Remove AI Development Remove Data Drift Remove ML
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AI Development Lifecycle Learnings of What Changed with LLMs

ODSC - Open Data Science

This problem often stems from inadequate user value, underwhelming performance, and an absence of robust best practices for building and deploying LLM tools as part of the AI development lifecycle. For instance: Data Preparation: GoogleSheets. Model Engineering: DVC (Data Version Control). Evaluation: Tools likeNotion.

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Snorkel Flow 2023.R3 release: PaLM integration, streamlined onboarding, and enhanced user experience

Snorkel AI

This new guided workflow is designed to ensure success for your AI use case, regardless of complexity, catering to both seasoned data scientists and those just beginning their journey. While creating your app, you’ll receive a preview of your dataset, allowing you to identify and correct critical data errors early.

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Snorkel Flow 2023.R3 release: PaLM integration, streamlined onboarding, and enhanced user experience

Snorkel AI

This new guided workflow is designed to ensure success for your AI use case, regardless of complexity, catering to both seasoned data scientists and those just beginning their journey. While creating your app, you’ll receive a preview of your dataset, allowing you to identify and correct critical data errors early.

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Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment

Snorkel AI

Snorkel AI and Google Cloud have partnered to help organizations successfully transform raw, unstructured data into actionable AI-powered systems. Snorkel Flow easily deploys on Google Cloud infrastructure, ingests data from Google Cloud data sources, and integrates with Google Cloud’s AI and Data Cloud services.

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Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment

Snorkel AI

Snorkel AI and Google Cloud have partnered to help organizations successfully transform raw, unstructured data into actionable AI-powered systems. Snorkel Flow easily deploys on Google Cloud infrastructure, ingests data from Google Cloud data sources, and integrates with Google Cloud’s AI and Data Cloud services.

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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

Building a machine learning (ML) pipeline can be a challenging and time-consuming endeavor. Inevitably concept and data drift over time cause degradation in a model’s performance. For an ML project to be successful, teams must build an end-to-end MLOps workflow that is scalable, auditable, and adaptable.

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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

Building a machine learning (ML) pipeline can be a challenging and time-consuming endeavor. Inevitably concept and data drift over time cause degradation in a model’s performance. For an ML project to be successful, teams must build an end-to-end MLOps workflow that is scalable, auditable, and adaptable.