Make Machine Learning Work for You

The enthusiasm for AI and its applications is reaching a nadir, according to an August 2023 Gartner Hype Cycle press release, where generative AI is nearly perched atop the category of technologies at their “Peak of Inflated Expectations,” ready to plunge into the “Trough of Disillusionment.” A quick look at social media agrees, with some pages filled with targeted advertisements about topics as prosaic as “GPT for your pile of receipts.” This is good evidence that the AI craze is becoming a hammer looking for a nail.

Yet, with all this fervor, according to McKinsey, while AI adoption has more than doubled since 2017, it has leveled off at around 50% to 60% during the past few years.

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IBM reveals that nearly half of the challenges related to AI adoption focus on data complexity (24%) and difficulty integrating and scaling projects (24%). While it may be expedient for marketers to “slap a GPT suffix on it and call it AI,” businesses striving to truly implement and incorporate AI and ML face a two-headed challenge: first, it’s difficult and expensive, and second, because it’s difficult and expensive, it’s hard to come by the “sandboxes” that are necessary to enable experimentation and prove “green shoots” of value that would warrant further investment. In short, AI and ML are inaccessible.

Data, data, everywhere

History shows that most business shifts at first seem difficult and expensive. However, spending time and resources on these efforts has paid off for the innovators. Businesses identify new assets, and use new processes to achieve new goals—sometimes lofty, unexpected ones. The asset at the focus of the AI craze is data.

The world is exploding with data. According to a 2020 report by Seagate and IDC, during the next two years, enterprise data is projected to increase at a 42.2% annual growth rate. And yet, only 32% of that data is currently being put to work.

Effective data management—storing, labeling, cataloging, securing, connecting, and making queryable—has no shortage of challenges. Once those challenges are overcome, businesses will need to identify users not only technically proficient enough to access and leverage that data, but also able to do so in a comprehensive manner.

Businesses today find themselves tasking garden-variety analysts with targeted, hypothesis-driven work. The shorthand is encapsulated in a common refrain: “I usually have analysts pull down a subset of the data and run pivot tables on it.”

To avoid tunnel vision and use data more comprehensively, this hypothesis-driven analysis is supplemented with business intelligence (BI), where data at scale is finessed into reports, dashboards, and visualizations. But even then, the dizzying scale of charts and graphs requires the person reviewing them to have a strong sense of what matters and what to look for—again, to be hypothesis-driven—in order to make sense of the world. Human beings simply cannot otherwise handle the cognitive overload.

The moment is opportune for AI and ML. Ideally, that would mean plentiful teams of data scientists, data engineers, and ML engineers that can deliver such solutions, at a price that folds neatly into IT budgets. Also ideally, businesses are ready with the right amount of technology; GPUs, compute, and orchestration infrastructure to build and deploy AI and ML solutions at scale. But much like the business revolutions of days past, this isn’t the case.

Inaccessible solutions

The marketplace is offering a proliferation of solutions based on two approaches: adding even more intelligence and insights to existing BI tools; and making it increasingly easier to develop and deploy ML solutions, in the growing field of ML operations, or MLOps.

BI is making significant inroads on augmenting its capabilities with ML, but still has the intrinsic cognitive overload challenge to overcome. ML capabilities are so embedded in BI interfaces that they aren’t easily extracted to be applied in more bespoke ways.

MLOps comes from the other direction, by easing the development and promotion of ML models. The challenge for MLOps is, while it makes data scientists and ML engineers more productive—more building and training models, and less wrangling data, deploying, and productionizing—it doesn’t address the

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By: David Kang
Title: Make Machine Learning Work for You
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Published Date: Wed, 20 Sep 2023 16:00:00 +0000


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