What You'll Learn
- Navigate the SageMaker Studio environment and configure ML instances.
- Prepare and process data using SageMaker Data Wrangler and Clarify.
- Build scalable machine learning models using built-in algorithms.
- Train and optimize models using Hyperparameter Tuning and Managed Spot Training.
- Deploy models into production using real-time and batch endpoints.
- Monitor model performance and set up automatic drift detection.
- Implement MLOps best practices using SageMaker Pipelines and Feature Store.
Prerequisites
Essential foundation for this course:
- Fundamental understanding of Cloud Computing (AWS basics).
- Working knowledge of Python and libraries like Pandas and NumPy.
- Basic understanding of the Machine Learning lifecycle.
- Active AWS Free Tier account (guidance for setup will be provided).
Career & Salary Outlook
MLOps Engineer$120,000 - $175,000
AWS Machine Learning Specialist$135,000 - $190,000
Cloud AI Solutions Architect$145,000 - $210,000
Senior Data Engineer$130,000 - $185,000
* Global salary benchmarks for AWS certified ML professionals.
Who Is This For?
Data Scientists
Cloud Engineers
DevOps Professionals
MLOps Aspirants
Solutions Architects