Explore a comprehensive playlist covering MLOps and related domains, including hands-on sessions with Azure, Kubernetes for data scientists and engineers, TFX pipelines, and end-to-end automated CI/CD pipelines for continuous deployment. Dive into feature stores for machine learning, model monitoring concepts, data drift, load and performance testing, and deployment strategies using containers, Google Cloud Run, and App Engine. Learn about deployment challenges, best practices, and continuous model deployment with GitHub and Kubernetes. Gain insights into Apache Airflow and its applications in MLOps workflows.
Overview
Syllabus
An Introduction to MLOps.
MLOps with Azure - Hands on Session.
Hands on Kubernetes for Data Scientists and Engineers.
MLOps with TFX pipelines - Tensorflow Extended.
MLOps - End to End automated CI/CD pipeline for Continuous Deployment.
Feature Store for Machine Learning - MLOps.
MLOps with Feature Store - Move models from development to production.
Accelerating Machine Learning with a Feature Store.
Model Monitoring - Concept and Data Drift - Part 2.
Model Monitoring Deep Dive.
Machine Learning Models - Load and Performance Testing Demo.
Model Deployment Deep Dive using Containers, Google Cloud Run and App Engine.
Model Deployment Challenges and Best Practices - Webinar for Analytics Vidhya.
MLOps - Machine Learning Deployment with CI/CD pipeline - Part 1.
MLOps - GitHub Kubernetes Continuous Model Deployment - Streamlit - Part 2.
Getting Started with Apache Airflow.
Taught by
AIEngineering
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Reviews
5.0 rating, based on 1 Class Central review
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Pradyumna KarkhaneCourse is very good to get overview of getting started with MLops. The panel was very experienced and with good handon material inorder to understand practical aspect of it.
