Amazon SageMaker AI

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Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI

Amazon Nova, Amazon SageMaker, Amazon SageMaker AI, Announcements, Artificial Intelligence, Foundation models /

Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […]

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Building enterprise-scale RAG applications with Amazon S3 Vectors and DeepSeek R1 on Amazon SageMaker AI

Amazon SageMaker, Amazon SageMaker AI, Generative AI, Launch /

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI,

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How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI

Amazon Machine Learning, Amazon SageMaker AI, Customer Solutions, DevOps /

This post is cowritten with Jimmy Cancilla from Rapid7. Organizations are managing increasingly distributed systems, which span on-premises infrastructure, cloud services, and edge devices. As systems become interconnected and exchange data, the potential pathways for exploitation multiply, and vulnerability management becomes critical to managing risk. Vulnerability management (VM) is the process of identifying, classifying, prioritizing,

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Advanced fine-tuning methods on Amazon SageMaker AI

Amazon SageMaker AI, Artificial Intelligence, Best Practices /

This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle stages, the spectrum of fine-tuning methodologies,

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New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models

Amazon Bedrock, Amazon SageMaker AI, Amazon SageMaker HyperPod, Amazon SageMaker JumpStart, Announcements /

As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure, tools, and workflows of Amazon SageMaker AI to scale and advance AI model development. Since launching

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Accelerating generative AI development with fully managed MLflow 3.0 on Amazon SageMaker AI

Amazon SageMaker AI, Announcements, Technical How-to /

Amazon SageMaker now offers fully managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development. As customers across industries accelerate their generative AI development, they require capabilities to

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Use K8sGPT and Amazon Bedrock for simplified Kubernetes cluster maintenance

Amazon Bedrock, Amazon Elastic Kubernetes Service, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence /

As Kubernetes clusters grow in complexity, managing them efficiently becomes increasingly challenging. Troubleshooting modern Kubernetes environments requires deep expertise across multiple domains—networking, storage, security, and the expanding ecosystem of CNCF plugins. With Kubernetes now hosting mission-critical workloads, rapid issue resolution has become paramount to maintaining business continuity. Integrating advanced generative AI tools like K8sGPT and

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Choosing the right approach for generative AI-powered structured data retrieval

AI/ML, Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, Amazon Q Business, Amazon QuickSight, Amazon Redshift, Amazon SageMaker, Amazon SageMaker AI, Generative AI, Generative BI, Natural Language Processing /

Organizations want direct answers to their business questions without the complexity of writing SQL queries or navigating through business intelligence (BI) dashboards to extract data from structured data stores. Examples of structured data include tables, databases, and data warehouses that conform to a predefined schema. Large language model (LLM)-powered natural language query systems transform how

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Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK

Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence /

Amazon SageMaker Inference has been a popular tool for deploying advanced machine learning (ML) and generative AI models at scale. As AI applications become increasingly complex, customers want to deploy multiple models in a coordinated group that collectively process inference requests for an application. In addition, with the evolution of generative AI applications, many use

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Using Amazon SageMaker AI Random Cut Forest for NASA’s Blue Origin spacecraft sensor data

Aerospace & Satellite, Amazon SageMaker, Amazon SageMaker AI, Amazon Simple Storage Service (S3), Amazon VPC, Artificial Intelligence, Intermediate (200), Python, Technical How-to /

The successful deorbit, descent, and landing of spacecraft on the Moon requires precise control and monitoring of vehicle dynamics. Anomaly detection provides a unique utility for identifying important states that might represent vehicle behaviors of interest. By producing unique vehicle behavior points, critical spacecraft system states can be identified to be more appropriately addressed and

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