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+---
+title: |
+  NEW VIDEO! 🎥 MLOps Tutorial #1: 
+  Intro to continuous integration for ML
+date: 2020-07-24
+description: |
+  A video tutorial abut using continuous integration in data 
+  science and machine learning projects. This tutorial shows 
+  how to use GitHub Actions and Continuous Machine Learning 
+  (CML) to create your own automated model training and 
+  evaluation system. 
+picture: 2020-07-24/blog_header.png
+author: elle_obrien
+commentsUrl: https://discuss.dvc.org/t/new-video-mlops-tutorial-1-intro-to-continuous-integration-for-ml/454
+tags:
+  - Continuous integration
+  - DevOps
+  - MLOps
+  - CML
+---
+
+Earlier this month, we launched [CML](https;//cml.dev), our latest open-source project in the MLOps space. We think it's a step towards establishing powerful DevOps practices (like continuous integration) as a regular fixture of machine learning and data science projects. But there are plenty of challenges ahead, and a big one is _literacy_. 
+
+So many data scientists, like developers, are self-taught. Data science degrees have only recently emerged on the scene, which means if you polled a handful of senior-level data scientists, there'd almost certainly be no universal training or certificate among them. Moreover, there's still no widespread agreement about what it takes to be a data scientist: is it an engineering role with a little bit of Tensorflow sprinkled on top? A title for statisticians who can code? We're not expecting an easy resolution to these existential questions anytime soon. 
+
+In the meantime, we're starting a video series to help data scientists curious about DevOps (and developers and engineeers curious about data science!) get started. Through hands-on coding examples and use cases, we want to give data science practitioners the fundamentals to explore, use, and influence MLOps. 
+
+The first video in this series uses a lightweight and fairly popular data science problem- building a model to predict wine quality ratings- as a playground to introduce continuous integration. 
+
+The tutorial covers:
+- Using Git-flow in a data science project (making a feature branch and pull request)
+- Creating your first GitHub Action to train and evaluate a model
+- Using CML to generate visual reports in your pull request summarizing model performance
+
+It's now up on YouTube!
+
+https://youtu.be/9BgIDqAzfuA
+
+[Code for the project is available online](https://github.com/andronovhopf/wine) so you can follow along! We also recommend checking out the [CML docs](https://github.com/iterative/cml) for more details, tutorials, and use cases.
+
+If you have questions, the best way to get in touch is by leaving a comment on the blog, video, or our [Discord channel](https://discord.gg/bzA6uY7). And, we're especially interested to hear what use cases you'd like to see covered in future videos- tell us about your data science project and how you could imagine using continuous integration, and we might be able to create a video! 
+