- Versioning Machine Learning Experiments vs Tracking Them - Dec 27, 2021.
Learn how to improve ML reproducibility by treating experiments as code.
- GitHub Desktop for Data Scientists - Sep 29, 2021.
Less scary than version control in the command line.
- Data Versioning: Does it mean what you think it means? - Aug 26, 2020.
Does data versioning mean what you think it means? Read this overview with use cases to see what data versioning really is, and the tools that can help you manage it.
- Nitpicking Machine Learning Technical Debt - Jun 8, 2020.
Technical Debt in software development is pervasive. With machine learning engineering maturing, this classic trouble is unsurprisingly rearing its ugly head. These 25 best practices, first described in 2015 and promptly overshadowed by shiny new ML techniques, are updated for 2020 and ready for you to follow -- and lead the way to better ML code and processes in your organization.
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- ModelDB 2.0 is here! - Mar 19, 2020.
We are excited to announce that ModelDB 2.0 is now available! We have learned a lot since building ModelDB 1.0, so we decided to rebuild from the ground up.
- Automatic Version Control for Data Scientists - Sep 24, 2019.
How can you keep your machine learning models and data organized so you can collaborate effectively? Discover this new tool set available for better version control designed for the data scientist workflow.
- Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
I am a Git god, why do I need another version control system for Machine Learning Projects?
- DevOps for Data Scientists: Taming the Unicorn - Jul 27, 2018.
How do we version control the model and add it to an app? How will people interact with our website based on the outcome? How will it scale!?
- Data Version Control in Analytics DevOps Paradigm - Aug 14, 2017.
DevOps and DVC tools can help reduce time data scientists spend on mundane data preparation and achieve their dream of focusing on cool machine learning algorithms and interesting data analysis.
- How A Data Scientist Can Improve Productivity - May 25, 2017.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
- Data Version Control: iterative machine learning - May 11, 2017.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.