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First presented at the AI-Ready Data Summit, this talk tackled the part of AI-ready data that tooling alone can’t solve: getting busy people to actually
- Joe Pringle
Mounting object storage as a filesystem is the fastest way to get a notebook or Spark job reading S3, Azure Data Lake Storage, or GCS
- Oz Katz
For the past few years, the hard work in AI has gone into models. Organizations spent that time learning, experimenting, and building the best models
- Gottfried Sehringer
AI is moving fast in life sciences. GxP is not. The teams that close that gap first get treatments to market faster. Pharma, biotech, and
- Vince Antinozzi
As AI systems scale, data bottlenecks for AI projects quickly become one of the key barriers to model development and deployment. Slow pipelines, inconsistent datasets,
- Idan Novogroder
AI-ready data is often misunderstood, dismissed as just another layer of hype on top of familiar practices like data quality. But that assumption misses something
- Einat Orr, PhD
Most organizations today are experimenting with AI, but few have built the systems needed to make AI repeatable, scalable, and genuinely useful in production. That’s
- Gottfried Sehringer
The lakeFS Control Plane for AI-ready Data provides agents that rely on large, multimodal datasets, isolated access, verifiable results and built-in governance. TL;DR A new
- Oz Katz
Unless you’ve been living under a rock, you’ve probably heard of multimodal data and its integration, now a standard feature of modern data platforms. As
- Tal Sofer
AI projects often end up failing due to data, not models. Inconsistent inputs, poor data quality, a lack of lineage, and fragmented workflows subtly weaken
- Idan Novogroder
Scaling AI isn’t about building better models; it’s about building the system around them. Without consistency in data, workflows and governance, teams hit the same
- Einat Orr, PhD
