Software:Amazon SageMaker

From HandWiki
Short description: Cloud machine-learning platform
Amazon SageMaker
Developer(s)Amazon, Amazon Web Services
Initial release29 November 2017; 6 years ago (2017-11-29)
TypeSoftware as a service
Websiteaws.amazon.com/sagemaker

Amazon SageMaker is a cloud based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud.[1] It can be used to deploy ML models on embedded systems and edge-devices.[2][3] SageMaker was launched in November 2017.[4]

Capabilities

SageMaker enables developers to operate at a number of levels of abstraction when training and deploying machine learning models. At its highest level of abstraction, SageMaker provides pre-trained ML models that can be deployed as-is.[5] In addition, SageMaker provides a number of built-in ML algorithms that developers can train on their own data.[6][7] Further, SageMaker provides managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch.[8] Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage,[9] AWS Batch for offline batch processing,[9][10] or Amazon Kinesis for real-time processing.[11]

Development interfaces

A number of interfaces are available for developers to interact with SageMaker. First, there is a web API that remotely controls a SageMaker server instance.[12] While the web API is agnostic to the programming language used by the developer, Amazon provides SageMaker API bindings for a number of languages, including Python, JavaScript, Ruby, Java, and Go.[13][14] In addition, SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and other applications.[15][16]

History and features

  • 2017-11-29: SageMaker is launched at the AWS re:Invent conference.[4][6][1]
  • 2018-02-27: Managed TensorFlow and MXNet deep neural network training and inference are now supported within SageMaker.[17][8]
  • 2018-02-28: SageMaker automatically scales model inference to multiple server instances.[18][19]
  • 2018-07-13: SageMaker adds support for recurrent neural network training, word2vec training, multi-class linear learner training, and distributed deep neural network training in Chainer with Layer-wise Adaptive Rate Scaling (LARS).[20][7]
  • 2018-07-17: AWS Batch Transform enables high-throughput non-realtime machine learning inference in SageMaker.[21][22]
  • 2018-11-08: Support for training and inference of Object2Vec word embeddings.[23][24]
  • 2018-11-27: SageMaker Ground Truth "makes it much easier for developers to label their data using human annotators through Mechanical Turk, third-party vendors, or their own employees."[25][2]
  • 2018-11-28: SageMaker Reinforcement Learning (RL) "enables developers and data scientists to quickly and easily develop reinforcement learning models at scale."[26][2]
  • 2018-11-28: SageMaker Neo enables deep neural network models to be deployed from SageMaker to edge-devices such as smartphones and smart cameras.[27][2]
  • 2018-11-29: The AWS Marketplace for SageMaker is launched. The AWS Marketplace enables 3rd-party developers to buy and sell machine learning models that can be trained and deployed in SageMaker.[28]
  • 2019-01-27: SageMaker Neo is released as open-source software.[29]

Notable Customers

  • NASCAR is using SageMaker to train deep neural networks on 70 years of video data.[30]
  • Carsales.com uses SageMaker to train and deploy machine learning models to analyze and approve automotive classified ad listings.[31]
  • Avis Budget Group and Slalom Consulting are using SageMaker to develop "a practical on-site solution that could address the over and under utilization of cars in real-time using an optimization engine built in Amazon SageMaker."[32]
  • Volkswagen Group uses SageMaker to develop and deploy machine learning in its manufacturing plants.[33]
  • Peak and Footasylum use SageMaker in a recommendation engine for footwear.[34]

Awards

In 2019, CIOL named SageMaker one of the "5 Best Machine Learning Platforms For Developers," alongside IBM Watson, Microsoft Azure Machine Learning, Apache PredictionIO, and AiONE.[35]

