The text describes the importance of Machine Learning Operations (MLOps) in integrating ML models into production systems. It explains Amazon SageMaker MLOps features like Projects, Pipelines, and Model Registry. The process of creating a custom project template for CI/CD pipelines using AWS services and GitHub is detailed, along with a summary of the implementation.
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Integrating Machine Learning Models into Production Systems
Machine learning (ML) models need to integrate into existing production systems and infrastructure to deliver value. ML operations (MLOps) focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building a robust MLOps pipeline demands cross-functional collaboration.
Amazon SageMaker MLOps
Amazon SageMaker MLOps is a suite of features that includes Amazon SageMaker Projects (CI/CD), Amazon SageMaker Pipelines, and Amazon SageMaker Model Registry. SageMaker Pipelines allows for straightforward creation and management of ML workflows, while SageMaker Model Registry centralizes model tracking, simplifying model deployment. SageMaker Projects introduces CI/CD practices to ML, facilitating effective scalability throughout your enterprise.
GitHub and GitHub Actions
GitHub is a web-based platform that provides version control and source code management using Git. GitHub Actions is a powerful automation tool within the GitHub ecosystem, allowing you to create custom workflows that automate your software development lifecycle processes, such as building, testing, and deploying code.
Prerequisites
Before implementing the solution, ensure you have the following prerequisites: a GitHub account, an AWS account, a SageMaker Studio domain, and the AWS Command Line Interface (AWS CLI) installed and configured.
Create a Custom SageMaker MLOps Project Template
Follow the step-by-step implementation to create a custom SageMaker MLOps project template that integrates with GitHub and GitHub Actions and make it available in Amazon SageMaker Studio for your data science team with one-click provisioning.
Summary
In this post, we walked through the process of using a custom SageMaker MLOps project template to automatically construct and organize a CI/CD pipeline. This pipeline effectively integrates your existing CI/CD mechanisms with SageMaker capabilities for data manipulation, model training, model approval, and model deployment. For a comprehensive understanding of the implementation details, visit the GitHub repository.
About the Authors
Dr. Romina Sharifpour and Pooya Vahidi are Senior Machine Learning and Artificial Intelligence Solutions Architects at Amazon Web Services (AWS). They have extensive experience in leading the design and implementation of innovative end-to-end solutions enabled by advancements in ML and AI.
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