Machine Learning Model Development
Contact us to book this course
Machine Learning and AI
On-Site, Virtual
1 day
This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. This course focuses on executing common tasks efficiently with AutoML and MLflow. Participants will delve into key topics, including regression and classification models, 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.
Objectives
After consuming this content, you should be able to:
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Describe fundamental concepts of machine learning.
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Describe the main components of MLflow for model development.
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Describe hyperparameter tuning and methods.
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Utilize MLflow for model tracking and model tuning with hyperopt.
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Explain benefits and features of Databricks AutoML
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Create an AutoML experiment, identify the best model, and modify the generated models.
Prerequisites
At a minimum, you should be familiar with the following before attempting to take this content:
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Knowledge of fundamental concepts of regression and classification methods
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Familiarity with Databricks workspace and notebooks
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Intermediate level knowledge of Python
Course outline
- Model Development and MLflow
- Evaluating Model Performance
- Hyperparameter Tuning Fundamentals
- Hyperparameter Tuning with Hyperopt
- Automated Model Development with AutoML
