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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

Description:

Dive into a comprehensive 2.5-hour video tutorial on building MLOps platforms using Databricks, AutoML, SKLearn, and AWS. Learn to start an MLOps project, set up CI/CD, and invoke ML library code. Explore end-to-end MLOps with Databricks to AWS containers, spin up Databricks clusters, and transition from Pandas to Spark. Create a fake news classifier using Kaggle and AutoML, register models, and set up inference endpoints. Utilize Github CodeSpaces and FastAPI to serve MLFlow models with Swagger documentation. Discover Iguazio's feature store capabilities and leverage AWS Cloud9 for developing containerized ML models. Finally, deploy your containerized model using AWS App Runner. Access source code and additional resources for further learning in cloud computing, data engineering, and MLOps.

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

Pragmatic AI Labs

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#Computer Science #Machine Learning #MLOps #DevOps #Programming #Cloud Computing #Amazon Web Services (AWS) #CI/CD #Data Science #Big Data #Databricks #AutoML #Programming Languages #Python #FastAPI #Machine Learning Model Deployment #MLFlow