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Machine Learning

Machine Learning

lakeFS Community: Leonard Aukea nominated for Machine Learning Professional of the year!

Adi Polak

Our community is full of people with incredible skills and know-how. And this nomination proves us right! Our community member @Leonard Aukea has been nominated for Machine Learning Professional of the year as part of the Nordic DAIR Awards. Congratulations, Leonard!  Who is Leonard? Leonard Aukea has been Heading Machine Learning Engineering and Operations at […]

Data Engineering Machine Learning Product

How to Develop Spark ETL Pipelines in Isolation

Amit Kesarwani, Vino SD, Iddo Avneri

Table of Contents Developing ETL pipelines – what it looks like today Developing and testing ETL pipelines is very complicated. Since you typically don’t want to test against production data, you need to create your own personal space, like a bucket, and Then either sample or copy data out of production. Both approaches have their

Data Engineering Machine Learning Product

Proudly announcing lakeFS Cloud

Einat Orr, PhD, Oz Katz

What is lakeFS? As data practitioners, we use many different terms to talk about what we do – we call it business intelligence, analytics, data pipelines, or insights. But there’s one term that captures what we do really well: delivering products.  When we were leading a large R&D organization, we couldn’t help but wonder about

Data Engineering Machine Learning

lakeFS – Data Versioning at Scale

Paul Singman

If you think about it, lakeFS is about two things — version control and big data. We see ourselves as bringing version control to big data. This bridges a workflow gap that currently exists when working with data and working with code.  This gap is purely artificial — there’s no conceptual reason why different workflows should be required for

Machine Learning Product

Build Reproducible Experiments with Kubeflow and lakeFS

Tal Sofer, Paul Singman

Introducing Kubeflow and lakeFS Kubeflow is a cloud-native ML platform that simplifies the training and deployment of machine learning pipelines on Kubernetes. An ML project using Kubeflow will consist of isolated components for each stage of the ML lifecycle. And each component of a Kubeflow pipeline is packaged as a Docker image and executed in a

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