Favorite Intelligent document processing (IDP) is a common use case for customers on AWS. You can utilize Amazon Comprehend and Amazon Textract for a variety of use cases ranging from document extraction, data classification, and entity extraction. One specific industry that uses IDP is insurance. They use IDP to automate data extraction for common

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Shared by AWS Machine Learning February 9, 2022

Favorite The AWS Machine Learning Operations (MLOps) framework is an iterative and repetitive process for evolving AI models over time. Like DevOps, practitioners gain efficiencies promoting their artifacts through various environments (such as quality assurance, integration, and production) for quality control. In parallel, customers rapidly adopt multi-account strategies through AWS

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Shared by AWS Machine Learning February 8, 2022

Favorite Many of our AWS customers provide research, analytics, and business intelligence as a service. This type of research and business intelligence enables their end customers to stay ahead of markets and competitors, identify growth opportunities, and address issues proactively. For example, some of our financial services sector customers do

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Shared by AWS Machine Learning February 7, 2022

Favorite Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications via a visual interface. Data preparation is a crucial step of the ML lifecycle, and Data Wrangler provides an end-to-end solution

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Shared by AWS Machine Learning February 2, 2022

Favorite Amazon SageMaker Data Wrangler makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. Previously, when you created a Data Wrangler data flow, you could choose different export options to easily integrate that data flow into your data

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Shared by AWS Machine Learning February 2, 2022

Favorite Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features

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Shared by AWS Machine Learning February 2, 2022

Favorite Technology trends and advancements in digital media in the past decade or so have resulted in the proliferation of text-based data. The potential benefits of mining this text to derive insights, both tactical and strategic, is enormous. This is called natural language processing (NLP). You can use NLP, for

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Shared by AWS Machine Learning February 2, 2022

Favorite Web browsers can be a single pane of glass for organizations to interact with their information—all of the tools can be viewed and accessed on one screen so that users don’t have to switch between applications and interfaces. For example, a customer call center might have several different applications

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Shared by AWS Machine Learning January 28, 2022

Favorite Starting today, you can use Amazon SageMaker Autopilot to tackle regression and classification tasks on large datasets up to 100 GB. Additionally, you can now provide your datasets in either CSV or Apache Parquet content types. Businesses are generating more data than ever. A corresponding demand is growing for

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Shared by AWS Machine Learning January 28, 2022

Favorite This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Sarvi Loloei, Machine Learning Engineer at Clearly. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post. A pioneer in online

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Shared by AWS Machine Learning January 27, 2022