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AWS Announces Nine New Amazon SageMaker Capabilities

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Distributed Training on Amazon SageMaker delivers new capabilities that can train large models up to two times faster than would otherwise be possible with today's machine learning processors Inc. company, announced nine new capabilities for its industry-leading machine learning service, Amazon SageMaker, making it even easier for developers to automate and scale all steps of the end-to-end machine learning workflow. Today's announcements bring together powerful new capabilities like faster data preparation, a purpose-built repository for prepared data, workflow automation, greater transparency into training data to mitigate bias and explain predictions, distributed training capabilities to train large models up to two times faster, and model monitoring on edge devices. Machine learning is becoming more mainstream, but it is still evolving at a rapid clip. With all the attention machine learning has received, it seems like it should be simple to create machine learning models, but it isn't. In order to create a model, developers need to start with the highly manual process of preparing the data.


Automating machine learning lifecycle with AWS

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Machine Learning and data science life cycle involved several phases. Each phase requires complex tasks executed by different teams, as explained by Microsoft in this article. To solve the complexity of these tasks, cloud providers like Amazon, Microsoft, and Google services automate these tasks that speed up end to end the machine learning lifecycle. This article explains Amazon Web Services (AWS) cloud services used in different tasks in a machine learning life cycle. To better understand each service, I will write a brief description, a use case, and a link to the documentation. In this article, machine learning lifecycle can be replaced with data science lifecycle.


An Introduction to Amazon SageMaker

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Amazon SageMaker helps data scientists and inventors to prepare, make, train, and deploy high- quality machine learning models by bringing together a broad set of capabilities purpose- erected for machine learning. Amazon SageMaker make available a set of solutions for the most common use cases that may be deployed readily with just a few clicks to make it easier to grow started. Amazon SageMaker is a completely accomplished machine learning service. Data scientists and developers may speedily and easily build and train machine learning models with SageMaker. They can straight deploy them into a production-ready hosted environment.


Amazon launches new AI services for DevOps and business intelligence applications

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Amazon today launched SageMaker Data Wrangler, a new AWS service designed to speed up data prep for machine learning and AI applications. Alongside it, the company took the wraps off of SageMaker Feature Store, a purpose-built product for naming, organizing, finding, and sharing features, or the individual independent variables that act as inputs in a machine learning system. Beyond this, Amazon unveiled SageMaker Pipelines, which CEO Andy Jassy described as a CI/CD service for AI. And the company detailed DevOps Guru and QuickSight Q, offerings that uses machine learning to identify operational issues, provide business intelligence, and find answers to questions in knowledge stores, as well as new products on the contact center and industrial sides of Amazon's business. During a keynote at Amazon's re:Invent conference, Jassy said that Data Wrangler has over 300 built-in conversion transformation types.


Review: AWS AI and Machine Learning stacks up

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Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.