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Overview
In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.
Machine Learning Development with Spark Distributed Model Tuning on Databricks Deploying Machine Learning Models with Spark Pandas on Spark
The content was developed for participants with these skills/knowledge/abilities:
Coming Soon...
Machine Learning Development with Spark A Brief Overview of Spark Architecture for Machine Learning Introduction to Spark ML for Model Development Model Tracking and Packaging with MLflow and Unity Catalog on Databricks Model Development with Spark Distributed Model Tuning on Databricks Overview of Hyperparameter Tuning Scalable HPO Frameworks on Databricks Optuna and Hyperopt with Spark ML HPO with Ray Tune Deploying Machine Learning Models with Spark Deployment with Spark Inference with Spark Model Deployment with Spark Optimization Strategies with Spark and Delta Lake Model Deployment with Spark Pandas on Spark Scaling with Pandas APIs Pandas UDFs and Function APIs Pandas APIs
Machine Learning Development with Spark
Distributed Model Tuning on Databricks
Deploying Machine Learning Models with Spark
Pandas on Spark
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