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How AWS Sales uses generative AI to streamline account planning | AWS Machine Learning Blog
Every year, AWS Sales personnel draft in-depth, forward looking strategy documents for established AWS customers. These documents help the AWS Sales team to align with our customer growth strategy and to collaborate with the entire sales team on long-term growth ideas for AWS customers. In this post, we showcase how the AWS Sales product team built the generative AI account plans draft assistant.
AI Workforce: using AI and Drones to simplify infrastructure inspections | AWS Machine Learning Blog
Inspecting wind turbines, power lines, 5G towers, and pipelines is a tough job. It’s often dangerous, time-consuming, and prone to human error. This post is the first in a three-part series exploring AI Workforce, the AWS AI-powered drone inspection system. In this post, we introduce the concept and key benefits. The second post dives into the AWS architecture that powers AI Workforce, and the third focuses on the drone setup and integration.
Correlate telemetry data with Amazon OpenSearch Service and Amazon Managed Grafana | AWS Big Data Blog
In this post, we show you how to use Amazon OpenSearch Service and Amazon Managed Grafana to correlate the various observability signals that improve root cause analysis, thereby resulting in reduced Mean Time to Resolution (MTTR). We also provide a reference solution that can be used at scale for proactive monitoring of enterprise applications to avoid a problem before they occur.
Build low-latency, resilient applications with Amazon MemoryDB Multi-Region | AWS Database Blog
On December 1, 2024, we announced the general availability of Amazon MemoryDB Multi-Region, a fully managed, active-active, multi-Region database that you can use to build applications with up to 99.999% availability, microsecond read, and single-digit millisecond write latencies across multiple Regions. In this post, we cover the benefits of MemoryDB Multi-Region, how it works, its disaster recovery capabilities, the consistency and conflict resolution mechanisms, and how to monitor replication lag across Regions.
Shaping the future: OMRON’s data-driven journey with AWS | AWS Machine Learning Blog
OMRON Corporation is a leading technology provider in industrial automation, healthcare, and electronic components. In their Shaping the Future 2030 (SF2030) strategic plan, OMRON aims to address diverse social issues, drive sustainable business growth, transform business models and capabilities, and accelerate digital transformation. At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. This post explores how OMRON Europe is using Amazon Web Services (AWS) to build its advanced ODAP and its progress toward harnessing the power of generative AI.
Using Large Language Models on Amazon Bedrock for multi-step task execution | AWS Machine Learning Blog
This post explores the application of LLMs in executing complex analytical queries through an API, with specific focus on Amazon Bedrock. To demonstrate this process, we present a use case where the system identifies the patient with the least number of vaccines by retrieving, grouping, and sorting data, and ultimately presenting the final result.
Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI | AWS Machine Learning Blog
Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. In this post, we demonstrate the steps involved in running Ray jobs on SageMaker HyperPod.
Improve PostgreSQL performance using the pgstattuple extension | AWS Database Blog
In this post, we explore the pgstattuple extension in depth; what insights it offers, how to use it to diagnose issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL, and best practices for harnessing its capabilities.
Introducing AWS MCP Servers for code assistants (Part 1) | AWS Machine Learning Blog
We’re excited to announce the open source release of AWS MCP Servers for code assistants — a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers. In this post, we walk through how these specialized MCP servers can dramatically reduce your development time while incorporating security controls, cost optimizations, and AWS Well-Architected best practices into your code.
Using StatsD for monitoring Oracle databases running on Amazon RDS or Amazon EC2 | AWS Database Blog
Monitoring databases is essential in large IT environments to prevent potential issues from becoming major problems that can result in data loss or downtime. Having custom dashboards and alarm-based monitoring for the database can help in analyzing historical metrics patterns and improve database availability by alerting users of any abnormal threshold breaches. In this post, we show you how to set up monitoring for your Oracle database using StatsD.
AWS App Studio introduces a prebuilt solutions catalog and cross-instance Import and Export | AWS Machine Learning Blog
In a recent AWS What’s New Post, App Studio announced two new features to accelerate application building: Prebuilt solutions catalog and cross-instance Import and Export. In this post, we walk through how to use the prebuilt solutions catalog to get started quickly and use the Import and Export feature
Harness the power of MCP servers with Amazon Bedrock Agents | AWS Machine Learning Blog
Today, MCP is providing agents standard access to an expanding list of accessible tools that you can use to accomplish a variety of tasks. In this post, we show you how to build an Amazon Bedrock agent that uses MCP to access data sources to quickly build generative AI applications.
