A self-study guide for teams building Artificial Intelligence and Machine Learning products.

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.

Join the community at Discord and follow us on Twitter!

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

Lectures Breakdown

Why ML, and why nowSupervised LearningUnsupervised LearningDeep LearningData AccessData CollectionData CurationFramework and HardwareTraining Neural NetworksModel EvaluationML Life CycleBusiness ObjectivesData PreparationModel DevelopmentTrain, Evaluate, and DeployA/B Testing

About us

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