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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 »

As Sub-Saharan Africa looks to its ‘digital decade’, President of Google EMEA Matt Brittin discusses the big opportunities and some new announcements from Google.

This post is co-written with Lee Rehwinkel from Planview. Businesses today face numerous challenges in managing intricate projects and programs, deriving valuable insights from massive data volumes, and making timely decisions. These hurdles frequently lead to productivity bottlenecks for program managers and executives, hindering their ability to drive organizational success efficiently. Planview, a leading provider …

How Planview built a scalable AI Assistant for portfolio and project management using Amazon Bedrock Read More »

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. LLMs have the potential to revolutionize content creation and the way people use search engines and …

Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark Read More »

The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. Since the inception of AWS GenAIIC in May 2023, we …

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1 Read More »