How Can MLflow Add Value To Machine Learning Lifecycle And Model Management

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One of the major concerns around machine learning is deploying it. Running a large number of deployment tools and environments, and migrating a model to a production environment can be extremely challenging. 

There are countless independent tools from data preparation to model training, and software tools that cover every stage of the machine learning life cycle. Machine learning developers need to use and deploy dozens of libraries while in a production environment. There is no standard way to migrate models from any library to any of these tools, so that every time a new deployment is made, new risks are created.

What Are The Challenges With ML Workflow?

The experimental results are difficult to reproduce. Algorithm scripts are difficult to run repeatedly for many reasons, such as code version, past parameters, and operating environment. Without detailed tracking, the team often encounters difficulties in using the same code to achieve the same effect. 

Whether you are a data scientist delivering training code to engineers for production or rolling back to the code to fix a bug, the steps to reproduce the machine learning workflow are critical. We have heard many horror stories, such as the model performance of the production environment …

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