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10 Cool Cloud AI And ML Services You Need To Know About

CRN takes a look at some cool cloud AI and ML services from the top cloud computing vendors, startups and other providers.

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Amazon SageMaker

Amazon SageMaker is Amazon Web Services’ (AWS) flagship, fully managed ML service that data scientists and developers can use to quickly build and train ML models and deploy them into production-ready hosted environments.

Introduced in November 2017, SageMaker is one of the fastest growing services in AWS history, with tens of thousands of active, external customers using it each month, according to the company.

SageMaker includes purpose-built tools for each step of the ML development lifecycle, including labeling, data preparation, feature engineering, statistical bias detection, auto ML, training, running, hosting, explainability, monitoring and workflows. It provides an integrated Jupyter authoring notebook instance for easy access to data sources for exploration and analysis, common ML algorithms optimized to run efficiently against extremely large data in distributed environments, and native support for bring-your-own algorithms and frameworks for flexible distributed training options. SageMaker supports TensorFlow, PyTorch, MXNet and Hugging Face.

AWS has launched more than 50 new SageMaker capabilities in the last year. At its re:Invent conference in December, AWS unveiled SageMaker Feature Store, a fully managed repository to store and share ML features, which are the attributes or properties models use during training and inference to make predictions; SageMaker Clarify to help developers detect bias in ML models and understand model prediction; and SageMaker Pipelines, described as the first purpose-built CI/CD service for ML. It also introduced Amazon SageMaker Data Wrangler, a SageMaker Studio feature that provides an end-to-end solution to import, prepare, transform, feature engineer and analyze data; and SageMaker Edge Manager, which provides model management for edge devices, allowing users to optimize, secure, monitor and maintain ML models on fleets of edge devices such as smart cameras, robots, personal computers and mobile devices.

 
 
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