See also

References

  1. ↑ 1.0 1.1 Woodie, Alex (2017-11-29). "AWS Takes the 'Muck' Out of ML with SageMaker". datanami. https://www.datanami.com/2017/11/29/aws-takes-muck-ml-sagemaker. 
  2. ↑ 2.0 2.1 2.2 2.3 Rodriguez, Jesus (2018-11-30). "With These New Additions, AWS SageMaker is Starting to Look More Real for Data Scientists". Towards Data Science. https://towardsdatascience.com/with-these-new-additions-aws-sagemaker-is-starting-to-look-more-real-b60f95bcbc38. [yes|permanent dead link|dead link}}]
  3. ↑ Terdiman, Daniel (2018-10-05). "How AI is helping Amazon become a trillion-dollar company". Fast Company. https://www.fastcompany.com/90246028/how-ai-is-helping-amazon-become-a-trillion-dollar-company. 
  4. ↑ 4.0 4.1 Miller, Ron (2017-11-29). "AWS releases SageMaker to make it easier to build and deploy machine learning models". TechCrunch. https://techcrunch.com/2017/11/29/aws-releases-sagemaker-to-make-it-easier-to-build-and-deploy-machine-learning-models/. 
  5. ↑ Ponnapalli, Priya (2019-01-30). "Deploy trained Keras or TensorFlow models using Amazon SageMaker". AWS. https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/. 
  6. ↑ 6.0 6.1 "Introducing Amazon SageMaker". AWS. 2017-11-29. https://aws.amazon.com/about-aws/whats-new/2017/11/introducing-amazon-sagemaker/. 
  7. ↑ 7.0 7.1 Nagel, Becky (2018-07-16). "Amazon Updates SageMaker ML Platform Algorithms, Frameworks". Pure AI. https://pureai.com/articles/2018/07/16/aws-sagemaker-update.aspx. 
  8. ↑ 8.0 8.1 Roumeliotis, Rachel (2018-03-07). "How to jump start your deep learning skills using Apache MXNet". O'Reilly. https://www.oreilly.com/ideas/how-to-jump-start-your-deep-learning-skills-using-apache-mxnet. 
  9. ↑ 9.0 9.1 Marquez, Ernesto. "Evaluate when to use added AWS Step Functions actions". TechTarget. https://searchaws.techtarget.com/tip/Evaluate-when-to-use-added-AWS-Step-Functions-actions. 
  10. ↑ "AWS Step Functions Adds Eight More Service Integrations". AWS. 2018-11-29. https://aws.amazon.com/about-aws/whats-new/2018/11/aws-step-functions-adds-eight-more-service-integrations. 
  11. ↑ "Deploy Amazon SageMaker and a Data Lake on AWS for Predictive Data Science with New Quick Start". AWS. 2018-08-15. https://aws.amazon.com/about-aws/whats-new/2018/08/deploy-sagemaker-and-a-data-lake-on-aws-with-new-quick-start. 
  12. ↑ Olsen, Rumi (2018-07-19). "Call an Amazon SageMaker model endpoint using Amazon API Gateway and AWS Lambda". AWS. https://aws.amazon.com/blogs/machine-learning/call-an-amazon-sagemaker-model-endpoint-using-amazon-api-gateway-and-aws-lambda/. 
  13. ↑ "Amazon SageMaker developer resources". https://aws.amazon.com/sagemaker/developer-resources. 
  14. ↑ Wiggers, Kyle (2018-11-21). "Amazon updates SageMaker with new built-in algorithms and Git integration". https://venturebeat.com/2018/11/21/amazon-updates-sagemaker-with-new-built-in-algorithms-and-git-integration. 
  15. ↑ "Use Notebook Instances". https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html. 
  16. ↑ Gift, Noah (2018-08-17). "Here Come The Notebooks". Forbes. https://www.forbes.com/sites/forbestechcouncil/2018/08/17/here-come-the-notebooks/#6ec3e3b77609. 
  17. ↑ "Amazon SageMaker now supports TensorFlow 1.5, Apache MXNet 1.0, and CUDA 9 for P3 Instance Optimization". AWS. 2018-02-27. https://aws.amazon.com/about-aws/whats-new/2018/02/amazon-sagemaker-now-supports-tensorflow-1-5--apache-mxnet-1-0--and-cuda-9-for-p3-instance-optimization/. 
  18. ↑ "Auto Scaling in Amazon SageMaker is now Available". AWS. 2018-02-28. https://aws.amazon.com/about-aws/whats-new/2018/02/auto-scaling-in-amazon-sagemaker-is-now-available/. 
