Webinar with Seqera and CytoReason: Trace any model back to its data.
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 is rarely mysterious. More often than not, bad outputs can be traced back to bad inputs: flawed data, unclear lineage, or uncontrolled environments. Smarter models won’t fix this on their […]
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, Sr. Principal Chief Architect, Field CTO Office at Red Hat, showed that AI sovereignty isn’t a policy debate but an infrastructure strategy. Every AI system depends on choices about data,
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 Unity Catalog feels like such a sharp turn. And just now, the Pattern Got Harder to Ignore. This Week at Databricks’ Data + AI Summit, the Pattern Got Harder to
Data agents are fast becoming the operating layer of enterprise AI – automating analysis, managing workflows, obtaining context, and acting across production systems. Headless agents are coming for your data, there’s no doubt about it. But while agent skills are improving at a breakneck pace, trust is still the biggest barrier to adoption. Denodo’s AI
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 without rewriting it around an SDK. It is also the fastest way to discover what object storage does not give you: no atomicity across files, thin audit trails, and configuration
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 they could. That work paid off, and it cleared the way for what’s happening now, everywhere, at breakneck speed: agents. Companies have found real uses for agents across the organization,
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, and poor reproducibility show up as delayed testing, rising AI infrastructure costs, and lower model reliability. To support faster iteration and production-ready MLOps operations, teams need efficient data management at
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 systems ingest data ranging from structured tables to unstructured text, graphics, and streams, the difficulty shifts from data collection to data integration. What differentiates experimental pipelines from production-grade systems is
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 walls: A Center of Excellence (CoE) for Enterprise AI solves this by standardizing how AI is built, validated, and deployed – so teams can move faster without losing control. But
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 how to operate such a system reliably at scale. This is where an AI Center of Excellence (AI CoE) comes in. It’s an organizational and technical response to that shift:
Based on my presentation at PyData Global 2025 When we – engineers – hear the word “compliance,” we tend to roll our eyes. We want to build features, not fill out forms. But here’s good news: the exact same tools that help you debug your code can also keep you out of trouble. In this
2025 was a defining year for lakeFS. Across open source and Enterprise editions, we shipped major capabilities that expanded lakeFS from a powerful data versioning layer into a control plane for AI-Ready Data – spanning structured and unstructured data, multiple public and private clouds, and a growing ecosystem of analytics and ML engines. Here’s our
We use cookies to improve your experience and understand how our site is used.
Join our community of experts:
introduce yourself, share your knowledge and discover best practices from fellow peers
