AWS Machine Learning Blog

Category: Artificial Intelligence

Use Block Kit when integrating Amazon Lex bots with Slack

If you’re integrating your Amazon Lex chatbots with Slack, chances are you’ll come across Block Kit. Block Kit is a UI framework for Slack apps. Like response cards, Block Kit can help simplify interactions with your users. It offers flexibility to format your bot messages with blocks, buttons, check boxes, date pickers, time pickers, select […]

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Patterns for multi-account, hub-and-spoke Amazon SageMaker model registry

Data science workflows have to pass multiple stages as they progress from the experimentation to production pipeline. A common approach involves separate accounts dedicated to different phases of the AI/ML workflow (experimentation, development, and production). In addition, issues related to data access control may also mandate that workflows for different AI/ML applications be hosted on […]

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Deploy multiple serving containers on a single instance using Amazon SageMaker multi-container endpoints

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 SageMaker support for direct multi-container endpoints. […]

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Machine Learning at the Edge with AWS Outposts and Amazon SageMaker

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 some use-cases. With other critical use-cases, […]

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Getting started with Amazon SageMaker Feature Store

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 to vectorized representation, aggregating log data […]

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Run ML inference on AWS Snowball Edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

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 devices, deploying and managing ML models […]

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Run your TensorFlow job on Amazon SageMaker with a PyCharm IDE

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 of experimentation required before finalizing a […]

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How Cortica used Amazon HealthLake to get deeper insights to improve patient care

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 therapies for their children. To bring […]

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Attendee matchmaking at virtual events with Amazon Personalize

Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations—no ML expertise required. Amazon Personalize makes it easy for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing. Besides […]

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Accurately predicting future sales at Clearly using Amazon Forecast

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 Canada, the US, Australia and New […]

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