AWS Celebrates 5 Years of Amazon SageMaker

Oct. 26, 2022 — In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions.

The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries now have access to its transformational benefits. To harness this opportunity and take ML out of the research lab and into the hands of organizations, AWS created Amazon SageMaker. This year, we celebrate the 5-year anniversary of Amazon SageMaker, Amazon’s flagship fully managed ML service, which was launched at AWS re:Invent 2017 and went on to become one of the fastest-growing services in AWS history.

AWS launched Amazon SageMaker to break down barriers to ML and democratize access to cutting-edge technology. Today, that success might have seemed inevitable, but in 2017, ML still required specialized skills typically possessed by a limited group of developers, researchers, PhDs, or companies that built their business around ML. Previously, developers and data scientists had to first visualize, transform, and preprocess data into formats that algorithms could use to train models, which required massive amounts of compute power, lengthy training periods, and dedicated teams to manage environments that often spanned multiple GPU-enabled servers—and a healthy amount of manual performance tuning. Additionally, deploying a trained model within an application required a different set of specialized skills in application design and distributed systems. As datasets and variables grew, companies had to repeat this process to learn and evolve from new information as older models became outdated. These challenges and barriers meant ML was out of reach to most except for well-funded organizations and research institutions.

The Dawn of a New Era in ML

That’s why AWS introduced Amazon SageMaker, its flagship ML managed service that enables developers, data scientists, and business analysts to quickly and easily prepare data, and build, train, and deploy high-quality ML models at scale. In the past 5 years, AWS has added more than 250 new features and capabilities, including the world’s first integrated development environment (IDE) for ML, debuggers, model monitors, profilers, AutoML, a feature store, no-code capabilities, and the first purpose-built continuous integration and continuous delivery (CI/CD) tool to make ML less complex and more scalable in the cloud and on edge devices.

In 2021, AWS pushed democratization even further to put ML within reach of more users. Amazon SageMaker enables more groups of people to create ML models, including the no-code environment in Amazon SageMaker Canvas for business analysts without ML experience, as well as a no-setup, no-charge ML environment for students to learn and experiment with ML faster.

Today, customers can innovate with Amazon SageMaker through a choice of tools—IDEs for data scientists and a no-code interface for business analysts. They can access, label, and process large amounts of structured data (tabular data) and unstructured data (photo, video, and audio) for ML. With Amazon SageMaker, customers can reduce training times from hours to minutes with optimized infrastructure. Finally, customers you can automate and standardize machine learning operations (MLOps) practices across their your organization to build, train, deploy, and manage models at scale.

New Features for the Next Generation of Innovation

Moving forward, AWS continues to aggressively develop new features that can help customers take ML further. For example, Amazon SageMaker multi-model endpoints (MMEs) allows customers to deploy thousands of ML models on a single Amazon SageMaker endpoint and lower costs by sharing instances provisioned behind an endpoint across all the models. Until recently, MMEs were supported only on CPUs, but, Amazon SageMaker MMEs now support GPUs. Customers can use Amazon SageMaker MME to deploy deep learning models on GPU instances and save up to 90% of the cost by deploying thousands of deep learning models to a single multi-model endpoint. Amazon SageMaker has also expanded support for compute-optimized Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS Graviton 2 and Graviton 3 processors, which are well suited for CPU-based ML inference, so customers can deploy models on the optimal instance type for their workloads.

To get started with Amazon SageMaker, visit aws.amazon.com/sagemaker.

Source: AWS