PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Foundations of Big Data & Spark

• A survey of the Big Data ecosystem and Spark's pivotal role in modern data platforms
• Comprehending Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinguishing between RDD and DataFrame APIs and determining the appropriate use cases for each
• Establishing and configuring SparkSession alongside understanding the basics of application configuration

Module 2: PySpark DataFrames

• Ingesting and exporting data from enterprise sources and various formats (CSV, JSON, Parquet, Delta)
• Manipulating PySpark DataFrames: performing transformations, actions, column expressions, filtering, joins, and aggregations
• Executing advanced operations such as window functions, managing timestamps, and handling nested data structures
• Implementing data quality checks and drafting reusable, maintainable PySpark code

Module 3: Efficient Processing of Large Datasets

• Grasping performance fundamentals: partitioning strategies, shuffle mechanics, caching, and persistence
• Utilizing optimization techniques such as broadcast joins and execution plan analysis
• Processing large datasets effectively and adhering to best practices for scalable data workflows
• Understanding schema evolution and modern storage formats prevalent in enterprise environments

Module 4: Feature Engineering at Scale

• Conducting feature engineering with Spark MLlib: managing missing values, encoding categorical variables, and scaling features
• Designing reusable preprocessing steps and preparing datasets for integration into Machine Learning pipelines
• An overview of feature selection techniques and strategies for handling imbalanced datasets

Module 5: Machine Learning with Spark MLlib

• Training regression and classification models at scale, including Linear Regression, Logistic Regression, Decision Trees, and Random Forests
• Comparing models and interpreting outcomes within distributed Machine Learning workflows

Module 6: End-to-End ML Pipelines

• Constructing comprehensive Machine Learning pipelines that combine preprocessing, feature engineering, and modeling
• Applying train/validation/test split strategies
• Conducting cross-validation and hyperparameter tuning using grid search and random search methods
• Structuring reproducible Machine Learning experiments

Module 7: Model Evaluation & Practical ML Decision Making

• Selecting appropriate evaluation metrics for regression and classification challenges
• Detecting overfitting and underfitting, and making informed decisions regarding model selection
• Interpreting feature importance and gaining insight into model behavior

Module 8: Production & Enterprise Practices

• Saving and loading models within Spark
• Implementing batch inference workflows on extensive datasets
• Understanding the Machine Learning lifecycle within enterprise contexts
• An introduction to versioning, experiment tracking concepts, and fundamental testing strategies

Practical Outcome