lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
Choosing between Databricks and Snowflake can be challenging for organizations navigating a modern data infrastructure. While both platforms are powerful in their own right, they have different strengths and weaknesses. The story of Databricks and Snowflake began with a partnership as each concentrated on different data management areas. While Snowflake focused on data warehousing, Databricks […]
MLflow is a popular solution for tracking experiments, managing models, and deploying them across several environments. One of the components of MLflow is Model Registry, a service that lets teams manage and track ML models and associated artifacts and provides a user interface for browsing them. How does Model Registry work, and what MLflow data
In the landscape of ML and AI, metadata is essential for building accurate, trustworthy models. By providing context around data, metadata supports efficient data discovery, tracking, and validation, which are crucial for creating reproducible and reliable models. As models grow more complex, so does the volume and complexity of metadata, making robust metadata management essential.
Hugging Face acquired Xethub to build an internal data version control system. XetHub is a platform for collaborative development created by former Apple researchers in 2021 to improve the efficiency of machine learning teams that deal with huge datasets and models. The solution provides Git-like version management for up to TB-sized repositories, facilitating team collaboration,
Data versioning is a central aspect of modern data management, especially in the context of GenAI and machine learning. Teams need a solution to version both their data and models. By keeping track of various iterations of datasets and models, they can manage changes smoothly and ensure the reproducibility of results. MLflow has become a
A team looking to build an application that uses a large language model (LLM) like OpenAI’s GPT-4 or Meta’s LLama 2 will inevitably run into this issue: How can we ensure that the responses generated by these models align with the specific business context? This is where retrieval augmented generation (RAG) comes in. RAG brings
What is a mount? A filesystem mount is the ability to present a local device or a remote location as a local directory. It is a basic feature provided by all operating systems and is widely used by system admins and developers. Let’s break down the differences between Mountpoint for Amazon S3 and lakeFS Mount:
Retrieval Augmented Generation (RAG) is on its way to becoming the dominant framework for implementing enterprise applications based on Large Language Models (LLMs). However, implementing RAG on your own is tricky. The framework calls for a high degree of knowledge and skill, as well as ongoing investment in DevOps and MLOps. Not to mention staying
Developing a machine learning application is a complex process that involves steps such as processing massive volumes of data, testing multiple ML models, parameter optimization, feature tuning, and others. This is why data version control is critical in the ML environment. If you want your experiments and data to be reproducible, you need to use
As OpenAI unveiled ChatGPT, which swiftly explained difficult problems, carved sonnets, and discovered errors in code, the usefulness and adaptability of LLMs became clear. Soon after, companies across various sectors began exploring new use cases, testing generative AI capabilities and solutions, and incorporating these LLM processes into their engineering environments. Whether it’s a chatbot, product
We are excited to announce the launch of lakeFS Mount, a powerful new lakeFS client designed to simplify your data workflows. lakeFS Mount allows you to mount a lakeFS repository (or a path within one) as a local directory on any workstation or server, bringing unprecedented ease and efficiency to your data operations. But what
Machine learning teams face many hurdles, from data sources with missing values to experiment reproducibility issues. MLflow is a tool that makes this easier. And Databricks makes working with it even more straightforward, thanks to its managed MLflow offering. Managed MLflow expands the capabilities of MLflow, with an emphasis on dependability, security, and scalability. Keep
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