Mastering MLOps

 Introduction to MLOps

– Overview of MLOps: Definition, Importance, and Industry Applications

– Key Concepts: Model Versioning and Monitoring

– The MLOps Lifecycle: From Data Ingestion to Model Deployment

 Data Engineering for MLOps

– Efficient Data Management and Pipelines with Python

– SQL Techniques for Handling Large-Scale Data

– Automating Data Workflows with Apache Airflow

– Case Study: Building a Data Pipeline in AWS

 AWS for MLOps

– Overview of AWS Services for Machine Learning (SageMaker, Lambda, Step Functions)

– Deploying Machine Learning Models with AWS SageMaker

– Automating Model Deployment with AWS Lambda and Step Functions

– Scaling and Monitoring with AWS CloudWatch and CloudFormation

– Hands-On Lab: Deploying a Simple Model to AWS SageMaker and Automating the Process

Version Control and Collaboration (Git)

– Best Practices for Using Git in MLOps Projects

– Managing Code and Model Versioning

– Collaborative Workflows in MLOps with GitHub/GitLab

Model Deployment and Automation

– Dockerizing ML Models for Scalable Deployments

– Automating Model Retraining and Deployment with AWS