Advanced Machine Learning with Databricks DBMLP-QA

DBMLP-QA
Price on request

This course is designed for data scientists and machine learning practitioners seeking to scale machine learning workflows and implement MLOps best practices using Databricks. The course is delivered over two four-hour modules, covering Apache Spark for ML, hyperparameter tuning with Optuna, and MLOps automation with Databricks tools such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. Participants will gain hands-on experience with Spark ML, pandas APIs on Spark, MLflow, and Unity Catalog, ensuring effective model tracking, governance, and deployment.

Explain Apache Spark’s architecture and its role in scalable machine learning.
Develop ML models using Spark ML and pandas APIs on Spark.
Perform hyperparameter tuning with Optuna on Spark.
Leverage MLflow and Unity Catalog for model tracking, packaging, and governance.
Implement MLOps best practices, including CI/CD, pipeline management, and environment separation.
Deploy and monitor ML models with Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving.
Use model rollout strategies, A/B testing, and drift detection for production ML.

Advanced Machine Learning with Databricks

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