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Snorkel AI
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Snorkel AI
@SnorkelAI
Frontier AI Data Lab advancing AI through better data
- #SnorkelAI was recognized on @Forbes as a top game-changing tech with our programmatic data labeling approach in #SnorkelFlow ↓ forbes.com/sites/forbeste…
- [1/5] **Spoiler alert** We trained a model with the same accuracy as GPT-3 (fine-tuned) that was 1400x smaller with 0.1% of the inference cost. How? With Data-centric Foundation Model (FM) Development in Snorkel Flow. Highlights in the thread 👇:
- In case you missed it, our #MachineLearning Engineer, @aarti_bagul, spoke with @jaygshah22 on building platforms for AI applications. Check out the exciting conversation ↓ snkl.ai/j0h
- We are delighted to announce our $85 million Series C at a $1 billion valuation to accelerate #DataCentricAI, with funding by @BlackRock, Addition Capital, @lightspeedvp, @GreylockVC, @googleventures, and more. Read more in @Fortune ↓
- Very excited to announce Snorkel v0.9, the biggest update to our open source framework for programmatically labeling, transforming & structuring training datasets for #ML. We add new core ops, algs, tutorials, and a full redesign of the core lib snorkel.org/hello-world-v-… #snorkelML
- This week we brought the #AI community together to share transformative ideas, practical applications, and new research on #DataCentricAI. If you weren't able to join us or want to view the insightful talks again, check out ↓ snkl.ai/dcai21
- How to Use Snorkel to Build AI Applications: The why, what, and how of Snorkel’s programmatic data labeling approach and the state-of-the-art #SnorkelFlow platform by our Head of Technology and Co-founder, @bradenjhancock ↓ snkl.ai/hts
- A big part of the ML workflow is in debugging. However, debugging for ML is hard! In this post, @chipro analyzes major sources of errors & their solutions at the four steps: * labeling * feature engineering * model training * model evaluation
- We're excited to announce Snorkel Flow, a new data-first ML development platform based on the core ideas of Snorkel! After years of research, deployments, and user conversations, we saw that Snorkel was just the first step- read about our path forward here
- We are starting a new vodcast called Snorkel #ScienceTalks, exploring some of the best ideas to make AI practical. In the 1st episode, @bradenjhancock talks to @Thom_Wolf about @huggingface Datasets & Transformers, and taking ML research into production. snorkel.ai/the-scientist-…
- Most organizations are in early phases of machine learning adoption, and there are many misperceptions of ML production. @chipro explained the 6 common myths in her recent talk at Stanford MLSys Seminar. What other myths have you encountered? snorkel.ai/machine-learni…
- In case you missed our #MLWhiteboard, where @krandiash reviewed two exciting papers on defining and building malleable #machinelearning systems, check it out ↓ snkl.ai/bms
- [1/5] Today, we’re excited to introduce Data-centric Foundation Model Development, a new paradigm for enterprises to use foundation models to solve complex, real-world problems. snorkel.ai/data-centric-f…
