PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Foundations

• An overview of the Big Data ecosystem and Spark's role in contemporary data platforms
• Comprehending Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinguishing between RDD and DataFrame APIs and determining when to utilize each approach
• Creating and configuring a SparkSession, along with understanding the fundamentals of application configuration

Module 2: PySpark DataFrames

• Reading and writing data to and from enterprise sources and formats (CSV, JSON, Parquet, Delta)
• Manipulating PySpark DataFrames: performing transformations and actions, utilizing column expressions, filtering, joins, and aggregations
• Executing advanced operations such as window functions, managing timestamps, and working with nested data structures
• Implementing data quality checks and writing reusable, maintainable PySpark code

Module 3: Processing Large Datasets Efficiently

• Grasping performance fundamentals: partitioning strategies, shuffle behavior, caching, and persistence
• Utilizing optimization techniques such as broadcast joins and execution plan analysis
• Efficiently processing large datasets 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: addressing missing values, encoding categorical variables, and scaling features
• Designing reusable preprocessing steps and preparing datasets for Machine Learning pipelines
• Introduction to feature selection techniques and handling imbalanced datasets

Module 5: Machine Learning with Spark MLlib

• Understanding the MLlib architecture and the Estimator/Transformer pattern
• Training regression and classification models at scale (Linear Regression, Logistic Regression, Decision Trees, Random Forest)
• Comparing models and interpreting results within distributed Machine Learning workflows

Module 6: End-to-End ML Pipelines

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

Module 7: Model Evaluation & Practical ML Decision Making

• Applying suitable evaluation metrics for regression and classification problems
• Identifying overfitting and underfitting and making practical decisions regarding model selection
• Interpreting feature importance and gaining a deeper understanding of model behavior

Module 8: Production & Enterprise Practices

• Persisting and loading models in Spark
• Implementing batch inference workflows on large datasets
• Understanding the Machine Learning lifecycle within enterprise environments
• An introduction to versioning, experiment tracking concepts, and basic testing strategies

Practical Outcome

• Ability to work independently with PySpark
• Ability to process large datasets efficiently
• Ability to perform feature engineering at scale
• Ability to build scalable Machine Learning pipelines