AWS Machine Learning Blog
In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK. This also uses resource chaining. Instead of supplying just the endpoint_config_name (in Boto3), you pass the whole endpoint_config object. As we have shown in these steps, SageMaker Core simplifies the development experience by providing an object-oriented interface for interacting with SageMaker resources. The use of intelligent defaults and resource chaining reduces the amount of boilerplate code and manual parameter specification, resulting in more readable and maintainable code. Cleanup Any endpoint created using the code in this post will incur charges. Shut down any unused endpoints by using the delete() method. A note on existing SageMaker Python SDK SageMaker Python SDK will be using the SageMaker Core as its foundation and will benefit from the object-oriented interfaces created as part of SageMaker Core. Customers can choose to use the object-oriented approach while using the SageMaker Python SDK going forward. Benefits The SageMaker Core SDK offers several benefits: Simplified development By abstracting low-level details and providing intelligent defaults, developers can focus on building and deploying ML models without getting slowed down by repetitive tasks. It also relieves the developers of the cognitive overload of having to remember long and complex multilevel dictionaries. They can instead work on the object-oriented paradigm that developers are most comfortable with. Increased productivity Features like automatic code completion and type hints help developers write code faster and with fewer errors. Enhanced readability Dedicated resource classes and resource chaining result in more readable and maintainable code. Lightweight integration with AWS Lambda Because this SDK is lightweight (about 8 MB when unzipped), it is straightforward to build an AWS Lambda layer for SageMaker Core and use it for executing various steps in the ML lifecycle through Lambda functions. Conclusion SageMaker Core is a powerful addition to Amazon SageMaker, providing a streamlined and efficient development experience for ML practitioners. With its object-oriented interface, resource chaining, and intelligent defaults, SageMaker Core empowers developers to focus on building and deploying ML models without getting slowed down by complex orchestration of JSON structures. Check out the following resources to get started today on SageMaker Core: SageMaker Core SDK documenation SageMaker Core GitHub PyPI package for SageMaker Core Hands-on examples and notebooks About the authors Vikesh Pandey is a Principal GenAI/ML Specialist Solutions Architect at AWS, helping customers from financial industries design, build and scale their GenAI/ML workloads on AWS. He carries an experience of more than a decade and a half working on entire ML and software engineering stack. Outside of work, Vikesh enjoys trying out different cuisines and playing outdoor sports. Shweta Singh is a Senior Product Manager in the Amazon SageMaker Machine Learning (ML) platform team at AWS, leading SageMaker Python SDK. She has worked in several product roles in Amazon for over 5 years. She has a Bachelor of Science degree in Computer Engineering and Masters of Science in Financial Engineering, both from New York University.
Published: 2024-10-15T17:37:15
