MLOps foundation roadmap for enterprises with Amazon SageMaker

Favorite As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT,

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Shared by AWS Machine Learning June 24, 2022

How Cepsa used Amazon SageMaker and AWS Step Functions to industrialize their ML projects and operate their models at scale

Favorite This blog post is co-authored by Guillermo Ribeiro, Sr. Data Scientist at Cepsa. Machine learning (ML) has rapidly evolved from being a fashionable trend emerging from academic environments and innovation departments to becoming a key means to deliver value across businesses in every industry. This transition from experiments in

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Shared by AWS Machine Learning June 24, 2022

Import data from cross-account Amazon Redshift in Amazon SageMaker Data Wrangler for exploratory data analysis and data preparation

Favorite Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations with a multi-account architecture want to avoid situations where

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Shared by AWS Machine Learning June 23, 2022

Visual inspection automation using Amazon SageMaker JumpStart

Favorite According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we’re still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements

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Shared by AWS Machine Learning June 22, 2022

Identify mangrove forests using satellite image features using Amazon SageMaker Studio and Amazon SageMaker Autopilot – Part 1

Favorite The increasing ubiquity of satellite data over the last two decades is helping scientists observe and monitor the health of our constantly changing planet. By tracking specific regions of the Earth’s surface, scientists can observe how regions like forests, water bodies, or glaciers change over time. One such region

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Shared by AWS Machine Learning June 21, 2022

Identify mangrove forests using satellite image features using Amazon SageMaker Studio and Amazon SageMaker Autopilot – Part 2

Favorite Mangrove forests are an import part of a healthy ecosystem, and human activities are one of the major reasons for their gradual disappearance from coastlines around the world. Using a machine learning (ML) model to identify mangrove regions from a satellite image gives researchers an effective way to monitor

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Shared by AWS Machine Learning June 21, 2022