Solving enterprise machine learning's 5 main challenges

At Algorithmia, we have learned how enterprise machine learning is developing across industries, and we are most interested in the challenges that hinder organizations from extracting ML value.

In late 2019, we published data on the main challenges, which we consolidated in a report called the “2020 State of Enterprise Machine Learning.” The findings from the report informed the challenges we discuss in this ebook. Among the most common were scale, model versioning and reproducibility, and obtaining organizational alignment on ML goals. There are several that repeated across industries, company size, and job role.

Five challenges of machine learning

5 challenges of enterprise machine learning

Within the ebook, we unpack the challenges into more manageable components to solve, providing questions your organization should ask and answer before proceeding with any ML project, and we provide a customer success story for each one. Removing these pain points from your organization’s ML roadmap will position you toward achieving machine learning value much faster.

Technology mismatch, a deep dive

One of the challenges is about the technology mismatch that can arise when several job roles begin working together on novel machine learning applications. Enterprise machine learning comprises several teams: data science, DevOps, IT, software development, and senior leadership. Because each team has different operational methods, KPIs, and success measures, machine learning programs at scale can become muddled in conflicting goals.

The ebook describes this challenge in detail, providing a chart to showcase the difference between flexible tooling and teams, and limiting, “lock-in” practices. “There’s a difference between doing what’s best for your company’s machine learning program and what’s best for operational production, and there is no standard for every problem that operations can anticipate,” (Algorithmia, 2020).

Overcoming challenges, setting realistic goals

Once these challenges have been met and your teams have aligned on goals, your organization can set KPIs to ensure you stay on track of your ML development. During that process, your company will likely need to make a decision about how you’ll proceed with ML management and operations. Will you build a system internally or opt to buy an off-the-shelf solution to save time?

It’s imperative to remember that time wasted up front will not be regained down the road, so extracting value from ML as soon as possible to maintain a competitive advantage in your industry is paramount.

Who is this ebook for?

This ebook is an amalgamation of our “5 missteps of machine learning every operations manager can avoid” webinar as well as the insights we gleaned from our interactive page for the 2020 state of enterprise machine learning. Both pieces lead into our upcoming whitepaper entitled, “Building versus buying an ML management platform,” which will publish on 1 June (follow that link for early sign-up).

We intend this ebook to be accessible to a wide audience, but the following groups will find it most helpful:

– Business leaders banking on machine learning for competitive advantage

– DevOps and IT engineers tasked with machine learning operational tasks

– Data science teams and their leadership who want their work deployed

We understand that there are a lot of questions surrounding enterprise machine learning and planning for business longevity in uncertain times. We hope this ebook will initiate many discussions about future-proofing companies and making smart ML–minded decisions. We look forward to those conversations and are a trusted resource for companies just starting to plan ML journeys.

Download the ebook here


Whitney Garrett