Remove APIs Remove Big data Remove Government Remove Technology
article thumbnail

Improve governance of your machine learning models with Amazon SageMaker

AWS Machine Learning

Overview of model governance. Model governance is a framework that gives systematic visibility into model development, validation, and usage. Model governance is applicable across the end-to-end ML workflow, starting from identifying the ML use case to ongoing monitoring of a deployed model through alerts, reports, and dashboards.

article thumbnail

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 113
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture.

Scripts 95
article thumbnail

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies.

article thumbnail

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table. put_record API to ingest individual records or to handle streaming sources. About the authors.

Scripts 73
article thumbnail

Identify rooftop solar panels from satellite imagery using Amazon Rekognition Custom Labels

AWS Machine Learning

Governments in many countries are providing incentives and subsidies to households to install solar panels as part of small-scale renewable energy schemes. With rapid development in computer vision technology, several third-party tools use computer vision to analyze satellite images and identify objects (like solar panels) automatically.

APIs 70
article thumbnail

MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

Building an MLOps foundation that can cover the operations, people, and technology needs of enterprise customers is challenging. Initial phase: During this phase, the data scientists are able to experiment and build, train, and deploy models on AWS using SageMaker services. Data lake and MLOps integration. MLOps maturity model.