Unlock the power of cloud-based machine learning with Amazon SageMaker. Set up your AWS environment, upload data to S3, and launch scalable training jobs using the SageMaker Python SDK. You’ll learn how to monitor jobs, retrieve trained models, and evaluate results—building a strong foundation for advanced SageMaker workflows.

Take your models live with SageMaker’s powerful deployment options. Learn to package and deploy models to serverless and real-time endpoints, test predictions at scale, and manage the full deployment lifecycle. You’ll gain hands-on experience with endpoint monitoring, cleanup, and cost management for reliable, production-ready inference.

Supercharge your ML projects by building automated pipelines with SageMaker Pipelines. Connect data processing, training, evaluation, and deployment into robust, repeatable workflows. You’ll learn to monitor pipeline runs, add evaluation steps, and automate model registration and deployment—making your machine learning solutions faster, smarter, and easier to maintain.

Master the SageMaker AI Console to efficiently manage your machine learning resources and workflows. Learn to navigate the web console interface for monitoring training jobs, managing endpoints, and organizing model artifacts. Gain practical skills in resource management, cost optimization, endpoint deployment, and setting up comprehensive monitoring and alerts for production ML systems.