Favorite Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with

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Shared by AWS Machine Learning August 18, 2021

Favorite Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business metric such as revenue, profit margin, video watch time, or any other numerical attribute of your item catalog

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Shared by AWS Machine Learning August 18, 2021

Favorite As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for

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Shared by AWS Machine Learning August 17, 2021

Favorite Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models built on different frameworks. SageMaker real-time inference endpoints are fully managed and can serve predictions in real time with low latency. This post introduces

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Shared by AWS Machine Learning August 17, 2021

Favorite You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these

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Shared by AWS Machine Learning August 12, 2021

Favorite In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values

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Shared by AWS Machine Learning August 12, 2021

Favorite This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica. Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental differences. Cortica was founded to fix the fragmented journey families typically navigate while seeking diagnoses and

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Shared by AWS Machine Learning August 11, 2021

Favorite As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount

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Shared by AWS Machine Learning August 11, 2021

Favorite Amazon Comprehend is a natural language processing (NLP) service that provides APIs to extract key phrases, contextual entities, events, sentiment from unstructured text, and more. Entities refer to things in your document such as people, places, organizations, credit card numbers, and so on. But what if you want to

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Shared by AWS Machine Learning August 10, 2021

Favorite This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Alex Thoreux, Web Analyst at Clearly. A pioneer in online shopping, Clearly launched their first site in 2000. Since then, they’ve grown to become one of the biggest online eyewear retailers in the world, providing customers across

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Shared by AWS Machine Learning August 10, 2021