Data Workloads with Repos and Workflows

This course teaches you how to scale ML pipelines with Spark, including distributed training, hyperparameter tuning, and inference. You will build and tune ML models with SparkML while leveraging MLflow to track, version, and manage these models. This course covers the latest ML features in Apache Spark, such as Pandas UDFs, Pandas Functions, and the pandas API on Spark, as well as the latest ML product offerings, such as Feature Store and AutoML.  

Welcome to Machine Learning with Databricks!This course is your gateway to mastering machine learning workflows on Databricks. Dive into data preparation, model development, deployment, and operations, guided by expert instructors. Learn essential skills for data exploration, model training, and deployment strategies tailored for Databricks. By course end, you'll have the knowledge and confidence to navigate the entire machine learning lifecycle on the Databricks platform, empowering you to build and deploy robust machine learning solutions efficiently.

This course begins by covering the basics of neural networks and the tensorflow.keras API. We will then focus on using Spark to scale our models, including distributed training, hyperparameter tuning, and inference, and the meanwhile leveraging MLflow to track, version, and manage these models. You will apply model interpretability libraries to explain model predictions. Further, you will learn the concepts behind Convolutional Neural Networks (CNNs) and transfer learning, and apply them to solve image classification tasks. We will wrap up the course by covering Recurrent Neural Networks (RNNs) and attention-based models for natural language processing (NLP) applications.