Machine Learning for Azure Databricks

The Machine Learning for Azure Databricks course is designed to help data scientists, ML engineers, and AI professionals build, train, and deploy machine learning models using Azure Databricks and Apache Spark. This course covers big data processing, feature engineering, ML model training, and MLOps integration to create scalable AI solutions in the cloud.

Who Should Enroll Now?

• Data Scientists working with large-scale machine learning models.
• ML Engineers looking to optimize workflows in Azure Databricks.
• Data Engineers integrating machine learning into big data pipelines.
• AI Professionals deploying and managing ML models in production.

Key Learning Objectives:

• Understand Azure Databricks and its Apache Spark-based architecture.
• Implement machine learning workflows using MLflow for experiment tracking.
• Perform feature engineering and data preprocessing in big data environments.
• Train and optimize ML models for high-performance scalability.
• Deploy and monitor ML models using MLOps best practices.

Why Choose This Course?
✅ Hands-on real-world ML use cases with Azure Databricks.
✅ Covers ML model lifecycle management from training to deployment.
✅ Learn best practices for big data and machine learning at scale.
✅ Gain expertise in MLflow, Apache Spark, and MLOps automation.