Building Machine Learning Models on Databricks

Build and Train Machine Learning Models on Databricks

Unlock the power of machine learning with this comprehensive course designed to equip you with the skills to build, train, and deploy models using the Databricks platform and MLflow. In today's competitive landscape, a robust machine learning environment is crucial for success. Learn how to leverage the Databricks Machine Learning Runtime and MLflow to streamline your entire ML workflow.

What You'll Learn:

• Data Preparation with Databricks: Master the process of loading, exploring, and processing data using Databricks notebooks. Discover the efficiency of Bamboolib for no-code data analysis and transformations, accelerating your data pipeline.
• Model Building with scikit-learn: Gain hands-on experience building and training both regression and classification models using the widely-used scikit-learn framework.
• Experiment Tracking & Management: Learn to create and manage machine learning experiments. Track model parameters and key metrics using MLflow Runs, and effectively compare different runs using the intuitive MLflow UI.
• Gradient Boosting with XGBoost: Build and train high-performance models using gradient boosting algorithms from the XGBoost framework for improved accuracy.
• Model Deployment & Serving: Productionize your models with Classic MLflow Model Serving. Implement real-time inference to power your applications with deployed models.
• Hyperparameter Tuning: Optimize your models using Hyperopt for efficient hyperparameter tuning. Scale your tuning efforts by running it in a distributed fashion on a Spark cluster using the SparkTrials class.
• MLflow Workflow Management: Understand how to effectively manage your entire machine learning workflow using MLflow, from experimentation to deployment.

By the end of this course, you will be able to:

• Build and train traditional machine learning models on Databricks.
• Utilize MLflow for comprehensive machine learning workflow management.
• Deploy and serve models for real-time inference.
• Optimize model performance through effective hyperparameter tuning.