Amazon SageMaker

Module 1: Introduction to SageMaker and Machine Learning

• Overview of SageMaker and its ecosystem
• Cloud ML workflow fundamentals
• Use cases for managed machine learning

Module 2: Data Preparation and Processing

• Data sourcing and cleansing techniques
• Using SageMaker Processing jobs
• Feature engineering and transformation

Module 3: Model Building and Training

• Using built-in algorithms and custom models
• Jupyter Notebooks and SageMaker Studio
• Distributed, incremental, and automated model training

Module 4: Model Tuning and Debugging

• Hyperparameter tuning with SageMaker
• Model debugging tools and techniques
• Best practices for robust model development

Module 5: Deployment and Inference

• Deploying models as real-time endpoints
• Batch transforms for large-scale inference
• Monitoring model performance after deployment

Module 6: Security and Cost Optimization

• IAM roles, authentication, and resource policies
• Encryption and secure data management
• Cost optimization with managed spot training

Module 7: Automation and MLOps

• Creating automated ML pipelines (SageMaker Pipelines)
• Continuous integration and deployment (CI/CD)
• Monitoring model quality and drift

Module 8: Advanced Topics and Integrations

• Using SageMaker JumpStart and foundation models
• Best practices for distributed training
• Integration with other AWS AI services

Module 9: Capstone Project

• Building and deploying an end-to-end ML solution

Experience in the Industry Gain knowledge from experts with practical Amazon SageMaker project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.