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Open source software has fundamentally reshaped technology—delivering unmatched flexibility, low friction, and rapid innovation. For some teams, it’s a philosophical commitment. For others, it’s the fastest path to building. lakeFS supports both models. For most data teams, the journey starts with open source and evolves over time. lakeFS open source offers a robust foundation for […]
Despite the increasing adoption of Artificial Intelligence (AI) applications, most organizations are bound to see implementation challenges. One of the issues lies in the data itself. A recent survey showed 80% of companies believe their data is suitable for AI, but more than half are actually dealing with challenges like internal data quality and categorization
Today’s organizations don’t just use a single data storage solution – they operate across on-prem servers, multiple cloud providers, and hybrid environments. This distributed approach has become necessary, but it comes with significant costs: teams struggle with siloed tools, duplicated processes, and an endless cycle of environment management that diverts focus from delivering actual value.
A solid AI infrastructure is essential for efficiently developing and deploying AI and machine learning (ML) applications – from facial and speech recognition to text processing and computer vision. Before we dive into why AI infrastructure is crucial and how it works, let’s define it first. What is AI infrastructure? AI infrastructure, also known as
With the volumes of generated data increasing, metadata has become an essential component in organizing and comprehending massive datasets. Metadata plays a key role in any modern data strategy, especially among organizations that treat data as one of their most precious assets. This article dives into all the different metadata types, tools, and frameworks to
Many companies are modernizing their data storage infrastructure to capitalize on the opportunities of machine learning (ML) and advanced analytics. However, teams face several unique data management challenges such as the increasing time required for AI training and inference workloads, as well as the cost and scarcity and resources, particularly GPUs. Storage is a key
Supercharging Machine Learning Machine learning (ML) is essential in driving critical business decisions and innovation across various industries. To maintain competitive advantages, organizations continually refine and enhance their ML models through iterative fine-tuning and parallel experimentation. While these strategies are powerful, they come with substantial challenges related to data management, reproducibility, and resource optimization. lakeFS
This article discusses AI Agents in business and automation, focusing on building an AI Agent using lakeFS, LangChain, OpenAI, and FAISS (Facebook AI Similarity Search) to answer questions based on documents. It explains what AI Agents and LangChain are, and how lakeFS is used for data version control. The article also provides an example of
Managing complex and massive data sets is tricky but metadata management tools can help teams keep their data in shape. Metadata management has become critical in data strategies created by organizations that treat data as an important asset. In this article, we dive into metadata management and give you an overview of tools teams use
One of the capabilities of lakeFS is that you can use it to create isolated environments for experimentation or development. Let’s say we want to build a machine learning model and need to prepare or clean some data. With lakeFS, we can do this in isolation without creating an entire copy of the dataset. Let’s
What is the key element that guarantees all data published on portals is discoverable, comprehensible, reusable, and interoperable for people and technology like AI? You guessed right; it’s metadata. Metadata also plays a key role in data governance and management. According to Gartner, organizations that fail to adopt a metadata-driven strategy for IT modernization might
Reproducibility is a fundamental challenge in building reliable machine learning (ML) models and AI applications. It’s not just about debugging a model when it fails in production; it’s also about ensuring that experiments are consistent, avoiding unintended variance, and making incremental progress with confidence. Without reproducibility, ML teams risk wasting time on unreliable results and
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