Scaling Machine Learning with Spark

Learn how to build end-to-end scalable machine learning solutions with Apache Spark through the course. The course introduces data and ML practitioners to creative solutions that go beyond traditional methods, fostering collaboration and understanding between practitioners. You will explore distributed ML workflows using technologies like Spark MLlib, MLflow, TensorFlow, and PyTorch. The course covers topics such as managing the ML lifecycle, data ingestion, preprocessing, feature engineering, model training, and combining Spark with deep learning. It provides practical examples and insights for data scientists working with machine learning. The teaching method involves a step-by-step approach, practical examples, and insights from industry experts. This course is intended for data and ML practitioners looking to enhance their skills in building scalable machine learning solutions using Apache Spark.