Category: Amazon Machine Learning

This post is going to introduce the overall architecture of DataRobot AI Platform and also demonstrate how you can build a GenAI application, from LLM selection, prompt testing and model evaluation to Retrieval Augmented Generation (RAG), LLM monitoring and guardrails. Everything has been combined into one unified DataRobot AI platform with a streamlined user experience.

In part one of this three-part blog post series on AWS Partner offerings for generative AI in AWS Marketplace, we will explore available core and infrastructure software offerings.

In this post, Ramya and I introduce you to the new SPG dashboard as a single pane of glass for your Marketplace transactions. You can view this dashboard without having AWS Identity and Access Management (IAM) permissions or technical proficiency on the underlying AWS services. We show how you can use the SPG dashboard for a simplified view of your AWS Marketplace subscriptions for spend management and usage tracking.

In this blog post, we introduce you to Glean – an enterprise-ready search and knowledge discovery solution that’s tailor-made for the enterprise workplace. Glean has been adopted by leading enterprise customers, including Databricks, Okta, and Grammarly, to solve their internal search and knowledge discovery needs. Now available in AWS Marketplace, Glean uses powerful large language models (LLMs) hosted by Amazon Bedrock to deliver generative AI solutions to the millions of customers building on AWS.

In this blog post, Sandeep, Malini, Bakha, and Aparna will show how to convert your healthcare data to FHIR format and load it into AWS HealthLake using HiPaaS FHIR data converter solution. This solution enables you to access and manage the data from various sources without the need for manual data entry or reconciliation.

In this post, Brian, Snehanshu, and I’ll show you how to mask healthcare data for regulatory compliance using Amazon HealthLake and DataMasque.

In this post, I show how to use InfinStor MLflow with Amazon SageMaker Studio to experiment, collaborate, train, and run inferences using this ML platform. With this solution, you do not need to write special code for experiment tracking or model management. You can also share experiments and models with authorized colleagues. SageMaker Studio provides the Notebook and remote IPython kernel portion of the solution, and InfinStor MLflow provides the experiment tracking and model management.

As AWS consultants, Victor and I often get asked about recommendations on the right instance configuration to use for real-time inference. Finding the correct instance size to host your trained machine learning (ML) models might be a challenging task. However, choosing the right instance and auto scaling configuration can help reduce model serving costs without […]

Many businesses are now adopting machine learning (ML) as a mainstream method of augmenting processes and building efficient systems. You can use active learning to get to an acceptable and working version of your ML model much faster. This post is a summary of the joint webinar with Mphasis and AWS Marketplace, Want to build […]

Image classification and object detection technology allows you to build scalable artificial intelligence models for business cases like visual search, product recommendations, autonomous vehicle object recognition, content moderation, and more. Today, services like Amazon Rekognition offer APIs to perform image analysis and object recognition. However, if your use case requires a more custom image classification […]