Accelerating Business Results from Machine Learning

That’s a question we get a lot from our customers. They understand the promise of ML, but actually realizing the full benefits of the technology—and getting a solid ROI—often remains elusive to them.

To help increase the value of ML initiatives for companies of all sizes, we’ve put together a whitepaper in partnership with Dell EMC and NVIDIA on how the idea of DevOps—a collaboration between development and operations teams to greatly accelerate product development—can be tweaked and applied to ML.

This whitepaper, titled “Accelerating Business Results from Machine Learning,” breaks down the growing practice of MLOps—what it is, how to adopt it, and how it can speed up your ML operations from prototype to production.

Here’s a taste of what you’ll find in the whitepaper:

Business leaders are sold on the promise of leveraging ML, but for most organizations, there is a gulf between the creative efforts of ML specialists and the practical realities of IT operations. The situation is analogous to the early days of DevOps.

More than a decade ago, the adoption of agile software development methodologies made it possible to break up software coding into smaller increments and fostered collaboration between previously siloed teams responsible for different phases of the development life cycle. The next innovation focused on breaking down the silos between IT operations and software development teams to create DevOps teams able to execute continuous integration and delivery so that fixes, enhancements, and new features could be implemented on software already in production, rather than the massive, time-consuming upgrades that characterize traditional development and project management methodologies.

To fulfill its promise, ML must emulate the DevOps production life cycle by fostering collaboration between ML specialists and the operations teams responsible for implementing the production phase of projects.

The whitepaper includes examples of how best to apply MLOps disciplines to ML development life cycles, as well as a detailed analysis of the reference architecture that allows for the coordination necessary to make MLOps successful.

This architecture was conceived by Redapt, EMC Dell, and NVIDIA to provide a solid and proven baseline, including infrastructure automation, resource scheduling, and ML workload orchestration.

If your organization is struggling to fully realize the benefits of ML, or you’re thinking about dipping your toes into the ML adoption process, this white paper is for you.

You can download your free copy of the whitepaper here.