If you are a Machine Learning engineer (or similar), you probably know the struggles of developing the best machine learning service or platform that meets the requirements of both your data science and software engineering teams. Maybe you have probably even heard of MLflow before but never actually used it. If that is the case, this series is right for you!
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How to implement MLflow in your current model, step by step and the easiest way possible!
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How to keep track of your models, parameters, metrics and code;
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How to containerize your projects and models;
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How to register and deploy your models;
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How to use MLflow UI.
-> YouTube Series Intro: Why bother to start using MLflow?
-> Part 1: Easy steps to start using MLflow tracking in your current model
-> Part 2: How to use MLflow module to containerize your projects and models
-> Part 3: How to Register and Deploy MLflow models
-> Part 4: How to Register and Deploy MLflow models locally
-> Part 6: MLflow UI 101
https://www.mlflow.org/docs/latest/index.html
Jules S. Damji from Databricks: