Merge pull request #1625 from iterative/new_video

New video

content/blog/2020-07-24-first-mlops-tutorial.md

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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!