Since 2006, Amazon Web Services (AWS) has been helping millions of customers build and manage their IT workloads. From startups to large enterprises to public sector, organizations of all sizes use our cloud computing services to reach unprecedented levels of security, resiliency, and scalability. Every day, they’re able to experiment, innovate, and deploy to production in less time and at lower cost than ever before. Thus, business opportunities can be explored, seized, and turned into industrial-grade products and services.
As Machine Learning (ML) became a growing priority for our customers, they asked us to build an ML service infused with the same agility and robustness. The result was Amazon SageMaker, a fully managed service launched at AWS re:Invent 2017 that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly.
Today, Amazon SageMaker is helping tens of thousands of customers in all industry segments build, train and deploy high quality models in production: financial services (Euler Hermes, Intuit, Slice Labs, Nerdwallet, Root Insurance, Coinbase, NuData Security, Siemens Financial Services), healthcare (GE Healthcare, Cerner, Roche, Celgene, Zocdoc), news and media (Dow Jones, Thomson Reuters, ProQuest, SmartNews, Frame.io, Sportograf), sports (Formula 1, Bundesliga, Olympique de Marseille, NFL, Guiness Six Nations Rugby), retail (Zalando, Zappos, Fabulyst), automotive (Atlas Van Lines, Edmunds, Regit), dating (Tinder), hospitality (Hotels.com, iFood), industry and manufacturing (Veolia, Formosa Plastics), gaming (Voodoo), customer relationship management (Zendesk, Freshworks), energy (Kinect Energy Group, Advanced Microgrid Systems), real estate (Realtor.com), satellite imagery (Digital Globe), human resources (ADP), and many more.
When we asked our customers why they decided to standardize their ML workloads on Amazon SageMaker, the most common answer was: “SageMaker removes the undifferentiated heavy lifting from each step of the ML process.” Zooming in, we identified five areas where SageMaker helps them most.
#1 – Build Secure and Reliable ML Models, Faster
As many ML models are used to serve real-time predictions to business applications and end users, making sure that they stay available and fast is of paramount importance. This is why Amazon SageMaker endpoints have built-in support for load balancing across multiple AWS Availability Zones, as well as built-in Auto Scaling to dynamically adjust the number of provisioned instances according to incoming traffic.
For even more robustness and scalability, Amazon SageMaker relies on production-grade open source model servers such as TensorFlow Serving, the Multi-Model Server, and TorchServe. A collaboration between AWS and Facebook, TorchServe is available as part of the PyTorch project, and makes it easy to deploy trained models at scale without having to write custom code.
In addition to resilient infrastructure and scalable model serving, you can also rely on Amazon SageMaker Model Monitorto catch prediction quality issues that could happen on your endpoints. By saving incoming requests as well as outgoing predictions, and by comparing them to a baseline built from a training set, you can quickly identify and fix problems like missing features or data drift.
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