The ML Debugger’s Arena: Conquer Market Mix Modeling

In machine learning, the path to success isn’t always straightforward. Data issues, model inaccuracies, and deployment hiccups are just some of the challenges that professionals face every day. In this program, you’ll dive into the real-world bugs and problems that occur in machine learning projects, with a specific focus on Market Mix Modeling (MMM) in the retail-CPG (Consumer Packaged Goods) domain. You’ll gain hands-on experience troubleshooting and solving common issues faced by data professionals working in this industry.

Throughout the program, you will:

• Identify bugs in data preprocessing, feature engineering, and model performance, pinpointing exactly where things are going wrong.

• Troubleshoot and debug real-world issues, such as inconsistent data, flawed feature engineering, or poor model accuracy, by applying industry-standard solutions.

• Resolve deployment issues, ensuring smooth integration of models and APIs into production, just like you would on a real project.

• Streamline your workflow by fixing common experiment tracking problems, ensuring data integrity and traceability in your machine learning projects.

This isn’t a traditional course with structured lessons or certifications. Instead, it’s a hands-on, open-ended challenge that mimics the real-world bugs that professionals deal with daily. By diving into these problems, you’ll build the practical skills needed to quickly identify and eliminate errors in ML pipelines, giving you the confidence to tackle any machine learning challenge in the field.