Latest posts
- Seven research papers push foundation model boundaries
- The recent debut of ChatGPT astounded the public with the power and speed of foundation models, but their enterprise use remains hampered by adaptation and deployment challenges. In the past year, Snorkel AI has researched several ways to overcome those challenges. ... - Snorkel AI Partners with Advanced Analytics Consultancy Aimpoint Digital
- Snorkel AI is delighted to announce a partnership with Aimpoint Digital, a premier analytics firm specializing in AI application development that builds, operationalizes, and scales data science solutions for biopharma, manufacturing, retail, and other major industries. Aimpoint Digital leads the industry in solving complex challenges and exploiting value-generating opportunities for… ... - Supercharge data scientist and domain expert collaboration with Comments and Tags in Snorkel Flow
- Labeling data manually can be a grind. Snorkel Flow slashes labeling time from months to minutes by allowing data scientists and domain experts collaborate through labeling functions. Snorkel Flow offers two unique capabilities that further supercharge that collaboration: Comments and Tags. ... - Snorkel AI Team presents research at NeurIPS 2022
- The Snorkel AI team will present five research papers advancing weak supervision and programmatic labeling at the NeurIPS 2022 conference that started this week. ... - Deepening Snorkel AI’s partnership with Microsoft Azure AI
- Snorkel AI is excited to build on our partnership with Microsoft Azure to help enterprises and government agencies solve their most impactful problems and unlock value from their data using AI. Learn how Azure customers can easily deploy Snorkel Flow on their Azure cloud infrastructure to accelerate AI application development… ... - Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI
- Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow New Snorkel Flow capabilities for Data-centric Foundation Model Development bridge the adaptation and deployment gaps between foundation models and enterprise AI. Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the… ... - Better not bigger: How to get GPT-3 quality at 0.1% the cost - We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise… ...
- What can Data-Centric AI learn from data & ML engineering?
- Databricks' Chief Technologist: Data-Centric AI can learn from Data Engineering and ML Engineering in five ways: continuous updates, versioning, code-centric deployment, data privatization and actionable monitoring. ...
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