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ProductizeML
Introduction
Objectives
About the Course
Guidelines
Syllabus
After Completion
Machine Learning
Why ML, and why now
Supervised Learning
Unsupervised Learning
Deep Learning
ML Terminology
Data Management
Data Access
Data Collection
Data Curation
Train and Evaluate
Framework and Hardware
Training Neural Networks
Model Evaluation
Productize It
ML Life Cycle
Business Objectives
Data Preparation
Model Development
Train, Evaluate, and Deploy
A/B Testing
KPI Evaluation
PM Terminology
Resources
Readings
Courses
Videos
Hands-On
Python for Machine Learning
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GitBook
Readings
Required Readings
Name
Author(s)
Publisher
Year
Rules of Machine Learning
Martin Zinkevich
Google
2020
People + AI Guidebook
People + AI Research (PAIR)
Google
2020
Designing Great ML Experiences
Apple's WWDC 2019
Apple
2019
Managing Machine Learning Projects
V.M. Megler
Amazon Web Services
2019
Everything We Wish We'd Known About Building Data Products
DJ Patil
First Round
Does AI make strong tech companies stronger?
Benedict Evans
2018
Shipping disruptive ML/AI products at scale (not papers)
Synthesized
Synthesized
2020
The Step-By-Step PM Guide to Building Machine Learning Based Products
Yavel Gavish
Medium
2017
Interesting readings
Name
Author(s)
Publisher
Year
Rise of the Data Product Manager
Trey Causey
Medium
2017
Practical Skills for The AI Product Manager
Justin Norman, Peter Skomoroch and Mike Loukides
O'Reilly
2020
What does Machine Learning look like?
Matt Fyles
What you need to know about product management for AI
Peter Skomoroch and Mike Loukides
O'Reilly
2020
7 Artificial Intelligence Trends and How They Work With Operational Machine Learning
Nisha Talagala
Oracle
2019
Machine Learning Glossary
Brendan Fortuner
2020
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Courses
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Contents
Required Readings
Interesting readings