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FreeMLOps Course – Build Machine Learning Production Grade Projects
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The course builds a customer satisfaction project on an Olist customer dataset. It moves from ZenML fundamentals and a blueprint of pipeline steps through data cleaning, model development and evaluation, then into experiment tracking and a deployment pipeline, and ends with a Streamlit app.
Learners who want one project that shows how the pieces of an MLOps workflow connect. Since each step is built in turn, it is easier to follow than a survey of tools, and you can see how the pipeline changes as steps are added.
MLOps, short for Machine Learning Operations, refers to the practice of applying DevOps principles to machine learning. This MLOps course will guide you through an end-to-end MLOps project, covering everything from data ingestion to deployment, using state-of-the-art tools like ZenML, MLflow, and various MLOps libraries.
💻 Code: https://github.com/ayush714/mlops-projects-course/tree/main
✏️ Course created by @AyushSinghSh
⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:01:28) MLOps Fundamentals (introduction to mlops)
⌨️ (0:32:54) Fundamentals of Zenml
⌨️ (0:45:31) Customer Satisfaction project
⌨️ (0:45:51) Dataset of Olist customer dataset
⌨️ (0:49:01) Installing necessary libraries
⌨️ (0:55:01) Creating blueprint of steps
⌨️ (1:08:06) Zenml Dashboard
⌨️ (1:13:46) Implementing steps
⌨️ (1:15:01) Working on data cleaning
⌨️ (1:33:41) Working on model development
⌨️ (1:41:09) Working on evaluation model
⌨️ (1:48:36) Run pipeline
⌨️ (1:54:28) Implementing experiment tracker
⌨️ (2:11:02) Deployment pipeline
⌨️ (2:58:08) Streamlit app
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