Tag: AI/ML

In this post, we explore how Planview was able to develop a generative AI assistant to address complex work management process by adopting Amazon Bedrock.

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. This example not only highlights the efficiency of SageMaker, but also its impact how geospatial ML can be used to monitor the environment for sustainability and conservation purposes.

In this post, we demonstrate how Amazon Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with Amazon Bedrock Agents.

In this post, we explore some ways you can use Anthropic’s Claude 3 Sonnet’s vision capabilities to accelerate the process of moving from architecture to the prototype stage of a solution.

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. In this post, we share how legal tech professionals can build solutions for different use cases with generative AI on AWS.

In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.

In this post, we present to you an in-depth guide to starting a continual pre-training job using PyTorch Fully Sharded Data Parallel (FSDP) for Mistral AI’s Mathstral model with SageMaker HyperPod.

In this post, we show you how to build an ecommerce product recommendation chatbot using Amazon Bedrock Agents and foundation models (FMs) available in Amazon Bedrock.

In this post, we show you how Thomson Reuters Labs (TR Labs) was able to develop an efficient, flexible, and powerful MLOps process by adopting a standardized MLOps framework that uses AWS SageMaker, SageMaker Experiments, SageMaker Model Registry, and SageMaker Pipelines. The goal being to accelerate how quickly teams can experiment and innovate using AI and machine learning (ML)—whether using natural language processing (NLP), generative AI, or other techniques. We discuss how this has helped decrease the time to market for fresh ideas and helped build a cost-efficient machine learning lifecycle.