lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
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 […]
As companies race to adopt AI technology, many firms in highly-regulated fields such as Healthcare, Financial Services, and Defence are at risk of being left behind. How is it possible to innovate and move fast while at the same time appeasing the demands of regulators? Companies that shy away from this new technology will almost
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
GPUs are blazingly fast, but many teams struggle to keep them running at peak performance. A recent poll on AI infrastructure shows that maximizing GPU use is a top priority, and data from Weights & Biases reveals that roughly a third of GPUs are at less than 15% usage, which is low. The good news
If you put garbage in, you’re likely to get garbage out. This phrase rings particularly true in the era of Generative AI, where models keep hallucinating despite the time and money teams pour into them. The saying ultimately relates to the quality of the data you use to develop and train your ML models. This
Data lineage tools make it easier for teams to track the transfer of data across several systems, databases, and applications. Ultimately, this translates into better capabilities around understanding and handling data. But how do you choose the best data lineage solution for your organization? This article dives into the most widespread data lineage tools to
Key Takeaways DataOps is an approach that aims to enhance collaboration among teams involved in data operations, including data engineers, data scientists, and stakeholders. The idea is to create a more coherent and efficient data-driven environment by automating time-consuming procedures, reducing errors, and speeding up data transmission. This will give companies better time for insight
What kind of tooling can you use to make data mesh work? Should you go after open-source or commercial solutions? There’s no single answer to these questions. But the first step is to know what tools are out there that could potentially help you build your data mesh architecture. Once you have a clear idea
Data engineers are easily the unsung heroes of modern business operations. Just think of all the people who create and maintain the data pipelines and infrastructures that store and analyze the ever-increasing waves of data. Without data engineers, many digital products or services simply wouldn’t be possible. Luckily, the data engineer’s life is changing to
Within five days of its release, ChatGPT counted one million registered users. This was the fastest growth of any product in its category, prompting the rise of the GenAI era that saw countless products developed at the speed of light. As expected, regulation could only catch up with this incredible expansion pace. What are the
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
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