AWS Machine Learning Blog

Category: Management Tools

Part 2: How NatWest Group built a secure, compliant, self-service MLOps platform using AWS Service Catalog and Amazon SageMaker

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 […]

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Improve your data science workflow with a multi-branch training MLOps pipeline using AWS

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 […]

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Create a cross-account machine learning training and deployment environment with AWS Code Pipeline

A continuous integration and continuous delivery (CI/CD) pipeline helps you automate steps in your machine learning (ML) applications such as data ingestion, data preparation, feature engineering, modeling training, and model deployment. A pipeline across multiple AWS accounts improves security, agility, and resilience because an AWS account provides a natural security and access boundary for your […]

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Create Amazon SageMaker projects with image building CI/CD pipelines

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 […]

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Secure multi-account model deployment with Amazon SageMaker: Part 2

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 […]

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Secure multi-account model deployment with Amazon SageMaker: Part 1

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 […]

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Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 2

In Part 1 of this series, we walk through a continuous model improvement machine learning (ML) workflow with Amazon Rekognition Custom Labels and Amazon Augmented AI (Amazon A2I). We explained how we use AWS Step Functions to orchestrate model training and deployment, and custom label detection backed by a human labeling private workforce. We described […]

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Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 1

If you need to integrate image analysis into your business process to detect objects or scenes unique to your business domain, you need to build your own custom machine learning (ML) model. Building a custom model requires advanced ML expertise and can be a technical challenge if you have limited ML knowledge. Because model performance […]

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Automate a centralized deployment of Amazon SageMaker Studio with AWS Service Catalog

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 […]

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Creating an end-to-end application for orchestrating custom deep learning HPO, training, and inference using AWS Step Functions

Amazon SageMaker hyperparameter tuning provides a built-in solution for scalable training and hyperparameter optimization (HPO). However, for some applications (such as those with a preference of different HPO libraries or customized HPO features), we need custom machine learning (ML) solutions that allow retraining and HPO. This post offers a step-by-step guide to build a custom deep […]

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