5 Underlying Principles of a Successful MLOps Maturity Assessment

Artificial intelligence (AI) / machine learning (ML) is one of the most exciting areas in technology these days, with long-promised insights and results joining the mainstream. But while the future for AI / ML is bright, the present isn’t always so rosy. A large majority of projects never produce expected results — and those that do are often hampered by an inability to move beyond rudimentary stages, integrate with existing systems, or scale to enterprise-level operations. In response, the discipline of MLOps has grown to unify AI / ML efforts, develop a framework for sustainable success, and accelerate growth and success.

Unlock the secret to making your AI / ML program a massive success. Click here to access our new MLOps white paper.

A useful tool to initiate an MLOps program is to conduct an assessment that:

  • examines your current state of MLOps maturity, 
  • develops a vision of a desired future state, 
  • and puts the tools, policies, and procedures in place to make it happen.

Simply put, an MLOps maturity assessment takes the possible and turns it into the doable. Read on to learn how to assess your own MLOps program’s effectiveness.

The Five Guiding Principles of an MLOps Assessment

Before beginning an assessment, it’s important to understand the framework of the process. There are five principles that describe the boundaries and objectives of an MLOps assessment, which are as follows:

1. Understand

Too often, AI / ML initiatives are undertaken without a clear understanding of either the true nature of the desired objectives that are being targeted or the suitability of AI / ML to achieve those objectives. While there is a great deal of interest in these disciplines, businesses run the risk of chasing them as the bright, shiny new thing.

The truth is that they are no panacea or wonder tool. It is critically important to be ready to take the time and effort to truly diagnose the underlying challenges that must be addressed and the desired outcomes that are needed. In some cases, AI / ML will not be the best solution.

2. Assess

In many instances, AI / ML efforts have been developed in an ad hoc fashion by disparate and isolated business groups, often leading to a hodgepodge of solutions, software, hardware, data, and more. This “ready, fire, aim” approach may yield positive results in the short run, but it’s highly unlikely that these results will scale — and even less likely that they can be merged into an enterprise solution.

The goal of an assessment is to recognize the good, the bad, and the ugly of current efforts so that both strengths and weaknesses can be recognized. A solid assessment will provide the foundation on which AI / ML success can be built.

3. Recommend

Once business objectives have been identified and an understanding of the status of AI / ML efforts is established, the next step is to move on to crafting a plan for development and refinement. This roadmap will have an eye on short-term needs and contain a vision of long-term goals that are both ambitious and practical.

The short-term objectives should be achievable and as impactful as possible while long-term plans are best when they combine lofty ambition with planned resilience. New insights will be generated along the way, making it possible to adapt plans and accelerate results as time passes.

4. Work

With a plan in place, it will be time to turn to the blocking and tackling of developing a vibrant and healthy AI / ML discipline. This begins with not only identifying the required resources (e.g., people, tools, budget, etc.) but also assessing the ways the current environment might be a hindrance to success.

A few common factors that can be a drag on progress and results include:

  • security practices, 
  • lack of team communication and integration, 
  • misaligned incentives, 
  • and institutional resistance.

For these reasons and more, an effective program will address immediate issues and look at the whole picture to build a pathway to long-term success.

5. Implement

A good assessment process will make it possible to generate action and results as quickly as possible. It is not a rigid, iterative process that has to go from point A to point B to point C, ad infinitum. Efforts will be directed to those areas that produce the greatest short-term impact, all the while maintaining a perspective that keeps sight of larger goals.

Of course, this is easier said than done, but AI / ML is one of those disciplines where it pays to act quickly, fail, and move forward. Lessons learned will spur further buy-in and encourage real-world results. 

MLOps Delivers on the Promise of AI / ML

MLOps is emerging as a critical “need to have” capability for sustained success with AI / ML. The best time to start such an effort is now — and the best time to end is never. After all, it takes a continuous MLOps effort to ensure that results are maintained, insights are maximized, and operations can successfully expand. 

Expert partners are especially important in this emerging field. Maven Wave / Atos has an experienced AI / ML team that can collaborate with your team to perform a comprehensive and effective MLOps assessment.

To learn more about our approach to MLOps, download our recent white paper “Delivering on the Promise of AI / ML: The Emergence of MLOps” or 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.
January 27th, 2023

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