❤️ Thanks for reading To know more about Data Caching Hub, head on to our documentation dvc.org/doc/start/experiment… 🧵[6/6]

🪞 Avoid duplication Avoid downloading data again and duplicating files when multiple people are working on the same data (for example on a shared development server). 🧵[4/6]

💰 Pay only for fast access Pay only for fast access to frequently-used data (upgrading your entire storage platform is expensive). 🧵[3/6]

⚡️ Speed up data transfers Speed up data transfers from massive object stores on the cloud, or share data across multiple machines without slowing things down. 🧵[2/6]

DVC’s built-in data caching lets us implement a simple and efficient storage layer globally This approach helps to: ⚡️ Speed up data transfers 💰 Pay only for fast access 🪞 Avoid duplication 🔀 Switch data inputs quickly @DVCorg @iterativeai #mlops #caching #data 🧵[1/6]

👨🏻‍💻 Thanks for reading till here! You can head straight to the docs to start integrating all these features in your projects 👇 cml.dev/doc/ref 🧵[7/7]

🧑🏼‍🏫 tensorboard The “cml tensorboard connect” returns a link to a tensorboard.dev page. 🧵[6/7]

💬 comment We can effortlessly post a Markdown report as a comment on a commit or pull/merge request with “cml comment create” command. 🧵[5/7]

⤴️ pr We can also commit specified files to a new branch and create a pull request using “cml pr create” command. 🧵[4/7]

🏃‍♂️ runner When a workflow requires computational resources, CML can automatically allocate cloud instances using “cml runner”. You can spin up instances on AWS, Azure, GCP, Kubernetes or any other provider. We can start a runner using “cml runner launch” command. 🧵[3/7]