You could also rely on Amazon SageMaker AutoPilot, a game-changing AutoML capability. Alternatively, you can train a model using one of the built-in algorithms, or your own code written for a popular open source ML framework (TensorFlow, PyTorch, and Apache MXNet), or your own custom code packaged in a Docker container. You can visit AWS Marketplace, pick an algorithm or a model shared by one of our partners, and deploy it on SageMaker in just a few clicks. When it comes to building models, Amazon SageMaker gives you plenty of options. These results couldn’t have been realized without the technical experience, trust, and dedication of both teams to achieve one goal: an uninterrupted clean and safe water supply.” You can learn more in this video. By standardizing our ML workloads on AWS, we were able to reduce costs and prevent downtime while improving the quality of the water produced. Using Amazon SageMaker, we built a ML model that learns from previous patterns and predicts the future evolution of fouling indicators. Says Aude Giard, Chief Digital Officer at Veolia Water Technologies: “ In 8 short weeks, we worked with AWS to develop a prototype that anticipates when to clean or change water filtering membranes in our desalination plants. 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. In addition to resilient infrastructure and scalable model serving, you can also rely on Amazon SageMaker Model Monitor to catch prediction quality issues that could happen on your endpoints. 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. 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.įor 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. #1 – Build Secure and Reliable ML Models, FasterĪs 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. 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. 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 (, iFood), industry and manufacturing (Veolia, Formosa Plastics), gaming (Voodoo), customer relationship management (Zendesk, Freshworks), energy (Kinect Energy Group, Advanced Microgrid Systems), real estate (), satellite imagery (Digital Globe), human resources (ADP), and many more. 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. Thus, business opportunities can be explored, seized, and turned into industrial-grade products and services.Īs 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. Every day, they’re able to experiment, innovate, and deploy to production in less time and at lower cost than ever before. 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. Since 2006, Amazon Web Services (AWS) has been helping millions of customers build and manage their IT workloads.
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