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Customizing sk-learn Models and Pipelines

Towards AI

One reason for rephrasing a regression problem into a classification problem could be that the user wants to focus on a specific price range and requires a model that can predict this range with high accuracy. Demo In this section, I show how the pricing pipeline is initialized, trained, and used to predict price categories.

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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.

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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.

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Simplifying the Image Classification Workflow with Lightning & Comet ML

Heartbeat

Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries. Image Classification for Cancer Detection As we all know, cancer is a complex and common disease that affects millions of people worldwide. This architecture is often used for image classification.

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Building better datasets with Snorkel Flow error analysis

Snorkel AI

If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs. Streamlined tagging workflows.

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Building better datasets with Snorkel Flow error analysis

Snorkel AI

If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs. Streamlined tagging workflows.

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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

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. In our case, we chose to use a float[] as the input type and the built-in DJL classifications as the output type.

ML 75