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By now, most data leaders know that developing useful AI applications takes more than RAG pipelines and fine-tuned models it takes accurate, reliable, AI-ready data that you can trust in real-time. To borrow a well-worn idiom, when you put garbage data into your AI model, you get garbage results out of it. Of course, some level of data quality issues is an inevitabilityso, how bad is “bad” when it comes to data feeding your AI and ML models?
These models are free to use, can be fine-tuned, and offer enhanced privacy and security since they can run directly on your machine, and match the performance of proprietary solutions like o3-min and Gemini 2.0.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
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As privacy standards continue to evolve, businesses face a dual challenge: to uphold ethical standards for data use while seizing the opportunities offered by data collaboration. Enter data clean rooms: a privacy-enhancing solution that allows organizations to share valuable insights without compromising compliance.* If you're new to data clean rooms, our recent blog post Data Clean Rooms Explained: What You Need to Know About Privacy-First Collaboration breaks down the fundamentals.
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