How Rocket Companies modernized their data science solution on AWS
In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational…
MLOps
Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls
This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model…
Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker | Amazon Web Services
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up…
FMOps/LLMOps: Operationalize generative AI and differences with MLOps | Amazon Web Services
Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the…
Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines | Amazon Web Services
MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. It’s natural to focus on a single model that you want to train and…