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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
Building infrastructure for AI-ready data is a technical challenge, that’s for sure. But it’s also a strategic imperative for organizations looking to scale AI across
- Tal Sofer
Modern data systems are subject to constant change – new data arrives, pipelines evolve, and tables get updated many times a day. Without the ability
- Itai Gilo
As AI adoption evolves and teams advance from scattered ML trial projects to running AI as a production system, they inevitably face the question of
- Einat Orr, PhD
Modern data operations call for more than just lightning-fast queries and scalable storage. Safety, reproducibility, and control are all key parts of the equation. As
- Itai Gilo
You probably heard the saying “Garbage in, garbage out.” It holds true for any data system but properly prepared and handled data is especially critical
- Tal Sofer
The AI agent revolution is here. Coding agents like Claude Code, Cursor, and Codex are writing production software. Infrastructure agents are provisioning cloud resources. Data
- Oz Katz
Free Virtual Event for Enterprise AI Leaders Building AI that works in production is hard. For most enterprise teams, the biggest obstacle isn’t the model,
- Gottfried Sehringer
AI systems increasingly influence decisions made in financial services, healthcare, and public services. This means teams need to be able to demonstrate that their models
- Idan Novogroder
Based on my presentation at PyData Global 2025. My colleague Yoav recently wrote about why reproducibility matters so much in healthcare AI and how data
- Joe Pringle
As data quantities increase and pipelines become more complex, understanding where data originated, how it changed, and how it is used becomes increasingly important. This
- Tal Sofer
