Accelerate Your AI Development and Deployment with Amazon SageMaker and Tecton
Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production.
ROI isn’t just about getting to production—it’s about model accuracy and performance. You need a scalable, reliable system with high accuracy and low latency for the real-time use cases that directly impact the bottom line every millisecond.
Streamline Feature Development and Model Training
Tecton’s declarative framework makes it simple to define features and generate accurate training data for SageMaker models:
- Experiment and iterate on features in SageMaker notebooks
- Orchestrate with Tecton-managed EMR clusters
- Generate accurate training data for SageMaker models
Serve Features with Robust, Real-Time Online Inference
Tecton’s declarative framework extends to online serving. Tecton’s real-time infrastructure is designed to help meet the demands of extensive applications and can reliably run 100,000 requests per second.
For critical ML apps, it’s hard to meet demanding service level agreements (SLAs) in a scalable and cost-efficient manner. Real-time use cases such as fraud detection typically have a p99 latency budget between 100 to 200 milliseconds. That means 99% of requests need to be faster than 200ms for the end-to-end process from feature retrieval to model scoring and post-processing.
Expand to Generative AI Use Cases with Your Existing AWS and Tecton Architecture
After you’ve developed ML features using the Tecton and AWS architecture, you can extend your ML work to generative AI use cases.
For instance, in the fraud detection example, you might want to add an LLM-powered customer support chat that helps a user answer questions about their account. To generate a useful response, the chat would need to reference different data sources, including the unstructured documents in your knowledge base (such as policy documentation about what causes an account suspension) and structured data such as transaction history and real-time account activity.
Build Valuable AI Apps Faster with AWS and Tecton
In this post, we walked through how SageMaker and Tecton enable AI teams to train and deploy a high-performing, real-time AI application—without the complex data engineering work. Tecton combines production ML capabilities with the convenience of doing everything from within SageMaker, whether that’s at the development stage for training models or doing real-time inference in production.
About the Authors
Isaac Cameron is Lead Solutions Architect at Tecton, guiding customers in designing and deploying real-time machine learning applications.
Alex Gnibus is a technical evangelist at Tecton, making technical concepts accessible and actionable for engineering teams.
Conclusion
By leveraging Tecton and Amazon SageMaker, you can streamline your AI development and deployment, accelerate your time-to-value, and focus on building new features and use cases instead of struggling to manage the existing infrastructure.
