🔧 DVC: Data Version Control es una extensión de git pensada para ciencia de datos. Permite versionar no solo el código, sino también los conjuntos de datos, otorgándole un control total sobre la historia de su proyecto CI/CD. dvc.org #HerramientasDev 💻🧠

🦉Looking for a great tutorial to get you started leveling up your data and ML game this weekend? Bex Tuychiev delivers with this great one, taking you through all the ins and outs (or deps and outs as the case may be 😉) Happy Friday! datacamp.com/tutorial/data-…

🦉 Community member Akash Mane shares his comprehensive guide on robust practices for clean dataset labeling and rotation. Lots of good practices you will want to apply to level up your workflows! 👉🏽 hubs.la/Q03MLndh0

Don’t miss 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝗕𝗶𝗼𝗶𝗺𝗮𝗴𝗶𝗻𝗴 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗟𝗲𝗻𝘀 in PubMed. Researchers Jiajun Cao, et. al. propose a Bio-Data-Centric AI workflow that includes DVC! 👇🏽 hubs.la/Q03NPY_j0

Curious about Data as a Product (DaaP)? Community member Chinmoy Kumar writes about this paradigm where data is actively managed, versioned, documented, and delivered like any other software product. 👉🏽 hubs.la/Q03NVQWt0

It's not enough to develop your AI/ML solutions. They also must be maintained. This great guide from Stuti Dhruv of @aalphindia, frames AI maintenance and support as mission-critical for sustaining reliability, compliance, and ROI after deployment. 👉🏽 hubs.la/Q03NW8V60

Codezup delivers this tutorial to automate training, evaluation, and deployment using GitHub actions and DVC. It covers environment setup, project structuring, pipeline definition (dvc.yaml), parameter management (params.yaml), and the best workflows. 👉hubs.la/Q03NWvxq0