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👨🏻‍💻 Setup CI/CD in your machine learning projects using these simple yet powerful “♾️ CML commands”:

🔄 ci
🏃‍♂️ runner
⤴️ pr
💬 comment
🧑🏼‍🏫 tensorboard

@Iterativeai @DVCorg
#cml #tensorboard #opensource

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🔄 ci

The “cml ci” command prepares the whole repository for CML operations after creating the cml.yaml file

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🏃‍♂️ 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.

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Read 7 tweets
✨🧠 The ecosystem that has grown up around @TensorFlow in the last few years blows my mind. There's just so much functionality, compared to some of the other, newer frameworks.

👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
1) @TensorFlow Extended (TFX)

It's no secret that I 💕 #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.

tensorflow.org/tfx/?hl=zh-cn
2) @TensorFlow Hub

If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easy—and are available in an #OSS marketplace: tfhub.dev.

site: tensorflow.org/hub/
Read 40 tweets

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