Machine Learning Operations (MLOps) Masterclass

1. What is MLOps? 
2. MLOps Motivation
3. MLOps challenges
4. MLOps challenges similar to DevOps
5. MLOps Components
6. Automated ML pipelines vs CI/CD ML pipelines
7. Different Roles involved in MLOps (ML Engineering + Operations)
8. Machine Learning Life Cycle 
9. MLOps Vs DevOps
10. Different tools for MLOps
11. Benefits Of AWS MLOps

1. MLOPs for LLM’s
2. LLM System Design
3. High-level view LLM-driven application
4. LLMOps Pipeline

1. Versioning
2. Testing
3. Automation (CI/CD)
4. Reproducibility
5. Deployment
6. Monitoring

1. Introduction to Amazon Sagemaker 
2. Using Amazon S3 along with Sagemaker [Hands-on] 
3. Built-in algorithms
4. Amazon Elastic Container Service (Amazon ECS)
5. Amazon SageMaker Notebooks and SDK [Hands-on]
6. Notebook instance type, IAM Role & VPC [Hands-on]
7. Build, Train & deploy ML Model using XGBoost [Hands-on]
8. Endpoint & Endpoint configurations [Hands-on]
9. Generate inference from deployed model [Hands-on]

1. AWS CodeCommit [Hands-on]
2. AWS CodePipeline [Hands-on]
3. AWS CodeBuild [Hands-on]

Sagemaker Studio tools

1. Introduction Sagemaker pipelines
2. Containers for Amazon SageMaker
3. AWS MLOps - ML Model Monitoring [Hands-on]
4. AWS MLOps - Amazon Sagemaker Feature Store
5. AWS MLOps - Post-Deployment Challenges

• 10 Hours of Live sessions from Industrial Experts
• 15+ Live Hands-on Labs
• 2 Real-time industrial projects
• One-on-One Debugging with Industry Mentors

• Data Scientists
• Data engineers & Data Analysts
• Research/Applied Scientists
• ML engineers
• DevOps engineers
• Aspiring MLOps Professionals and Enthusiasts
• Machine Learning professionals who want to deploy models to production
• Anyone who wants to learn Docker & Kubernetes, AWS, Azure, GCP, DVC, Feast, MLFlow etc
• Individuals interested in the data and the AI industry

• 10:00 AM - 2:30 PM IST