Do you remember the old days when you had to hand-code Gradient Descent or manually tune your hyperparameters? Over the last 20 years, the machine learning tool stack has improved considerably and now includes tools like Keras and Scikit. DataRobot has kept pace with this acceleration and offers a wide set of tools for data scientists.
In this webinar, we show how to extend Python to try multiple modeling approaches (anomaly, time series, multiclass), create sophisticated feature engineering across multiple datasets, and even build hundreds of diverse models with a few lines of code. We’ll discuss how data scientists save time, get more accurate results, and most important, create value for their organizations.
During this webinar, you will learn:
How you can use Python with DataRobot to build powerful model factories
How you can use Feature Discovery to accelerate feature engineering and improve your models
How to customize your models with DataRobot's latest feature, Composable ML