Following content is important: First picture of the article to understand the lifecycle of a AI product and section "Lifecycle Tracking for Data Scientists" to understand what are MLOps steps in the ML project lifecycle
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?
What is MLOps?
ARTICLE 2. MLOps: Continuous delivery and automation pipelines in machine learning
How to implement and automate CI, CD, CT for ML systems?
What are the differences between DevOps and MLOps?
What are the data science steps for ML?
What are the 3 maturity level of MLOps?
REPO 3. MLOps on Azure
How does Azure ML help with MLOps?
What are MLOps Best Practices?
What are MLOps Solutions using Azure?
What are the key challenges Azure with to solve with MLOps?