Microsoft launches MLOps v2

MLOps v2 will allow AI professionals to deploy an end-to-end standardised and unified Machine Learning lifecycle scalable across multiple workspaces.
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Microsoft recently announced the Beta #2 version of MLOps v2 to simplify your MLOps workstream with a unified solution accelerators available on GitHub repository. The full release is targeted for July 2022.

Check out the GitHub repository here.


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MLOps v2 will allow AI professionals to deploy an end-to-end standardised and unified Machine Learning lifecycle scalable across multiple workspaces. By abstracting agnostic infrastructure in an outer loop, the customer can focus on the inner loop development of their use cases. MLOps v2 is a set of workflows that intends to serve as the initial point for MLOps implementation in Azure.

MLOps v2 provides a templatized approach for the end-to-end Data Science process and focuses on driving efficiency at each stage. Currently, the general customer struggle is standing up an end-to-end MLOps engine due to resource, time, and skill constraints. 

One main issue is that it often takes a significant amount of time to bootstrap a new Data Science project. The MLOps v2 provides templates that can be reused to establish a “Cookie-Cutter-Approach” for the bootstrapping process to shorten the process from days to hours or minutes. The bootstrapping process encapsulates key MLOps decisions such as the components of the repository, the structure of the repository, the link between model development and model deployment, and technology choices for each phase of the Data Science process. 

The MLOps v2 architectural pattern is made up of four modular elements representing phases of the MLOps lifecycle for a given data science scenario, the relationships and process flow between those elements, and the personas associated with ownership of those elements. 

Solution accelerator

The solution accelerator provides a modular end-to-end approach for MLOps in Azure based on pattern architectures. As each organization is unique, solutions will often need to be customized to fit the organization’s needs.

The solution accelerator goals are:

  • Simplicity
  • Modularity 
  • Repeatability 
  • Collaboration 
  • Enterprise readiness 

(Source: Microsoft)

MLOps v2 is the de-facto MLOps solution for Microsoft on forward. Aligned with the development of Azure Machine Learning v2, MLOps v2 gives you and your customer the flexibility, security, modularity, ease-of-use, and scalability to go fast to product with your AI. MLOps v2 not just unifies Machine Learning Operations at Microsoft, even more, it sets innovative new standards to any AI workload.

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