AI and Machine Learning are at-or-about the top of the Gartner Hype Cycle right now, indicating that they should be approached and invested in with caution. There are a range of real-world uses of AI and Machine Learning which can enable DevOps practices now though.

Workshop Requirements We'll be working through developing a custom machine learning solution for predicting whether a pull-request (or code merge) will result in a bug.

There will be some maths content, but it will be relatively light and hopefully I will explain it all well enough.

People will need to have the following installed (all of this works on any platform - Windows/Linux/Mac):

• Git: https://git-scm.com/
• Postman (or equivalent): https://www.getpostman.com/
• A code editor (Bernard will be using VS Code)
• Jupyter Notebook: http://jupyter.org/install
• Python (prerequisite for Jupyter): https://www.python.org/
• Python libraries:
• Sci-Kit Learn: http://scikit-learn.org/stable/
• • $ pip install scikit-learn
• Pandas: https://pandas.pydata.org/
• • $ pip install pandas
• Flask: http://flask.pocoo.org/
• • $ pip install flask
  • $ pip install flask_debug
• Optional libraries:
• • MatPlotLib:https://matplotlib.org/
  • NumPy: http://www.numpy.org/