Generate compliant content with Amazon Bedrock and ConstitutionalChain | AWS Machine Learning Blog
In this post, we explore practical strategies for using Constitutional AI to produce compliant content efficiently and effectively using Amazon Bedrock and LangGraph to build ConstitutionalChain for rapid content creation in highly regulated industries like finance and healthcare
Minimize generative AI hallucinations with Amazon Bedrock Automated Reasoning checks | AWS Machine Learning Blog
To improve factual accuracy of large language model (LLM) responses, AWS announced Amazon Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. In this post, we discuss how to help prevent generative AI hallucinations using Amazon Bedrock Automated Reasoning checks.
Optimizing cost savings: The advantage of Amazon Aurora over self-managed open source databases | AWS Database Blog
In this post, we highlight often overlooked architectural designs and the inherent features of Aurora that optimize costs when deploying an open source database. The following sections examine various use cases, contrasting typical self-managed database configurations and their associated costs with the equivalent solution on Aurora, highlighting potential cost savings and operational efficiencies.
Build agentic systems with CrewAI and Amazon Bedrock | AWS Machine Learning Blog
In this post, we explore how CrewAI’s open source agentic framework, combined with Amazon Bedrock, enables the creation of sophisticated multi-agent systems that can transform how businesses operate. Through practical examples and implementation details, we demonstrate how to build, deploy, and orchestrate AI agents that can tackle complex tasks with minimal human oversight.
Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today | AWS Machine Learning Blog
Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. In this post, we discuss how to get started with image content filters in Amazon Bedrock Guardrails.
Enhance governance with metadata enforcement rules in Amazon SageMaker | AWS Big Data Blog
Amazon SageMaker Catalog now supports metadata rules allowing organizations to enforce metadata standards across data publishing and subscription workflows. In this post, we guide you through two workflows: setting up metadata enforcement rules for a specific domain and publishing an asset or data product in a catalog, and setting up metadata enforcement rules for a specific domain and subscribing to an asset or data product that is owned by a project within that domain.
Enable Amazon Bedrock cross-Region inference in multi-account environments | AWS Machine Learning Blog
In this post, we explore how to modify your Regional access controls to specifically allow Amazon Bedrock cross-Region inference while maintaining broader Regional restrictions for other AWS services. We provide practical examples for both SCP modifications and AWS Control Tower implementations.
Generate training data and cost-effectively train categorical models with Amazon Bedrock | AWS Machine Learning Blog
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test data creation for multiclass classification supervised learning use cases. We dive deep into this process on how to use XML tags to structure the prompt and guide Amazon Bedrock in generating a balanced label dataset with high accuracy. We also showcase a real-world example for predicting the root cause category for support cases. This use case, solvable through ML, can enable support teams to better understand customer needs and optimize response strategies.
Integrating custom dependencies in Amazon SageMaker Canvas workflows | AWS Machine Learning Blog
When implementing machine learning workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an example of how to incorporate code that relies on external dependencies into your SageMaker Canvas workflows.
Generative AI-powered game design: Accelerating early development with Stability AI models on Amazon Bedrock | AWS Machine Learning Blog
Generative AI has emerged as a game changer, offering unprecedented opportunities for game designers to push boundaries and create immersive virtual worlds. At the forefront of this revolution is Stability AI’s cutting-edge text-to-image AI model, Stable Diffusion 3.5 Large (SD3.5 Large), which is transforming the way we approach game environment creation. In this post, we explore how you can use SD3.5 Large to address practical gaming needs such as early concept art and character design.
Up and running with Apache OFBiz and Amazon Aurora DSQL | AWS Database Blog
In this post, we show you a worked example of taking an existing application that works on PostgreSQL databases and adapting it to work with an Aurora DSQL database. In addition to adapting for the previously mentioned aspects, we also address some data type incompatibilities and work around some limits that currently exist in Aurora DSQL.
Amazon SageMaker JumpStart adds fine-tuning support for models in a private model hub | AWS Machine Learning Blog
Today, we are announcing an enhanced private hub feature with several new capabilities that give organizations greater control over their ML assets. These enhancements include the ability to fine-tune SageMaker JumpStart models directly within the private hub, support for adding and managing custom-trained models, deep linking capabilities for associated notebooks, and improved model version management.
