In this course, you will delve into the core elements of the model training process and learn how to select the most suitable compute environment for your specific training requirements. You will explore Amazon SageMaker and the pre-built deep learning framework docker containers and ML library docker images, which provide an efficient way to develop and train your models.
Additionally, you will gain hands-on experience in developing machine learning models using the SageMaker built-in algorithms and libraries. You will also learn to use SageMaker script mode, which supports popular frameworks like Apache MXNet, TensorFlow, and PyTorch. This course will equip you with the knowledge and skills to use these powerful tools and frameworks to build robust and accurate models.
Furthermore, you will learn about various techniques for reducing model training time, which is a crucial aspect in optimizing the overall performance and efficiency of your machine learning workflows. By the end of this course, you will understand the model training process. Lastly, you will learn to make informed decisions when selecting the appropriate compute environment, frameworks, and optimization strategies for your specific use cases.
- Course level:Advanced
- Duration: 1.5 hours
Activities
- Online materials
- Exercises
- Knowledge check questions
Course objectives
- Define core elements in the model training process.
- Select the best compute environment for training based on specific requirements.
- Identify SageMaker pre-built deep learning framework Docker containers.
- Identify SageMaker pre-built ML library Docker images.
- Develop ML models using SageMaker built-in ML algorithms and libraries.
- Develop ML models using Amazon SageMaker Studio.
- Develop ML models using SageMaker script mode and supported frameworks, such as Apache MXNet, TensorFlow, and PyTorch.
- Describe common methods for reducing model training time.
- Describe how to integrate external models into SageMaker.
Intended audience
- Cloud architects
- Machine learning engineers
Recommended Skills
- Completed at least 1 year of experience using SageMaker and other AWS services for ML engineering
- Completed at least 1 year of experience in a related role such as backend software developer, DevOps developer, data engineer, or data scientist
- A fundamental understanding of programming languages such as Python
- Completed preceding courses in the AWS ML Engineer Associate Learning Plan
Course outline
- Section 1: Introduction
- Lesson 1: How to Use This Course
- Lesson 2: Course Overview
- Lesson 3: Model Training Concepts
- Section 2: Compute Environments
- Lesson 4: Compute Environment Selection
- Lesson 5: AWS Container Services
- Section 3: Train a Model
- Lesson 6: Create A Training Job Using the Amazon SageMaker Console
- Lesson 7: Train a Model Using a SageMaker Built-in Algorithm
- Lesson 8: Train a Model Using SageMaker Script Mode
- Lesson 9: Methods to Reduce Training Time
- Section 4: External Models
- Lesson 10: Integrating External Models into SageMaker
- Section 5: Conclusion
- Lesson 11: Course Summary
- Lesson 12: Assessment
- Lesson 13: Contact Us
