💡Whitepaper: Training Data for ML Models—A Deep Dive

Struggling to get the right training data for your ML models?

This whitepaper breaks down the common challenges faced by ML teams, including accessing the right training datasets, the time-travel problem, training-serving skew, and backfilling.

It also deep dives into how teams can better create and manage training data, and why more and more teams are turning to MLOps solutions like feature platforms to standardize access and the use of training data across their organizations. Some benefits include:  

• Single authorship of features. Write a single definition of a feature that will work in an online environment and can be backfilled against historical data in the offline environment.

• Easy generation of training data. Generate an accurate training dataset on demand with just a few lines of code, without having to worry about backfilling complexity.

• Solving the time-travel problem. Backfill feature data by performing point-in-time correct joins and ensure consistency between training and serving.

• Accelerated notebook-driven development. Data teams can run code, explore data, and share results all in one notebook.