PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Fundamentals

• Exploration of the Big Data landscape and Spark’s position within current data platforms
• Detailed analysis of Spark architecture, covering drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Comparison of RDD and DataFrame APIs, including guidance on when to apply each paradigm
• Initialization and configuration of SparkSession, along with key application setting parameters

Module 2: PySpark DataFrames

• Ingestion and export of data from enterprise sources and formats (CSV, JSON, Parquet, Delta)
• Manipulating PySpark DataFrames through transformations, actions, column expressions, filtering, joins, and aggregations
• Executing advanced tasks such as window functions, timestamp management, and nested data handling
• Incorporating data validation checks and writing clean, maintainable PySpark code

Module 3: Efficient Large Dataset Processing

• Mastering performance basics: partitioning strategies, shuffle mechanics, caching, and persistence
• Leveraging optimization tactics such as broadcast joins and analyzing execution plans
• Best practices for scalable data workflows and high-efficiency processing of large data volumes
• Insights into schema evolution and contemporary storage formats prevalent in enterprise settings

Module 4: Scalable Feature Engineering

• Applying Spark MLlib for feature engineering: managing missing data, encoding categorical features, and scaling variables
• Architecting reusable preprocessing steps to ready datasets for Machine Learning pipelines
• Foundations of feature selection and strategies for managing imbalanced data

Module 5: Machine Learning with Spark MLlib

• Understanding MLlib’s internal structure and the Estimator/Transformer design pattern
• Training regression and classification models at scale (Linear Regression, Logistic Regression, Decision Trees, Random Forest)
• Benchmarking models and interpreting outcomes in distributed Machine Learning contexts

Module 6: End-to-End ML Pipelines

• Constructing complete Machine Learning pipelines that integrate preprocessing, feature engineering, and modeling
• Implementing train/validation/test partitioning strategies
• Executing cross-validation and hyperparameter optimization via grid and random search methods
• Establishing frameworks for reproducible Machine Learning experimentation

Module 7: Model Evaluation & Strategic ML Decisions

• Selecting and applying suitable evaluation metrics for regression and classification tasks
• Detecting overfitting and underfitting to guide practical model selection
• Analyzing feature importance to understand model dynamics

Module 8: Production & Enterprise Standards

• Saving and retrieving models within the Spark environment
• Developing batch inference workflows for large-scale data processing
• Navigating the Machine Learning lifecycle in corporate environments
• Introduction to model versioning, experiment tracking, and foundational testing protocols

Practical Outcome

• Proficiency in independently utilizing PySpark
• Capability to process extensive datasets with high efficiency
• Skill in executing feature engineering at scale
• Ability to develop scalable Machine Learning pipelines