Create, automate, and manage end-to-end ML workflows at scale including massive data volumes, thousands of training experiments, and hundreds of model versions
Automate the different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment
Compose, manage, and reuse ML workflows
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly.
Choose the best models for deploying into production
Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK.
Automatic tracking of models
Amazon SageMaker Pipelines logs every step of your workflow, creating an audit trail of model components such as training data, platform configurations, model parameters, and learning gradients. Audit trails can be used to recreate models and help support compliance requirements.
Bring CI/CD to machine learning
Amazon SageMaker Pipelines brings CI/CD practices to machine learning, such as maintaining parity between development and production environments, version control, on-demand testing, and end-to-end automation, helping you scale ML throughout your organization.
“At iFood, we strive to delight our customers through our services using technology such as machine learning (ML). … Building a complete and seamless workflow to develop, train, and deploy models has been a critical part of our journey to scale ML. Amazon SageMaker Pipelines helps us to quickly build multiple scalable automated ML workflows, and makes it easy to deploy and manage our models effectively. SageMaker Pipelines enables us to be more efficient with our development cycle. We continue to emphasize our leadership in using AI/ML to deliver superior customer service and efficiency with all these new capabilities of Amazon SageMaker.”
Sandor Caetano, Chief Data Scientist, iFood
“At INVISTA, we are driven by transformation and look to develop products and technologies that benefit customers around the globe. We see machine learning as a way to improve the customer experience, but with datasets that span hundreds of millions of rows, we needed a solution to help us prepare data, and develop, deploy, and manage ML models at scale. ... We can easily automate and manage ML workflows at scale with Amazon SageMaker Pipelines, so we can easily stitch together individual steps of the ML workflow… With Amazon SageMaker Pipelines, we can operationalize our ML workflows faster.”
Caleb Wilkinson, Lead Data Scientist - INVISTA
“A strong care industry where supply matches demand is essential for economic growth from the individual family up to the nation’s GDP. We’re excited about Amazon SageMaker Pipelines, as we believe it will help us scale better across our data science and development teams, by using a consistent set of curated data that we can use to build scalable end-to-end machine learning (ML) model pipelines from data preparation to deployment. With the newly announced capabilities of Amazon SageMaker, we can accelerate development and deployment of our ML models for different applications, helping our customers make better informed decisions through faster real-time recommendations.”
Clemens Tummeltshammer, Data Science Manager, Care.com
“Using ML, 3M is improving tried-and-tested products, like sandpaper, and driving innovation in several other spaces, including healthcare. As we plan to scale machine learning to more areas of 3M, we see the amount of data and models growing rapidly – doubling every year. We are enthusiastic about the new SageMaker features because they will help us scale. Amazon SageMaker Data Wrangler makes it much easier to prepare data for model training, and Amazon SageMaker Feature Store will eliminate the need to create the same model features over and over. Finally, Amazon SageMaker Pipelines will help us automate data prep, model building, and model deployment into an end to end workflow so we can speed time to market for our models. Our researchers are looking forward to the taking advantage of the new speed of science at 3M.”
David Frazee, Technical Director at 3M Corporate Systems Research Lab