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 modern 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 use each
• Establishing and configuring SparkSession along with understanding the fundamentals of application configuration

Module 2: PySpark DataFrames

• Ingesting and exporting data from enterprise sources and various formats (CSV, JSON, Parquet, Delta)
• Manipulating PySpark DataFrames: utilizing transformations, actions, column expressions, filtering, joins, and aggregations
• Executing advanced operations like window functions, managing timestamps, and handling 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
• Applying 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: managing missing values, encoding categorical variables, and scaling features
• Designing reusable preprocessing steps and preparing datasets for Machine Learning pipelines
• An introduction to feature selection and strategies for handling imbalanced datasets

Module 5: Machine Learning with Spark MLlib

• Understanding 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
• Implementing train/validation/test split strategies
• Executing cross-validation and hyperparameter tuning via 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 challenges
• Identifying overfitting and underfitting and making informed decisions regarding model selection
• Interpreting feature importance and gaining insights into model behavior

Module 8: Production & Enterprise Practices

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

Practical Outcome

• Competence in working autonomously with PySpark
• Proficiency in processing large datasets efficiently
• Capability to perform feature engineering at scale
• Skill in building scalable Machine Learning pipelines