For their engineering final project at @Insper, Arthur Olga, Gabriel Monteiro, Guilherme Leite, and Vinicius Lima created the MLOps Guide, which provides a Complete MLOps development cycle using DVC, CML, and IBM Watson.
@alexburlacu1996 tells a great story and provides many tips from his experience in MLOps in his blog โA Fable About MLOpsโฆand Broken Dreams ๐ญโ.
If youโd like an online CheatSheet for DVC you can find one created by @igor_chubin here ๐ cheat.sh/dvc
Pick a command from the drop-down menu and bam ๐ฅ, youโve got the info you need!
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๐งโ๐ป Data Query Language
We have a new solution for managing datasets of unstructured data! Extend your DVC environment with the first unstructured data query language for machine learning.
Schedule a meeting with us if that's what you're needing!
๐ฆ DVC is designed to improve upon past solutions to make the life of ML teams easier. Hereโs how it differentiates from other related technologies:
DVC builds upon Git by the concept of data files โ large files that should not be stored in a Git repository, but need to be tracked and versioned.
It leverages Git's features to enable managing different versions of data, data pipelines, and experiments.
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๐ฆ Git-LFS vs DVC
DVC does not require special servers like Git-LFS demands. Any cloud storage like S3, Google Cloud Storage, or even an SSH server can be used as a remote storage.
No additional databases, servers, or infrastructure are required.