This course is an introduction to MLOps, which focuses on automating the process to move the machine learning models from development to the production phase. It dives deep into topics such as TFX pipelines, Kubernetes, feature stores, model monitoring, model deployment with Containers, GCP Run and App Engine and also CI/CD pipeline. It uses hands-on labs to give participants a better understanding of how to use them for real-world scenarios. It can also guide participants through setting up Apache Airflow. Overall, this course provides an excellent deep dive into the MLOps pipeline and the many tools available to deploy machine learning models with higher accuracy and efficiency.
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.
