Course material for MLOps summer course in Nova. Also available at this URL.
- Understand the fundamental principles of agile development
- Understand how to integrate design, model development, training and operations in a continuous cycle.
- Apply quality assurance concepts at every step of the process.
- Create a complete model as well as a report in a software artifact that changes continuously.
- Applying fundamental machine learning and scientific principles to the above design and reports.
Before the syllabus, we need to understand the principles | Web and read the introduction to the course | Web.
- Design thinking for solving real-life problems | Web
- Agile development | Web
- Test driven development for scraping workflows | Web.
- Containers for reproductible science | Web.
This will most probably not be included
- Open science: creation and deployment of machine learning projects | Web.
- MLOps tools.
- Automation infrastructure using GitHub actions | Web.
The course is organized as a bootcamp, with different sessions over which a project, and eventually a coauthored preprint, will be developed. Students can use this repository template to kick-start their projects with spell-checking workflows.
Updated data will be available in this repo; you can also explore data at this notebook.
Session objectives and material are as follows:
- First session, meet and greet, organization and setup.
- Second session, understanding agile.
- Third session, downloading data.
- Fourth session, extracting data, containerizing.
- Fifth session, presenting information.
Most tasks that are included in workflows are also in the top-level Makefile
Should be all of them, although at a certain point they can be out of sync.
But there are a few tools that you should have installed in your system
- Perl, preferably installed via
perlbrew, includingcpanm - R
- Python
All text here is released under a cc-by-sa license, all code under the GPL.