Latest posts
- Data labeling: a practical guide (2023) - Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here's a handy guide. ...
- How AI is powering the next generation of trade surveillance - Regulators and compliance officers face a constantly evolving landscape of financial markets. Rule-based systems struggle where AI succeeds. ...
- How AI speeds patient classification and recruitment in clinical trials - The medical industry is exploding with data. Manually labeling data for clinical trials is a challenge. Fortunately, AI can help. ...
- Which is better, retrieval augmentation (RAG) or fine-tuning? Both. - Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.” ...
- Former U.S. Chief Data Scientist on past and future of data science - Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.” ...
- How we matured our ML-on-Kubernetes capabilities and saved on cloud costs - We designed, implemented, and rolled out a multi-faceted autoscaling solution that expands our ML capabilities while saving on cloud costs. ...
- 4 new papers show foundation models can build on themselves - The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves. ...
- How GPT helped expand our marketing team’s capacity - GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets. ...
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