AWS Machine Learning Blog

Category: Intermediate (200)

Building scalable, secure, and reliable RAG applications using HAQM Bedrock Knowledge Bases

This post explores the new enterprise-grade features for HAQM Bedrock Knowledge Bases and how they align with the AWS Well-Architected Framework. With HAQM Bedrock Knowledge Bases, you can quickly build applications using Retrieval Augmented Generation (RAG) for use cases like question answering, contextual chatbots, and personalized search.

Generate customized, compliant application IaC scripts for AWS Landing Zone using HAQM Bedrock

As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using HAQM Bedrock.

Slack delivers native and secure generative AI powered by HAQM SageMaker JumpStart

We are excited to announce that Slack, a Salesforce company, has collaborated with HAQM SageMaker JumpStart to power Slack AI’s initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by third party model providers. This keeps customer data in Slack at all times and upholds the same security practices and compliance standards that customers expect from Slack itself.

Explore data with ease: Use SQL and Text-to-SQL in HAQM SageMaker Studio JupyterLab notebooks

HAQM SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate […]

Distributed training and efficient scaling with the HAQM SageMaker Model Parallel and Data Parallel Libraries

In this post, we explore the performance benefits of HAQM SageMaker (including SMP and SMDDP), and how you can use the library to train large models efficiently on SageMaker. We demonstrate the performance of SageMaker with benchmarks on ml.p4d.24xlarge clusters up to 128 instances, and FSDP mixed precision with bfloat16 for the Llama 2 model.

Manage your HAQM Lex bot via AWS CloudFormation templates

HAQM Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Managing your […]

HAQM Bedrock Knowledge Bases now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

With HAQM Bedrock Knowledge Bases, you can securely connect foundation models (FMs) in HAQM Bedrock to your company data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant, context-specific, and accurate responses without retraining the FMs. In this post, we discuss two new features of HAQM Bedrock Knowledge Bases […]

HAQM Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy

At AWS re:Invent 2023, we announced the general availability of HAQM Bedrock Knowledge Bases. With HAQM Bedrock Knowledge Bases, you can securely connect foundation models (FMs) in HAQM Bedrock to your company data using a fully managed Retrieval Augmented Generation (RAG) model. For RAG-based applications, the accuracy of the generated responses from FMs depend on […]

Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model. One popular approach is using Retrieval Augmented Generation (RAG) to create Q&A systems […]

Option 2: Notebook export

Seamlessly transition between no-code and code-first machine learning with HAQM SageMaker Canvas and HAQM SageMaker Studio

HAQM SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. HAQM SageMaker Canvas is a powerful […]