MLOps Platforms From Zero - Databricks, MLFlow-MLRun-SKLearn

This course demonstrates an end-to-end MLOps workflow using Databricks, MLflow, scikit-learn, FastAPI, and AWS. It covers AutoML experiments, model registration and inference endpoints, cloud development environments, and containerized model serving.

Syllabus

Intro
Starting an MLOps project
Setup CI/CD
Invoke ML Library Code
End to End MLOps with Databricks to AWS Containers Diagram
Spinning up Databricks Cluster
Doing Pandas to Spark
Creating Fake News Classifier using Kaggle and AutoML
Creating Databricks AutoML Experiment
Viewing Databricks AutoML Experiment notebook
Registering models with Databricks
Setting up Inference endpoint with the Databricks platform
Using Github CodeSpaces to serve out downloaded Databricks model with MLFlow
Using FastAPI to serve Swagger documentation of MLFlow model
Feature Store Capabilities of Iguazio
Using AWS Cloud9 to develop containerized ML Models
Using AWS App Runner to serve out containerized model