SESSION
Machine Learning Model Development (repeat)
OVERVIEW
| EXPERIENCE | In Person |
|---|---|
| TYPE | Paid Training |
| TRACK | Paid Training |
| DURATION | 240 min |
- Audience: Machine learning professionals
- Hands-on labs: Yes
- Certification path: Databricks Certified Machine Learning Associate
- Description: In this half-day course, you’ll learn how to develop traditional machine learning models on Databrick. We’ll cover topics like using popular ML libraries, executing common tasks efficiently with AutoML and MLflow, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment.
- Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python