Remove Accountability Remove APIs Remove Management
article thumbnail

Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. An automotive retailer might use inventory management APIs to track stock levels and catalog APIs for vehicle compatibility and specifications.

APIs 127
article thumbnail

Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

AWS Machine Learning

Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. Using natural language processing (NLP) and OpenAPI specs, Amazon Bedrock Agents dynamically manages API sequences, minimizing dependency management complexities.

APIs 130
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

Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

APIs 129
article thumbnail

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions of entities, which are inherently embedded in most real-world data, including fraud detection scenarios, recommendations, community detection, and search/retrieval problems. Specifically, GraphStorm 0.3

APIs 116
article thumbnail

Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Ultimately, this systematic approach to managing models, prompts, and datasets contributes to the development of more reliable and transparent generative AI applications. MLflow is an open source platform for managing the end-to-end ML lifecycle, including experimentation, reproducibility, and deployment.

article thumbnail

Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. Implementing a cost allocation strategy early is critical for managing your expenses and future optimization activities that will reduce your spend.

article thumbnail

Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105