Machine learning operations (MLOps) are key to effectively transition from an experimentation phase to production. The practice provides you the ability to create a repeatable mechanism to build, train, deploy, and manage machine learning models. To quickly adopt MLOps, you often require capabilities that use your existing toolsets and expertise. Projects in Amazon SageMaker give organizations the ability to easily set up and standardize developer environments for data scientists and CI/CD (continuous integration, continuous delivery) systems for MLOps engineers. With SageMaker projects, MLOps engineers or organization administrators can define templates that bootstrap the ML workflow with source version control, automated ML pipelines, and a set of code to quickly start iterating over ML use cases.

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost never the end of the work, but is a part of a larger application or workflow. Another challenge is the varied nature of ML development activities performed by different user roles. For example, the DevOps engineer develops infrastructure components, such as CI/CD automation, builds production inference pipelines, and configures security and networking.

SageMaker Projects give organizations the ability to easily setup and standardize developer environments for data scientists and CI/CD systems for MLOps Engineers. With SageMaker Projects, MLOps engineers or organization admins can define templates which bootstrap the ML Workflow with source version control, automated ML Pipelines, and a set of code to quickly start iterating over ML use cases. With Projects, dependency management, code repository management, build reproducibility, artifact sharing and management become easy for organizations to set up. SageMaker Projects are provisioned using AWS Service Catalog products. Project templates are used by organizations to provision Projects for each of their users.

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with existing source control systems such as GitHub, GitHub Enterprise, and Bitbucket. This new capability also allows you to utilize existing installations of Jenkins for orchestrating your ML pipelines.