A Manager’s Guide to Starting a Computer Vision Program
With every passing day, deep learning and computer vision are taking the world by storm. But what does this mean for your typical data science team? How do you know if you should be using computer vision in your work, and how do you get started?
This talk will provide a guide for data science leaders and managers who are thinking about dipping their toes into computer vision. It will help you think through a set of sequential questions that you’re likely to encounter along the way:
- Should you use a fully out-of-the-box solution like AWS Rekognition or Google Cloud Vision?
- Should you build your own custom models without writing code by making use of a service like Google AutoML Vision or Clarifai? When should you decide to code your own models instead?
- If you code your own models, what infrastructure should you use? Do you need to invest in your own on-premise GPU’s or are cloud GPU’s sufficient? Or maybe you should you use a hosted service for training and deployment?
- Keras, PyTorch,Tensorflow (Eager), MXNet, Gluon–it seems like a new framework emerges every month. What factors should you consider when you’re deciding on a language and framework?
- Where do you find talent? Should you hire it or grow it?
Keras, PyTorch,Tensorflow (Eager), MXNet, Gluon–it seems like a new framework emerges every month. What factors should you consider when you’re deciding on a language and framework?
Where do you find talent? Should you hire it or grow it?This talk will draw heavily on my experiences at ShopRunner, an e-commerce network offering its members free two-day shipping, free returns, and seamless checkout at hundreds of retailers, where we’ve been building a computer vision program to turn tens of millions of product images into compelling consumer features across our website, mobile app, and browser extension.`