JAX London, 3–6 October 2022
The Conference for Java & Software Innovation

Agile Machine Learning: From Theory to Production

This talk originates from the archive. To the CURRENT program
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Tuesday, October 10 2017
18:15 - 19:05

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.

Behind the Tracks

Software Architecture & Design
Software innovation & more
Architecture structure & more
Agile & Communication
Methodologies & more
Emerging Technologies
Everything about the latest technologies
DevOps & Continuous Delivery
Delivery Pipelines, Testing & more
Cloud & Modern Infrastructure
Everything about new tools and platforms
Big Data & Machine Learning
Saving, processing & more