Timestamps

• (01:44) Mohamed described his interest growing up in Egypt and studying Biomedical Engineering at Cairo University in the early 2000s.
• (04:22) Mohamed commented on his experience moving to the US to pursue an MBA degree and working in various software engineering roles.
• (07:35) Mohamed shared his experience authoring two books: (1) 3D Business Analyst: The Ultimate Hands-On Guide to Mastering Business Analysis and (2) Business Analysis for Beginners: Jump-Start Your BA Career in 4 Weeks.
• (13:19) Mohamed discussed his move to the Bay Area for a Senior Engineering Manager role at Twilio, managing and shipping a series of communication API products using Machine and Deep Learning.
• (17:39) Mohamed dissected engineering challenges building ML systems at Amazon, alongside key leadership lessons he acquired from managing Amazon’s Kindle mobile and ML engineering teams.
• (20:50) Mohamed shared his insider perspective on Amazon’s practices of customer obsession, working backward, and disagree-to-commit.
• (24:52) Mohamed mentioned the benefits of teaching a computer vision course for engineers at Amazon’s internal Machine Learning university.
• (28:33) Mohamed went over the engineering (hardware + software) and ML challenges associated with building a proprietary threat detection platform at Synapse Tech Corporation (where he was the Head of Engineering).
• (32:03) Mohamed shared concrete technical challenges with building an ML system that performs inference on edge devices.
• (37:03) Mohamed revealed specific data labeling challenges while building the ML system at Synapse.
• (39:57) Mohamed went over his one year as the VP of Engineering for the AI Platform at Rakuten, when he incubated the idea for Kolena.
• (42:52) Mohamed explained the current state of ML testing infrastructure and unpacked his current project Kolena, a rigorous ML QA platform that lets users take control of their ML testing.
• (49:07) Mohamed has been collaborating with a few institutions, podcasters, and ML influencers to raise awareness of the importance of ML testing and different approaches to tackle the problem.
• (50:12) Mohamed touched on his side hustles working with Intel in autonomous drones and teaching content with Udacity’s AI Nanodegree programs.
• (53:07) Mohamed dissected his project Mowgly, an educational platform with tracks curated by industry experts to guide users to master specific topics.
• (54:58) Mohamed described his experience authoring a book with Manning in 2020 called “Deep Learning For Vision Systems.”
• (58:51) Closing segment.

Mohamed’s Contact Info

• LinkedIn
• Twitter
• Website
• YouTube
• GitHub
• Kolena

Mentioned ContentPeople

• Andrew Trask (Leader at OpenMined, Senior Research Scientist at DeepMind, Ph.D. Student at the University of Oxford)
• Francois Chollet (Senior Software Engineer at Google, Creator of Keras)
• Lex Fridman (Host of the popular Lex Fridman Podcast, AI Researcher working on autonomous vehicles and human-robot interaction at MIT)

Books

• “Mindset” (by Carol Dweck)
• “Outliers” (by Malcolm Gladwell)

Notes

My conversation with Mohamed was recorded back in March 2021. Here are some updates that Mohamed shared with me since then:

• Kolena is an ML testing and validation platform that enables teams to implement testing best practices to rigorously test their models’ behavior and ship high-quality ML products much faster.
• Mohamed and his team have signed a couple of big enterprise customers and raised a large seed round from top-tier investors and almost every industry leader in the AI space. These were strong signals that Kolena is solving a very important problem!
• Mohamed’s first impression on the market is: the ML market is hungry for a reliable testing platform for models. Kolena has quite of a waitlist and plans to launch early next year.

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

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