Machine Learning with Spark Mllib

This session covers the essential aspects of using Apache Spark for machine learning tasks. We discuss the foundational components that enable efficient and scalable processing.

We explore the architecture and capabilities of Spark MLlib, which provides a comprehensive framework for building and deploying machine learning models.

We dive into the Spark ML idiom, focusing on key concepts such as transformers, estimators, and pipelines. These constructs form the backbone of Spark’s machine learning workflow, enabling seamless integration with various algorithms.

Data preprocessing is a critical step in machine learning, particularly when dealing with large datasets. We discuss feature engineering at scale, leveraging Spark’s capabilities to efficiently handle data transformation, scaling, and storage requirements.

We examine the process of running large-scale machine learning algorithms on Spark, covering regression, classification, and clustering techniques. This involves understanding how to configure and optimize these algorithms for maximum performance and efficiency.

Model evaluation and hyperparameter tuning are crucial components in any machine learning workflow. We discuss the importance of evaluating model performance using various metrics and techniques, as well as strategies for hyperparameter tuning to optimize model performance.

Finally, we address the topic of exporting and deploying Spark models into production environments. This involves understanding how to deploy models in scalable and fault-tolerant configurations, enabling organizations to leverage machine learning capabilities without compromising system reliability or performance.

Reading and Listening

  1. Textbook: Apache Spark for Machine Learning, Deepak Gowda, November 2024, Packt Publishing.
  2. Chapter 8: Mining Frequent Patterns
  3. Chapter 9: Model Deployment

Additional Resources