Build an ML Product - 4 Mistakes to Avoid
Details
If you want to build an industry-grade ML product, you have to build and evaluate a large list of ML models. You may be able to manage the ML pipeline manually during the early stages. However, you will soon realize that the manual process is not sustainable and you will have no choice other than building an automated ML pipeline. Why is it mandatory to have an automated pipeline? Is it a nice-to-have or must-have feature in the daily development practice? In this talk we share 4 important facts that many ML startups neglect. These negligences will heavily affect the company success later down the road!
About our speaker, Pedram:
Pedram is a technology startup executive with 5+ years of experience in developing industry-grade ML products. He received his Ph.D. in Electrical and Computer Engineering from the University of British Columbia, Vancouver, Canada. Pedram was a machine intelligence team lead at Thalmic Labs for 3 years. He also worked as a venture partner for Tehran-based Shenasa venture capital where his role was to boost deal flow and make selected startups investment-ready. He currently runs a digital magazine on startup and technology with a mission to help high-potential technology startups build and grow (https://leanmint.com/).
About the venue:
HiVE is Vancouver’s longest standing co-working space that operates as a non-profit society.
Build an ML Product - 4 Mistakes to Avoid