Amazon SageMaker Best Practices
Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
RDCreated by Sireesha Muppala, Shelbee Eigenbrode, Randy DeFauw
Explore proven strategies for building, training, and deploying machine learning models using Amazon SageMaker. Learn how to automate workflows, optimize resources, and address common data science challenges. Gain practical insights to make your ML solutions more efficient and reliable.
Packt | Sep 2021 | 348 min
What You Will Learn
You will work through practical examples and step-by-step explanations that mirror real-world machine learning challenges. Each section focuses on applying SageMaker features to solve specific problems, improve efficiency, and automate processes. By practicing these techniques, you will build confidence in managing end-to-end ML pipelines.
Key Features
- Master techniques for scalable data processing and bias detection in ML workflows
- Automate deployment and monitoring of models to streamline production tasks
- Optimize training time and costs using advanced SageMaker capabilities
Target Audience
This course is ideal for data scientists and ML engineers with experience in Amazon SageMaker, Python, and Jupyter Notebooks. If you want to deepen your skills in automating, optimizing, and scaling machine learning solutions on AWS, and already have a solid foundation in ML concepts and AWS basics, you'll benefit most from this content.
