Category: AWS Service Catalog

In this post, we share the story of how The Weather Company (TWCo) enhanced its MLOps platform using services such as Amazon SageMaker, AWS CloudFormation, and Amazon CloudWatch. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. TWCo reduced infrastructure management time by 90% while also reducing model deployment time by 20%.

Having an environment capable of delivering Amazon SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams […]

This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]

In this post, you will learn how to create a multi-branch training MLOps continuous integration and continuous delivery (CI/CD) pipeline using AWS CodePipeline and AWS CodeCommit, in addition to Jenkins and GitHub. I discuss the concept of experiment branches, where data scientists can work in parallel and eventually merge their experiment back into the main […]

Amazon SageMaker projects are AWS Service Catalog provisioned products that enable you to easily create end-to-end machine learning (ML) solutions. SageMaker projects give organizations the ability to use templates that bootstrap ML solutions for your users to speed up the start time for ML development. You can now use SageMaker projects to manage custom dependencies […]

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise […]

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill […]

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to […]

As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing IT and business work streams. A […]

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model […]