Apache Spark™ Programming with Databricks

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.

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.

This course focuses on the fundamentals of preparing data for machine learning using Databricks. Participants will learn essential skills for exploring, cleaning, and organizing data tailored for traditional machine learning applications. Key topics include data visualization, feature engineering, and optimal feature storage strategies. Through practical exercises, participants will gain hands-on experience in efficiently preparing data sets for machine learning within the Databricks. This course is designed for associate-level data scientists and machine learning practitioners. and individuals seeking to enhance their proficiency in data preparation, ensuring a solid foundation for successful machine learning model deployment.