Driving Efficiency with MLOps

Driving Efficiency with MLOps

The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. However, as is always the case with new technology, machine learning comes with its own set of challenges:

A typical ML model lifecycle

Here’s what a machine learning model lifecycle looks like: 

MLOps & Microsoft Azure
 

What is MLOps

According to Wikipedia, “MLOps (‘Machine Learning’ + ‘Operations’) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.”

How is MLOps different from DevOps

So, is MLOps just another fancy name for DevOps? Since machine learning is also a software system, most of the DevOps practices apply to MLOps too. However, there are some important differences:

Implementing MLOps with Azure Machine Learning

Tavant’s Manufacturing Analytics Platform (TMAP) is an analytics and machine learning-based platform that provides important business insights to our customers in the manufacturing domain especially Warranty. It is based on Azure and we use Azure Machine Learning’s MLOps features for managing our models’ lifecycle. Here’s a list of features that Azure provides for MLOps:

Conclusion

MLOps is a must for enterprises using machine learning at scale. It allows for managing the complete model lifecycle including model governance and should be made mandatory for all Machine Learning projects. Azure Machine Learning provides has a great feature set for implementing MLOps. It does lack some of the advanced features like model lineage but one can always use dedicated MLOps platforms like MLflow or DotScience on Azure to bring in any missing features. 

Original source https://www.tavant.com/blog/driving-efficiency-mlops-microsoft-azure

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