Enterprise Machine Learning with Amazon SageMaker | CodeSignal Learn

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intermediate

Enterprise Machine Learning with Amazon SageMaker

Artificial Intelligence

5 courses

92 practices

12 hours

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.

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295 learners

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Verified skills you'll gain

ADVANCED

Machine Learning Model Development

ADVANCED

Model Deployment Processes

Tools you'll use

Amazon SageMaker

AWS

FastAPI

Pandas

Python

Scikit-learn

Course 1

Revisiting Machine Learning Fundamentals

5 lessons

23 practices

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.

Course 2

Training Models in AWS with SageMaker Python SDK

5 lessons

20 practices

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.

Course 3

Deploying Models to AWS Endpoints with SageMaker

5 lessons

21 practices

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.

Course 4

Automating ML Workflows with SageMaker Pipelines

5 lessons

24 practices

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.

Course 5

Managing ML Resources in SageMaker AI Console

4 lessons

4 practices

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.

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