A data ingestion pipeline diagram for Amazon Bedrock Knowledge Bases using S3 and Redshift.

How to Architect Amazon Bedrock Knowledge Base Ingestion Pipelines

By: Oscar Moncada

Best practices for S3 ingestion, Redshift access, multimodal content, and retrieval at scale

Many Amazon Bedrock implementations start with what looks like a straightforward goal: connect structured and unstructured data sources, make them accessible to a foundation model, and enable natural language interaction. What becomes clear very quickly is that the quality, reliability, and cost of the solution depend heavily on decisions made at the data layer (not in the model or prompt design).

Cloud automation strategy blog - data center image with automation screen

Automation Strategies to Improve Your Cloud Operations Workflow

Managed Services

By: Kevin RisonChu

Cloud operations teams that automate their single most repetitive, highest-friction manual task before chasing more complex systems see the fastest and most durable gains, based on strategies shared by 13 technology leaders for this piece.

The Evolution of AI Services on AWS: Past, Present, and Future

News & Insights

By: Kevin RisonChu

AI Trends Shaping the Next Era of Innovation

Learn how AWS has lowered barriers to AI adoption, providing scalable solutions that empower both startups and enterprises to integrate advanced AI capabilities, fundamentally changing the way we work.

An Overview of Data Lakes, Use Cases, and Corresponding Tools on AWS

Managed Services

SMBs are increasingly leveraging data lakes for advanced and real-time analytics

A data lake is a centralized repository that allows businesses to store structured and unstructured data at any scale. It provides a single source of data that can be analyzed and used to support a variety of business needs, such as reporting, analytics, machine learning, and more.

How to: Your guide to Artificial Intelligence on AWS in 2021

Managed Services

AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. To remove the complexity of getting started with machine learning, AWS developed the Sagemaker platform to help customers get started using AI to their advantage, with no experience.

AI on AWS: Business Applications for Artificial Intelligence

Application Modernization

The business potential of artificial intelligence has been on the horizon for decades. But many businesses have found AI hard to implement, or to even start imagining. Thanks to some relatively recent breakthroughs, that potential is being realized now.

Cloud Computing’s Influence On Artificial Intelligence

News & Insights

By: Oscar Moncada

If you’ve been following technology news in recent months you’ve probably heard all the buzz about artificial intelligence and terms such as “machine learning” and “neural networks”. The truth is artificial intelligence has been around for a while. It is just being covered a little more in the news lately. One of the factors that have allowed artificial intelligence to be more relevant has been the public cloud. The advent of the public cloud has had an enormous impact on the advancement of artificial intelligence in recent years.