Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository.
The continuous evolution of MLOps and the increasing complexity of AI models necessitate more efficient feature engineering and pipeline management.
Improved ML feature pipelines accelerate model development and deployment, making AI applications more robust and scalable for businesses.
Data scientists and ML engineers can now build, train, and deploy models more rapidly with enhanced feature management capabilities.
- · AWS
- · Data Scientists
- · ML Engineers
- · Businesses adopting AI
Faster development and iteration cycles for machine learning models on AWS.
Increased adoption of Amazon SageMaker for complex ML workflows due to improved efficiency.
Potential for a wider range of industries to operationalize sophisticated AI solutions more quickly, driving further demand for cloud ML services.
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Read at AWS Machine Learning Blog