The AiEdge+: The Different Maturity Levels in Machine Learning

Going beyond simple model development

In today’s post, we explore the different stages of MLOps growth, using examples from Microsoft and Google to show how the use of automated systems, teamwork, and money can affect the success of Machine Learning projects within a company. It also talks about when it's best to use Kubernetes to launch Machine Learning models, encouraging careful use of technology based on the number of users and the complexity of the team, and offering a well-rounded view for good MLOps habits. We cover:

• The MLOps levels of maturity

• When to use Kubernetes for Machine Learning

• Learn more about Maturity Levels in Machine Learning with articles and Youtube videos

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The MLOps levels of maturity

There are 3 ingredients that pretty much guarantee the failure of any Machine Learning project: having the Data Scientists training models in notebooks, having the data teams siloed, and having no DevOps for the ML applications! Interestingly enough, that is where most companies trying out machine learning get stuck. The level of investment in ML infrastructures for companies is directly proportional to the level of impact they expect ML to have on the business. And the level of impact is in turn proportional to the level of investment. It is a vicious circle! 

Both Microsoft and Google established standards for MLOps maturity that capture the degree of automation of ML practices, and there is a lot to learn from those: 

• Microsoft - Machine Learning operations maturity model  

• Google - MLOps: Continuous delivery and automation pipelines in machine learning

Level 0 is the stage without any automation. Typically the Data Scientists (or ML engineers depending on the company) are completely disconnected from the other data teams. That is the guaranteed failure stage! It is possible for companies to pass through that stage to explore some ML opportunities, but if they stay stuck at that stage, ML is never going to contribute to the revenue of the company.

The level 1 is when there is a sense that ML applications are software applications. As a consequence, basic DevOps principles are applied at the software level in production but there is a failure to realize the specificity of ML operations. In development, data pipelines are better established to streamline manual model development.