MLOps: Continuous delivery and automation pipelines in machine learning - Asset | <RecipeText />: What Is MLOps?

Covers: implementation of MLOps

Estimated time needed to finish: 40 minutes

Main sections: MLOps level 0/1/2 to understand what is each maturity level of MLOps defined by Google

Level 0: manual process where all the steps are manually defined and maintained, which makes changes very difficult to test as well as monitor. THe development and production enviroments are not separated so that very difficult to provide satisfied product services.
Level 1: ML pipeline automation where it separates the development and production enviroment. In this level you can continously deliver model. However, process are still manully.
Level 2: CI/CD pipeline automation where you can update your ML pipeline rapidly as well as reliably with all the steps automated together.

Google Cloud

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Recipe

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Total time needed: ~3 hours

Objectives: For me, a successful ML app is not only about the accuracy of the Model, but also the infrastructure around the model to make the model a scalable, stable application. This is where MLOps becomes important, to apply DevOps best practices into ML project.
Potential Use Cases: For the ones that are interested in MLOps or want to make your ML product more robust.
Who is This For ?: BEGINNERPeople interested in MLOps

Click on each of the following annotated items to see details.

ARTICLE 1. What Is MLOps?

20 minutes

ARTICLE 2. MLOps: Continuous delivery and automation pipelines in machine learning

40 minutes

REPO 3. MLOps on Azure

60 minutes

ARTICLE 4. Machine Learning Operations

30 minutes

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