PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Foundations

• Examination of the Big Data landscape and the integration of Spark within contemporary data architectures
• Analysis of Spark core components, including driver processes, executors, cluster management, lazy evaluation, DAGs, and execution planning
• Distinction between RDD and DataFrame APIs, with guidelines for selecting the appropriate methodology for specific tasks
• Initialization of SparkSession and adherence to fundamental application configuration standards

Module 2: PySpark DataFrames

• Ingestion and export of data from institutional sources utilizing standard formats (CSV, JSON, Parquet, Delta)
• Management of PySpark DataFrames through transformations, actions, column expressions, filtering, joins, and aggregation techniques
• Application of advanced operations, including window functions, timestamp management, and nested data structures
• Implementation of data integrity validations and development of maintainable, reusable PySpark code for government applications

Module 3: Processing Large Datasets Efficiently

• Assessment of performance principles, such as partitioning methods, shuffle mechanics, caching, and persistence strategies
• Utilization of optimization techniques, including broadcast joins and detailed execution plan analysis
• Optimization of large dataset processing and adherence to best practices for scalable data workflows
• Review of schema evolution and contemporary storage formats employed in enterprise and public sector environments

Module 4: Feature Engineering at Scale

• Execution of feature engineering using Spark MLlib, covering missing value imputation, categorical variable encoding, and feature scaling
• Construction of reusable preprocessing steps and preparation of datasets for Machine Learning pipelines
• Introduction to feature selection methods and strategies for addressing imbalanced datasets

Module 5: Machine Learning with Spark MLlib

• Comprehension of MLlib architecture and the Estimator-Transformer design pattern
• Training of regression and classification models at scale, including Linear Regression, Logistic Regression, Decision Trees, and Random Forests
• Comparative analysis of models and interpretation of results within distributed Machine Learning contexts

Module 6: End-to-End ML Pipelines

• Development of comprehensive Machine Learning pipelines integrating preprocessing, feature engineering, and model training
• Application of train/validation/test partitioning strategies
• Execution of cross-validation and hyperparameter optimization using grid search and random search methods
• Structuring of reproducible Machine Learning experiments to ensure accountability and transparency

Module 7: Model Evaluation & Practical ML Decision Making

• Selection and application of appropriate evaluation metrics for regression and classification objectives
• Identification of overfitting and underfitting to support informed model selection decisions
• Interpretation of feature importance and analysis of model behavior for decision-making for government agencies

Module 8: Production & Enterprise Practices

• Persistence and retrieval of models within Spark environments
• Implementation of batch inference workflows for large-scale datasets
• Understanding the Machine Learning lifecycle within enterprise and public sector operational frameworks
• Introduction to model versioning, experiment tracking, and foundational testing strategies

Practical Outcome

• Proficiency in independently managing PySpark operations
• Capability to process large-scale datasets with efficiency and accuracy
• Skill in performing feature engineering at scale for analytical purposes
• Competence in constructing scalable Machine Learning pipelines suitable for government deployment