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The now-infamous “pizza with glue” AI result is a symptom of something deeper than one bizarre edge case. When AI systems fail, the root cause
- Oz Katz
AI raises a question most organizations haven’t answered yet: who really controls the foundation? In a recent presentation at the AI-Ready Data Summit, Matthew Miller,
- Iddo Avneri
Databricks built its reputation on openness. Spark. Delta Lake. MLflow. A company that rose by betting on open ecosystems over proprietary silos. Which is why
- Oz Katz
Data agents are fast becoming the operating layer of enterprise AI – automating analysis, managing workflows, obtaining context, and acting across production systems. Headless agents
- Tal Sofer
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
