WEBINAR: Agents are already in your data. Find out how to stay in control.
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,
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, it’s the data behind it. Study after study shows that organizations abandon AI projects due to poor data quality and inadequate data infrastructure. The gap between AI ambition and AI
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 is why modern data systems generate more than just data – they also produce data denoting context: metadata. If teams don’t track metadata, they risk losing context that makes their
TL;DR CytoReason is a technology company transforming biopharma’s decision-making—from trial and error to data-driven—through its AI platform of computational disease models. Leveraging an extensive database of public and proprietary data, the company maps human diseases tissue by tissue and cell by cell. Researchers at leading pharma companies, including Pfizer and Sanofi, rely on CytoReason’s technology
Data practitioners rarely need convincing to prioritize data quality – it’s already a top-of-mind concern for most. If data is messy, incomplete, or outdated, teams are basically flying blind. Gartner estimates that poor data quality costs businesses around $12.9 million a year. But ask anyone about metadata quality and you might get a blank stare.
This post recaps a comprehensive tutorial published by Alex Merced from Dremio and Tal Sofer from lakeFS, highlighting how version control transforms multimodal data management for AI teams. The Challenge: Keeping Diverse Data Types in Sync and Queriable Modern AI pipelines consume more than just structured data. Training sets include images, model artifacts, logs, and
Multimodal data is nothing else than data gathered from several sources or formats, such as text, photos, audio, video, and sensor readings. What value does it bring to teams? It collectively provides a more complete, holistic view of the environment. This is especially relevant to AI systems learning to understand and interact with humans more
Making Sense of Large-Scale Data Through Metadata Picture this: Your ML team needs to find all images labeled “defective” from Q3 production runs tagged by a specific annotation workflow to retrain a quality control model. In a data lake with 10 billion objects, how do you find them? For most teams, the answer is: you
From Inspiration to Action When we were still dreaming up lakeFS, one of the projects that inspired us was DVC (Data Version Control). It was one of those moments when you realize – “Ah, others see it too.” We weren’t alone in believing that data should be managed like code. DVC was built by data
Many lakeFS customers in the aerospace, automotive, healthcare & life sciences, and manufacturing industries also are heavy users of MATLAB. lakeFS solves a range of data ops challenges for these organizations by serving as a “control plane” for AI-ready data – versioning complex data pipelines, tracking metadata and lineage, and enabling team collaboration through git-like
We’re excited to share that lakeFS has been named a Representative Vendor in the 2025 Gartner® Market Guide for DataOps Tools. We believe this recognition reflects what we’re seeing across the industry: the urgent need for data infrastructures that can provide AI-ready data efficiently, repeatably, and safely as organizations build production AI systems. DataOps Market
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
