MLonCode San Francisco Meetup recap
On November 1st, we hosted our first Machine Learning on Code San Francisco meetup at Holberton School. We had a pretty good turnout for a first event and got really feedback from participants. Here is a quick post including talk abstracts, slides and video recordings !
source{d} Engine: Turn your Code into actionable Data
In this first talk, the one and only Francesc Campoy introduced the field of Code as Data and live demo’ed the kind of insights one can extract from a large codebase with the help of SQL, language classification, program parsing and token extraction. Here are the slides and video of Francesc ’ talk.
You can also find more information about the demo script in this Jupyter Notebook.
Inextricably Linked: Reproducibility and Productivity in Data Science and AI
In the second talk, Mark Coleman from dotscience presents his team’s research comparing the evolution of Software Development & DevOps with that of Data Science & AI.
Because it is more complex and has far more moving parts, Data Science & AI is where Software Development was in 1999: people are emailing and Slacking notebooks to each other, due to a lack of appropriate tooling. There are few CI/CD pipelines and model health monitoring is scarce. A lot that could be automated is still manual. And teams are silo’ed. This causes problems both for productivity: it’s hard to collaborate, and reproducibility: which impacts on governance and compliance.
Learn more about source{d} and dotscience:
- Watch source{d} Engine in 5 minutes video
- Check out the source{d} Engine repository on GitHub
- Sign up for our upcoming Online Meetup
- Join the source{d} Community Slack
- Check out the dotscience website
