In machine learning, it's known that you can’t create a great model without great training data. You need to train your ML models the right way, with the right data, in order to get accurate predictive outputs.
In this whitepaper, we break down the challenges data scientists and ML teams face in generating training data for ML models, including accessing the right training datasets, the time-travel problem, training-serving skew, backfilling, and more.
We also share 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, realizing benefits including:
- 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.
Download the whitepaper to learn more!