The Challenges of Building Operational AI And How to Avoid Them

Daniel Skantze, Peltarion

To date, deep learning has been for advanced data scientists or researchers. Interest is surging thanks to the success of new use cases for neural networks such as tumour segmentation, speech recognition, image search engines and more. But how do you get started without a data science background?

Daniel will share his deep learning experience, giving a brief introduction to AI, explaining:

  • Why Deep Learning is particularly relevant for developers
  • Why a lot of AI initiatives actually fail (though few people talk about it)
  • How to avoid common AI pitfalls, from avoiding glue code to building projects to scale from the offset
  • How to get AI projects started in your organisation

Objective of the talk

Daniel will explain how developers can approach the field of deep learning as a whole, giving advice on overcoming the most common challenges in data science to ensure a greater chance of project success in the future.

Required audience experience

Experience in software engineering with an interest in starting a deep learning project.

Track 2
Location: Gielgud Date: September 30, 2019 Time: 11:20 am - 12:05 pm Daniel Skantze, Peltarion Daniel Skantze, Peltarion