Learn how to implement machine learning on large-scale datasets using Apache Spark, Dask, and distributed learning frameworks
Series Overview
This series is a practical educational content consisting of 5 chapters that allows you to learn the theory and implementation of large-scale data processing and distributed machine learning systematically from the basics.
Large-scale data processing is a technology for efficiently processing and analyzing datasets that cannot be handled by a single machine. Distributed data processing with Apache Spark, Python-native parallel processing with Dask, distributed deep learning with PyTorch Distributed and Horovod - these technologies have become essential skills in modern data science and machine learning. You will understand and be able to implement technologies that companies like Google, Netflix, and Uber use to process data ranging from several terabytes to several petabytes. We provide practical knowledge from Spark processing using RDD, DataFrame, and Dataset APIs, parallel computing with Dask arrays and dataframes, distributed deep learning combining Data Parallelism and Model Parallelism, to building end-to-end large-scale ML pipelines.
Features:
- ✅ Theory to Practice: Systematic learning from scalability challenges to implementation and optimization
- ✅ Implementation-Focused: Over 40 executable Python/Spark/Dask/PyTorch code examples
- ✅ Practical Orientation: Practical workflows assuming real large-scale datasets
- ✅ Latest Technology Compliance: Implementation using Apache Spark 3.5+, Dask 2024+, PyTorch 2.0+
- ✅ Practical Applications: Practice in distributed processing, parallelization, distributed learning, and performance optimization
Total Learning Time: 5.5-6.5 hours (including code execution and exercises)
How to Proceed with Learning
Recommended Learning Order
For Beginners (completely new to large-scale data processing):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Required Time: 5.5-6.5 hours
For Intermediate Learners (with basic experience in Spark/Dask):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Required Time: 4.5-5.5 hours
Strengthening Specific Topics:
- Scalability and distributed processing basics: Chapter 1 (intensive study)
- Apache Spark: Chapter 2 (intensive study)
- Dask parallel processing: Chapter 3 (intensive study)
- Distributed deep learning: Chapter 4 (intensive study)
- End-to-end pipeline: Chapter 5 (intensive study)
- Required Time: 65-80 minutes/chapter
Chapter Details
Chapter 1: Fundamentals of Large-Scale Data Processing
Difficulty: Intermediate
Reading Time: 65-75 minutes
Code Examples: 7
Learning Content
- Scalability Challenges - Memory constraints, computational time, I/O bottlenecks
- Distributed Processing Concepts - Data parallelism, model parallelism, task parallelism
- Parallelization Strategies - MapReduce, partitioning, shuffle
- Distributed System Architecture - Master-Worker, shared-nothing, consistency
- Performance Metrics - Scale-out efficiency, Amdahl's law
Learning Objectives
- ✅ Understand the challenges of large-scale data processing
- ✅ Explain basic concepts of distributed processing
- ✅ Select appropriate parallelization strategies
- ✅ Understand characteristics of distributed systems
- ✅ Quantitatively evaluate scalability
Chapter 2: Apache Spark
Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 10
Learning Content
- Spark architecture - Driver, Executor, Cluster Manager
- RDD (Resilient Distributed Dataset) - Transformations, actions
- DataFrame API - Structured data processing, Catalyst Optimizer
- MLlib - Distributed machine learning, Pipeline API
- Spark Performance Optimization - Caching, partitioning, broadcast
Learning Objectives
- ✅ Understand Spark architecture
- ✅ Appropriately use RDD and DataFrame
- ✅ Implement distributed machine learning with MLlib
- ✅ Optimize Spark jobs
- ✅ Identify and resolve performance bottlenecks
Chapter 3: Dask
