looking to deploy machine learning have no shortage of options. The machine learning space features strong competition between open source tools and software built and supported by traditional vendors. Regardless of whether an enterprise chooses machine learning software from a vendor or open source tool, it is common for applications to be hosted in the cloud computing environments and delivered as a service. There are more vendors and platforms than one article could name, but the following list gives a high-level overview of offerings from some of the bigger players in the field.

A more exhaustive list of vendor offerings can be found in this expert overview of machine learning platforms.

In general, most enterprise machine learning users consider open source tools to be more innovative and powerful. However, there is still a strong case for proprietary tools, as vendors offer training and support that is generally absent from open source offerings. Many of today's vendor tools support use of open source libraries, allowing users to have the best of both worlds.

Read more from the original source:
In-Depth Guide to Machine Learning in the Enterprise