Artificial Intelligence(AI) and Machine Learning(ML) are all the rage right now. In this session, we’ll be looking at engineering best practices that can be applied to ML, how ML research can be integrated with an agile development cycle, and how open ended research can be managed within project planning
According to a recent Narrative Science survey, 38% of enterprises surveyed were already using AI, with 62% expecting to be using it by 2018. So it’s understandable that many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve, let alone how it might fit into a traditional engineering team or how they might get it to a production setting.
At Basement Crowd we are currently taking a new product to market and trying to go from a simple idea to a production ML system. Along the way we have had to integrate open ended academic research tasks with our existing agile development process and project planning, as well as working out how to deliver the ML system to a production setting in a repeatable, robust way, with all the considerations expected from a normal software project.
7. 62%
Percentage of organizations expecting to be using AI Technologies by 2018
Narrative Science - Outlook on Artificial Intelligence in the Enterprise 2016
20. 3 Principles:
1) Don’t build Machine Learning for the sake of it
2) Do you need ML in your MVP to test product
market fit?
3) Is your ML mission critical?
24. ML Anti-Patterns:
Dead experiment code - Configuration debt
Code glue - Pipeline jungles
Sculley, D., et al. "Hidden technical debt in machine learning systems."
25. “Glue code and pipeline jungles are
symptomatic of integration issues that may
have a root cause in overly separated
‘research’ and ‘engineering’ roles”
Sculley, D., et al. "Hidden technical debt in machine learning systems."