Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
Now you can easily build, train, and deploy machine learning (ML) models using Amazon Sagemaker. Developed by the world's leading public cloud provider, Amazon Sagemaker is the leading solution for creating ML models for any use case with fully-managed infrastructure, tools, and workflows. Powered by AWS, Sagemaker delivers high-performance, low-cost ML at scale.
Sagemaker is designed for the modern ML lifecycle, offering a wide breadth and depth of features. This includes built-in algorithms and automatic model tuning for hyperparameter optimization. Using Sagemaker, you can prepare, build, train and tune, and deploy and manage your ML solutions to address a wide range of applications – from monitoring models on edge devices to reducing costs with container instances.
- Built on Amazon's AI/ML innovation: Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices.
- Enable more people to innovate with ML: Sagemaker provides a wealth of tools for developing your machine learning models, including IDEs for data scientists and no-code interfaces for business analysts.
- Data-focused: ML requires data to be successful. With Sagemaker, you can access, label, and process large amounts of structured data (or tabular data) and unstructured data such as phone, video, and audio.
- Improve productivity with Amazon SageMaker Studio: The first fully integrated development environment (IDE) for machine learning, SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps – giving you complete access, control, and visibility into each step required to build, train, and deploy models.
- Experiment management and tracking: Machine learning is an iterative process based on continuous experimentation. Over time, the explosion of data has made it harder to track the best-performing models and the exact ingredients that went into creating those models in the first place. Amazon SageMaker Experiments helps you track, evaluate, and organize training experiments in an easy and scalable manner, and includes Amazon SageMaker Studio as well as a Python SDK with deep Jupyter integrations.
- Model monitoring: Machine learning models are typically trained and evaluated using historical data, but their quality degrades after they are deployed in production. The validity of prediction results can change over time and errors can be introduced upstream which can impact model quality. With Sagemaker, you can detect deviations in model quality to take the right corrective actions.
- Reduce training time: Take your ML deployment from hours to minutes with optimized infrastructure, and boost productivity by 10x with purpose-built tools.
- Automate MLOps: Sagemaker helps automate and standardize your MLOps practices across your organization, allowing you to build, train, deploy, and manage your models at scale.
Amazon Sagemaker is a fully-managed cloud service that lets you build, train, and deploy machine learning (ML) models.
- Built on two decades of Amazon innovation
- Tools for developing machine learning models
- IDEs for data scientists
- No-code interfaces for business analysts
- Handle large volumes of structured/unstructured data
- Build, train, and deploy with Sagemaker Studio
- Model monitoring
- Reduce training time and boost productivity
- Automate MLOps
AWS provides a wealth of accessible resources for Amazon Sagemaker. For more information and help, visit AWS Knowledge Center.
Amazon Sagemaker requires knowledge and experience to manage successful MLOps. Need help getting your ML modeling off the ground? Contact us and talk to one of our AWS engineers about your goals – and how we can help.