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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

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Snorkel Flow Spring 2023: warm starts and foundation models

Snorkel AI

Rapid, model-guided iteration with New Studio for all core ML tasks. Enhanced studio experience for all core ML tasks. If you want to see Snorkel Flow in action, sign up for a demo. Enhanced new studio experience Snorkel Flow now supports all ML tasks through a single interface via our new Snorkel Flow Studio experience.

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Simplifying the Image Classification Workflow with Lightning & Comet ML

Heartbeat

A guide to performing end-to-end computer vision projects with PyTorch-Lightning, Comet ML and Gradio Image by Freepik Computer vision is the buzzword at the moment. Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. For Problem type , select Classification. Then we train, build, test, and deploy the model using SageMaker Canvas, without writing any code. Choose Create.

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Snorkel Flow Summer 2023: faster, easier and more secure

Snorkel AI

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. This is especially helpful for classification across many classes, where users tend to write more LFs. Intelligent Auto-Suggest Strategies for Labeling Functions You can now target specific error hotspots using slice-based suggestions.