ProductizeML

About the course
ProductizeML provides guidelines and best practices for managing the end-to-end Machine Learning life cycle and its productization.
The course will start diving into the fundamentals around the Machine Learning algorithms, from the supervised and unsupervised learning strategies to the latest advances in deep learning techniques; followed by some recommended ways of accessing and managing data. Tips and best practices when training and evaluating models will be provided. And last but not least, the core piece of this course: how to bring it to production and make an outstanding product out of it! 
Why ProductizeML?
Learn to develop an ML product that can be included in your business solution.
Join the community to interact and share with mentors and fellow students.
Manage your time to go through the course material at your own pace.
Practice with real-world examples built by industry experts.
For more information on how to start this course, please read the course's ​Objectives and ​Guidelines. 
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This course is constantly growing and expanding, meaning that some sections might be under construction 🚧 — do NOT panic, and instead leave a message of what you would like to see!
Course Lectures
Introduction
Machine Learning
Data Management
Train and Evaluate
Productize It
Hands-On
Lectures Breakdown
Machine Learning
Data Management
Train and Evaluate
Productize It
Why ML, and why now
Supervised Learning
Unsupervised Learning
Deep Learning
Data Access
Data Collection
Data Curation
Framework and Hardware
Training Neural Networks
Model Evaluation
ML Life Cycle
Business Objectives
Data Preparation
Model Development
Train, Evaluate, and Deploy
A/B Testing
KPI Evaluation
​                                                       Productize ML: A Machine Learning in Production Course​
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Have a question or suggestion? You can reach us at [email protected] .

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Objectives

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