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In this new age of AI, businesses across industries are recognizing the power of intelligent apps to drive innovation and maintain a competitive edge. By harnessing cloud-native technologies and AI-powered tools, companies are modernizing their applications, creating new experiences for their users, and creating new revenue streams. This blog explores the benefits of modernizing and …

From idea to impact: Real-world success stories of building intelligent apps with Azure Read More »

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Evaluation, on the other hand, involves …

Empower your generative AI application with a comprehensive custom observability solution Read More »

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and …

Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline Read More »

AI has reset our expectations of what technology can achieve. From transforming how we explore the cosmos to enabling doctors to provide personalized care and making business functions operate more intelligently, it all comes down to you—the developer—to turn this potential into reality. As developers, you’re experiencing a dramatic shift in what you build and …

Azure at GitHub Universe: New tools to help simplify AI app development Read More »

Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. These meetings often involve exchanging information and discussing actions that one or more parties must take after the session. The traditional way to make sure information and actions aren’t forgotten is …

Build a video insights and summarization engine using generative AI with Amazon Bedrock Read More »

Enterprises in industries like manufacturing, finance, and healthcare are inundated with a constant flow of documents—from financial reports and contracts to patient records and supply chain documents. Historically, processing and extracting insights from these unstructured data sources has been a manual, time-consuming, and error-prone task. However, the rise of intelligent document processing (IDP), which uses …

Automate document processing with Amazon Bedrock Prompt Flows (preview) Read More »

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. A multi-account strategy is essential not only for improving governance but also for enhancing …

Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch Read More »

In today’s fast-evolving digital landscape, enterprises need more than just powerful AI models—they need AI solutions that are adaptable, reliable, and scalable. With upcoming availability of Data Zones and new enhancements to Provisioned offering in Azure OpenAI Service, we are taking a big step forward in making AI broadly available and also enterprise-ready. These features …

Accelerate scale with Azure OpenAI Service Provisioned offering Read More »

In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without …

Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas Read More »

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. In these scenarios, customized model monitoring for near real-time batch inference with Amazon …

Customized model monitoring for near real-time batch inference with Amazon SageMaker Read More »