Difficulty: Intermediate
Reading Time: 65-75 minutes
Code Examples: 9
Learning Content
- Dask arrays/dataframes - NumPy/Pandas compatible API, lazy evaluation
- Parallel computing - Task graphs, scheduler, workers
- Dask-ML - Parallel hyperparameter tuning, incremental learning
- Dask Distributed - Cluster configuration, dashboard
- NumPy/Pandas Integration - Out-of-core computation, chunk processing
Learning Objectives
- ✅ Understand Dask data structures
- ✅ Utilize task graphs and lazy evaluation
- ✅ Implement parallel machine learning with Dask-ML
- ✅ Configure and manage Dask clusters
- ✅ Efficiently execute out-of-core computations
Chapter 4: Distributed Deep Learning
Difficulty: Advanced
Reading Time: 70-80 minutes
Code Examples: 9
Learning Content
- Data parallelism - Mini-batch splitting, gradient synchronization, AllReduce
- Model parallelism - Layer splitting, pipeline parallelism
- PyTorch DDP - DistributedDataParallel, process groups
- Horovod - Ring AllReduce, TensorFlow/PyTorch integration
- Distributed Training Optimization - Communication reduction, gradient compression, mixed precision
Learning Objectives
- ✅ Understand data parallelism and model parallelism
- ✅ Implement distributed training with PyTorch DDP
- ✅ Execute large-scale training using Horovod
- ✅ Minimize communication overhead
- ✅ Evaluate scaling efficiency of distributed training
Chapter 5: Practice: Large-Scale ML Pipeline
Difficulty: Advanced
Reading Time: 70-80 minutes
Code Examples: 8
Learning Content
- End-to-end distributed training - Data loading, preprocessing, training, evaluation
- Performance optimization - Profiling, bottleneck analysis
- Large-Scale Feature Engineering - Spark ML Pipeline, feature store
- Distributed Hyperparameter Tuning - Optuna, Ray Tune
- Practical Project - Model training on datasets with hundreds of millions of rows
Learning Objectives
- ✅ Build end-to-end large-scale ML pipelines
- ✅ Identify and resolve performance bottlenecks
- ✅ Implement large-scale feature engineering
- ✅ Execute distributed hyperparameter tuning
- ✅ Implement large-scale ML at real project level
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain challenges of large-scale data processing and scalability concepts
- ✅ Understand basic principles of distributed processing, parallelization, and distributed learning
- ✅ Explain characteristics and use cases of Apache Spark, Dask, and PyTorch DDP
- ✅ Understand differences between data parallelism and model parallelism
- ✅ Explain performance evaluation methods for distributed systems
Practical Skills (Doing)
- ✅ Perform distributed processing using RDD/DataFrame in Apache Spark
- ✅ Execute parallel computations compatible with NumPy/Pandas using Dask
- ✅ Implement distributed deep learning with PyTorch DDP and Horovod
- ✅ Execute distributed machine learning with MLlib and Dask-ML
- ✅ Build ML pipelines for large-scale datasets
Application Ability (Applying)
- ✅ Select appropriate processing methods based on data scale
- ✅ Identify and optimize bottlenecks in distributed processing
- ✅ Evaluate and improve scaling efficiency
- ✅ Design end-to-end large-scale ML systems
- ✅ Execute professional-level large-scale data processing projects
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, classes, modules
- ✅ NumPy/Pandas Basics: Array operations, DataFrame processing
- ✅ Machine Learning Fundamentals: Training, evaluation, hyperparameter tuning
- ✅ scikit-learn/PyTorch: Experience implementing model training
- ✅ Command Line Operations: bash, basic terminal operations
Recommended (Nice to Have)
- 💡 Distributed Systems Basics: MapReduce, parallel processing concepts
- 💡 Docker Basics: Containers, images, Dockerfile
- 💡 Kubernetes Basics: Pod, Service (when using Spark on K8s)
- 💡 Deep Learning Basics: Neural networks, gradient descent
- 💡 Cloud Basics: AWS, GCP, Azure (when using EMR, Dataproc)
Recommended Prior Learning:
- 📚 - ML fundamental knowledge