Lilly Tech Systems
Learn AWS SageMaker
Master machine learning on AWS with Amazon SageMaker. From notebooks and training to deployment and MLOps, learn to build, train, and deploy ML models at scale on the world's most comprehensive cloud platform.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is SageMaker? Understand the AWS ML ecosystem, SageMaker's role, and how it simplifies the ML lifecycle.
2. Setup
AWS account setup, IAM configuration, SageMaker Studio, domain creation, and understanding pricing.
3. Notebooks
SageMaker notebooks, Studio notebooks, instance types, lifecycle configurations, and data exploration.
4. Training
Built-in algorithms, custom training jobs, distributed training, hyperparameter tuning, and spot instances.
5. Deployment
Real-time endpoints, serverless inference, batch transform, auto-scaling, and multi-model endpoints.
6. MLOps
SageMaker Pipelines, Model Registry, Model Monitor, Feature Store, and CI/CD for ML workflows.
7. Best Practices
Cost optimization, security, performance tuning, architecture patterns, and production-readiness guidelines.
What You'll Learn
By the end of this course, you'll be able to:
Build ML Models
Train machine learning models using SageMaker's built-in algorithms and custom training scripts on managed infrastructure.
Deploy at Scale
Deploy models as real-time endpoints, serverless functions, or batch processing jobs with auto-scaling.
Automate MLOps
Build automated ML pipelines with model versioning, monitoring, and continuous training workflows.
Optimize Costs
Use spot instances, right-size resources, and implement cost-effective architectures for ML workloads.