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Using AWS Cloud9 to develop containerized ML Models
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Classroom Contents
MLOps Platforms From Zero - Databricks, MLFlow-MLRun-SKLearn
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- 1 Intro
- 2 Starting an MLOps project
- 3 Setup CI/CD
- 4 Invoke ML Library Code
- 5 End to End MLOps with Databricks to AWS Containers Diagram
- 6 Spinning up Databricks Cluster
- 7 Doing Pandas to Spark
- 8 Creating Fake News Classifier using Kaggle and AutoML
- 9 Creating Databricks AutoML Experiment
- 10 Viewing Databricks AutoML Experiment notebook
- 11 Registering models with Databricks
- 12 Setting up Inference endpoint with the Databricks platform
- 13 Using Github CodeSpaces to serve out downloaded Databricks model with MLFlow
- 14 Using FastAPI to serve Swagger documentation of MLFlow model
- 15 Feature Store Capabilities of Iguazio
- 16 Using AWS Cloud9 to develop containerized ML Models
- 17 Using AWS App Runner to serve out containerized model
