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6 Crucial Considerations for MLOps Success

Interest in AI / ML is exploding, but these new techniques and technologies present some unique challenges that can result in suboptimal results if not addressed correctly. Dysfunctional AI / ML efforts can be characterized by high costs, an inability to scale, and slow or unnecessarily limited outcomes — but it doesn’t have to be that way.

Are your machine learning efforts proving their worth? Unlock the secret to a successful AI / ML program in our recent MLOps white paper.

In a recent webinar, MLOps in Action: Real-World Examples for Establishing Best Practices, the Maven Wave / Atos team delivered a comprehensive look at how to diagnose problems and improve on the AI / ML efforts by focusing on ten facets in an MLOps assessment. During the discussion, six takeaways emerged that illuminate what to expect from an MLOps approach and how to best proceed. Learn more about these critical MLOps considerations below.

1. Ask yourself if this is the best tool for the job.

A common problem with any new technology is the wishful thinking that it will be a panacea for whatever challenges the enterprise faces. It’s important to first understand what business objectives are, assess current capabilities, and then determine the next (and best) steps. AI / ML isn’t always the best approach, and there is nuance in implementation even when it is.

As a side note, Maven Wave / Atos has curated over 200 tools for MLOps. In fact, some of them may even sound familiar, such as Kubeflow.

2. Understand how results were achieved.

AI / ML are powerful tools, but they must be deployed with care. It is far too easy for programs to become so complex and opaque that the way results are obtained is unknown. Governance and explainability must be maintained — particularly in fields like financial services and healthcare — and are especially important as operations scale up.

3. Follow the (modified) Silicon Valley mantra.

Start-ups often operate under the banner of “move fast / break things”, but this approach doesn’t work well for an existing enterprise. Instead, the approach should be to “learn quickly / fail fast”. The idea is to make the most of the unique insights AI / ML deliver by being ready to incorporate them quickly and then moving on to try something new from what was just revealed. 

4. Don’t wait around for the “best time”.

The old saying goes that the best time to plant a tree was 20 years ago and the second best time is right now. It’s never too late to get started with AI / ML and paralysis by analysis should be avoided at all costs. In fact, case studies cited in our webinar included companies that are thought leaders in AI / ML but are working hard to improve their MLOps results. 

5. Look inside and outside.

It is neither practical nor even possible to go it alone when it comes to developing AI / ML into full-blown MLOps excellence, if only for the reason that there is a mismatch between demand and supply for talent and experience. The objective should be to enable internal capabilities and growth while making measured use of external resources in the forms of open-source technologies, relevant vendors, and outstanding experts.

6. Deliver short and think long.

When it comes to new technologies, COOs and CFOs often have fits because costs go way up and results are slow to materialize. This is even more true when it comes to AI / ML. To counter this, it’s a good idea to identify opportunities for short-term gains and celebrate them when they’re achieved. At the same time, cultivate a 5-to-10-year outlook that takes an informed view of what will be possible down the road.

How To Succeed With MLOps

The demand for AI / ML is easy to understand because it promises to deliver outstanding insights that drive innovation. However, these are complex technologies that are uniquely different from what came before and should be handled accordingly. Developing robust MLOps capabilities will ensure that results will be both maximized and optimized over the long term.

To learn more about our approach to MLOps, watch the replay of our latest webinar  MLOps in Action: Real-World Examples for Establishing Best Practices.

Maven Wave / Atos drives the future of industry with innovative business outcomes, fueled by cloud, with risk top of mind. To help organizations maximize economic outcomes and advancements, we bring a rich blend of industry-specific technological expertise, agile-integrated design, and best practices for transformation. Contact us to learn more.

About the Author

Brian Ray
Brian Ray is a Managing Director and Data Science ML/AI Horizontal Practice Lead for Maven Wave Partners. Mr. Ray is heading the group’s mission of solving complex analytical problems for major businesses worldwide through the power of Data Science with enablement in the cloud. Prior to Maven Wave, Brian has 20 years of hands-on experience in Engineering around the Sciences. A big picture strategist, team builder, and influential top technologist, he has extensive expertise in agile delivery of Data Science — from ideation/discovery, feasibility, exploration, modeling, to engineering and architecture, to hands-on integration and deployment of best-in-class solutions.
February 7th, 2023

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