Build multi-Region resilient Apache Kafka applications with identical topic names using Amazon MSK and Amazon MSK Replicator | AWS Big Data Blog
This post explains how to use MSK Replicator for cross-cluster data replication and details the failover and failback processes while keeping the same topic name across Regions.
Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference | AWS Machine Learning Blog
In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.
Evaluate and improve performance of Amazon Bedrock Knowledge Bases | AWS Machine Learning Blog
In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.
Transition a pivot query that includes dynamic columns from SQL Server to PostgreSQL | AWS Database Blog
When assisting customers with migrating their workloads from SQL Server to PostgreSQL, we often encounter a scenario where the PIVOT function is used extensively for generating dynamic reports. In this post, we show you how to use the crosstab function, provided by PostgreSQL’s tablefunc extension, to implement functionality similar to SQL Server’s PIVOT function, offering greater flexibility.
Amazon Bedrock launches Session Management APIs for generative AI applications (Preview) | AWS Machine Learning Blog
Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across […]
Integrate natural language processing and generative AI with relational databases | AWS Database Blog
In this post, we present an approach to using natural language processing (NLP) to query an Amazon Aurora PostgreSQL-Compatible Edition database. The solution presented in this post assumes that an organization has an Aurora PostgreSQL database. We create a web application framework using Flask for the user to interact with the database. JavaScript and Python code act as the interface between the web framework, Amazon Bedrock, and the database.
Using Amazon S3 Tables with Amazon Redshift to query Apache Iceberg tables | AWS Big Data Blog
In this post, we demonstrate how to get started with S3 Tables and Amazon Redshift Serverless for querying data in Iceberg tables. We show how to set up S3 Tables, load data, register them in the unified data lake catalog, set up basic access controls in SageMaker Lakehouse through AWS Lake Formation, and query the data using Amazon Redshift.
Automate IT operations with Amazon Bedrock Agents | AWS Machine Learning Blog
This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock, AWS Lambda, and Amazon CloudWatch to create an AI assistant for effective incident management. This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents. The solution uses the power of Amazon Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.
Connect, share, and query where your data sits using Amazon SageMaker Unified Studio | AWS Big Data Blog
In this blog post, we will demonstrate how business units can use Amazon SageMaker Unified Studio to discover, subscribe to, and analyze these distributed data assets. Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems.
Build a generative AI enabled virtual IT troubleshooting assistant using Amazon Q Business | AWS Machine Learning Blog
Discover how to build a GenAI powered virtual IT troubleshooting assistant using Amazon Q Business. This innovative solution integrates with popular ITSM tools like ServiceNow, Atlassian Jira, and Confluence to streamline information retrieval and enhance collaboration across your organization. By harnessing the power of generative AI, this assistant can significantly boost operational efficiency and provide 24/7 support tailored to individual needs. Learn how to set up, configure, and leverage this solution to transform your enterprise information management.
Process formulas and charts with Anthropic’s Claude on Amazon Bedrock | AWS Machine Learning Blog
In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. By extracting and structuring the key information from the source materials, the models can create a searchable knowledge base that allows you to quickly locate the data, formulas, and visualizations you need to support your work.
Introducing vector search with UltraWarm in Amazon OpenSearch Service | AWS Big Data Blog
Amazon OpenSearch Service also offers a multi-tiered storage solution to its customers in the form of UltraWarm and Cold tiers. In this post, we discuss this new capability and its use cases, and provide a cost-benefit analysis in different scenarios.
Unleashing the multimodal power of Amazon Bedrock Data Automation to transform unstructured data into actionable insights | AWS Machine Learning Blog
Today, we're excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.
Streamline AWS resource troubleshooting with Amazon Bedrock Agents and AWS Support Automation Workflows | AWS Machine Learning Blog
AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of Amazon Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.
Build a data lakehouse in a hybrid Environment using Amazon EMR Serverless, Apache DolphinScheduler, and TiDB | AWS Big Data Blog
This post discusses a decoupled approach of building a serverless data lakehouse using AWS Cloud-centered services, including Amazon EMR Serverless, Amazon Athena, Amazon Simple Storage Service (Amazon S3), Apache DolphinScheduler (an open source data job scheduler) as well as PingCAP TiDB, a third-party data warehouse product that can be deployed either on premises or on the cloud or through a software as a service (SaaS).