Emerging Best Practices for Machine Learning Engineering
In this talk, I'lll walk through some of the emerging best practices for Machine Learning engineering and contrast them to those of traditional software development. I will be covering topics including Product Management; Research and Development; Deployment; QA and Lifecycle Management of Machine Learning projects.
Outline/Structure of the Talk
- Present a case study that guides the rest of the talk.
- Review of best practises or “sensible defaults” in software projects.
- Consider challenges that ML projects introduce.
- A breakdown of a typical Machine Learning project in an iterative, agile style, and some of the best practises in each phase.
- Understanding of modern software best practises (or "sensible defaults").
- An understanding of the unique challenges that Machine Learning projects introduce for product managers, data scientists and software engineers.
- A look at an Agile approach to Machine Learning delivery with some opinionated best practises.
Product managers, software developers and data scientists looking to understand best practises in ML.
Prerequisites for Attendees
Some basic understand of Machine Learning and, preferably, some experience developing software.