AWS Sagemaker

AWS Sagemaker

This exam validates your ability to design, develop, and manage complete machine learning solutions using Amazon SageMaker, a powerful managed service for scalable AI on AWS. You’ll gain practical experience in creating automated ML workflows, training models using custom and built-in algorithms, and deploying them to real-time and batch endpoints. The certification also tests your skills in integrating SageMaker with other AWS services for security, monitoring, automation, and governance. Whether you're a data scientist optimizing performance or a DevOps professional orchestrating ML pipelines, this exam equips you to lead cloud-native AI initiatives from experimentation to production-grade deployment.

Skills Required

• Understanding of machine learning lifecycle
• Familiarity with Python, Jupyter notebooks, and ML libraries
• Basic to intermediate experience with AWS services (S3, IAM, Lambda)
• Knowledge of data processing, training, and evaluation

Who should take the Exam?

This exam is ideal for:

• Machine learning engineers and data scientists using AWS tools
• AWS solution architects working with ML workflows
• AI professionals deploying models at scale in the cloud
• Software developers integrating SageMaker with applications
• DevOps engineers managing MLOps pipelines on AWS

Course Outline

• Introduction to SageMaker and AWS ML Services
• Data Preparation and Feature Engineering
• Model Training and Tuning
• Model Deployment and Inference
• Model Monitoring and Drift Detection
• MLOps and Automation with Pipelines
• Security, Governance, and Cost Optimization
• Advanced Use Cases and Integrations