  19. ↑ "Amazon Sagemaker Now Uses Auto-scaling". Polar Seven. 2018-03-24. https://polarseven.com/amazon-sagemaker-now-uses-auto-scaling. 
  20. ↑ "Amazon SageMaker Announces Several Enhancements to Built-in Algorithms and Frameworks". AWS. 2018-07-13. https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-announces-enhancements-for-built-in-algorithms-and-frameworks/. 
  21. ↑ "Amazon SageMaker Now Supports High Throughput Batch Transform Jobs for Non-Real Time Inferencing". AWS. 2018-07-17. https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-supports-high-throughput-batch-transform-jobs-for-non-real-time-inferencing. 
  22. ↑ Simon, Julien (2019-01-24). "Making the most of your Machine Learning budget on Amazon SageMaker". Medium. https://medium.com/@julsimon/making-the-most-of-your-machine-learning-budget-on-amazon-sagemaker-a6982bdd5edd. 
  23. ↑ "Introduction to Amazon SageMaker Object2Vec". AWS. 2018-11-08. https://aws.amazon.com/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/. 
  24. ↑ "Amazon SageMaker Now Supports Object2Vec and IP Insights Built-in Algorithms". AWS. 2018-11-19. https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-now-supports-object2vec-and-ip-insights-built-i. 
  25. ↑ "Introducing Amazon SageMaker Ground Truth - Build Highly Accurate Training Datasets Using Machine Learning". AWS. 2018-11-28. https://aws.amazon.com/about-aws/whats-new/2018/11/introducing-amazon-sagemaker-groundtruth. 
  26. ↑ "Introducing Reinforcement Learning Support with Amazon SageMaker RL". AWS. 2018-11-28. https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-announces-support-for-reinforcement-learning. 
  27. ↑ "Introducing Amazon SageMaker Neo - Train Once, Run Anywhere with up to 2x in Performance Improvement". AWS. 2018-11-28. https://aws.amazon.com/about-aws/whats-new/2018/11/introducing-amazon-sagemaker-neo. 
  28. ↑ Robuck, Mike (2018-11-29). "AWS goes deep and wide with machine learning services and capabilities". FierceTelecom. https://www.fiercetelecom.com/telecom/aws-goes-deep-and-wide-machine-learning-services-and-capabilities. 
  29. ↑ Janakiram, MSV (2019-01-27). "Amazon Open Sources SageMaker Neo To Run Machine Learning Models At The Edge". Forbes. https://www.forbes.com/sites/janakirammsv/2019/01/27/amazon-open-sources-sagemaker-neo-to-run-machine-learning-models-at-the-edge/#260498d14d03. 
  30. ↑ Digman, Larry (2019-06-04). "NASCAR to migrate 18 petabytes of video archives to AWS". ZDNet. https://www.zdnet.com/article/nascar-to-migrate-18-petabytes-of-video-archives-to-aws/. 
  31. ↑ Crozier, Ry (2019-05-02). "Carsales builds Tessa AI to check vehicle ads". IT News. https://www.itnews.com.au/news/carsales-builds-tessa-ai-to-check-vehicle-ads-524583. 
  32. ↑ "Avis Budget Group and Slalom Further Digitize the Car Rental Process with Machine Learning on AWS". AWS. 2019-05-31. https://aws.amazon.com/about-aws/whats-new/2019/05/avis-slalom-machine-learning-on-aws. 
  33. ↑ "Volkswagen and AWS Join Forces to Transform Automotive Manufacturing". Metrology News. 2019-05-24. https://metrology.news/volkswagen-and-aws-join-forces-to-transform-automotive-manufacturing/. 
  34. ↑ Mari, Angelica (2019-05-14). "Footasylum steps up artificial intelligence to drive customer centricity". Computer Weekly. https://www.computerweekly.com/news/252463301/Footasylum-steps-up-artificial-intelligence-to-drive-customer-centricity. 
  35. ↑ Pandey, Ashok (2019-02-21). "5 Best Machine Learning Platforms For Developers". CIOL. https://www.ciol.com/5-best-machine-learning-platforms-developers. 




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