Enterprise Machine Learning with Amazon SageMaker

Overview

Bring your machine learning projects to life in the cloud! This path takes you through every stage of the ML lifecycle with Amazon SageMaker—AWS’s platform for building, training, deploying, and automating models. Gain the skills to deliver scalable, production-ready ML solutions.

Syllabus

Course 1: Revisiting Machine Learning Fundamentals
Course 2: Training Models in AWS with SageMaker Python SDK
Course 3: Deploying Models to AWS Endpoints with SageMaker
Course 4: Automating ML Workflows with SageMaker Pipelines
Course 5: Managing ML Resources in SageMaker AI Console

Courses

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3 hours
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Work through a practical, end-to-end machine learning project: explore and visualize data, apply preprocessing, build and evaluate models, and deploy a simple REST API. This course refreshes your core ML skills and ensures you’re ready for the more complex, cloud-based workflows ahead.
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3 hours
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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.
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3 hours
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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.
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3 hours
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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.