Module 8: Big Data & Distributed ML

Apache Spark & PySpark

Master Apache Spark fundamentals, PySpark programming, and distributed data processing for large-scale machine learning.

Content by: Nirav Khanpara

AI/ML Engineer

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Spark Architecture

•Spark Core: Distributed computing engine
•Spark SQL: Structured data processing
•Spark Streaming: Real-time data processing
•MLlib: Machine learning library
•GraphX: Graph processing

PySpark Programming

•RDDs (Resilient Distributed Datasets): Core data structure
•DataFrames: Structured data manipulation
•Spark SQL: SQL-like queries on DataFrames
•UDFs (User Defined Functions): Custom functions

Spark Operations

•Transformations: Lazy operations (map, filter, groupBy)
•Actions: Eager operations (collect, count, save)
•Caching: Persist data in memory
•Partitioning: Optimize data distribution

Performance Optimization

•Memory Management: Tune memory allocation
•Partitioning Strategy: Optimize data partitioning
•Broadcast Variables: Share data across nodes
•Accumulators: Global counters and aggregators

🎯 Practice Exercise

Test your understanding of this topic:

I understand the basic concepts covered in this topicI can apply the concepts in practical scenariosI'm ready to move to the next topic

Distributed Computing

Learn distributed computing principles, cluster management, and fault tolerance for large-scale ML systems.

Content by: Nirav Khanpara

AI/ML Engineer

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Distributed Systems Concepts

•Scalability: Handle increasing workloads
•Fault Tolerance: System resilience to failures
•Consistency: Data consistency across nodes
•Availability: System uptime and reliability

Cluster Management

•Resource Allocation: CPU, memory, and storage management
•Load Balancing: Distribute workloads evenly
•Node Management: Add/remove cluster nodes
•Monitoring: Track cluster health and performance

Distributed Algorithms

•MapReduce: Distributed data processing pattern
•Consensus Algorithms: Agreement among distributed nodes
•Distributed Training: Train models across multiple nodes
•Gradient Descent: Distributed optimization

Fault Tolerance

•Replication: Data redundancy across nodes
•Checkpointing: Save intermediate results
•Recovery Mechanisms: Restore from failures
•Health Checks: Monitor node status

🎯 Practice Exercise

Test your understanding of this topic:

I understand the basic concepts covered in this topicI can apply the concepts in practical scenariosI'm ready to move to the next topic

Scalable ML Algorithms

Master scalable machine learning algorithms and techniques for processing large datasets.

Content by: Nirav Khanpara

AI/ML Engineer

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Distributed ML Algorithms

•Distributed Linear Regression: Scale linear models
•Distributed Random Forest: Parallel tree training
•Distributed K-means: Parallel clustering
•Distributed Neural Networks: Parallel neural network training

Federated Learning

•Local Training: Train on local data
•Model Aggregation: Combine local models
•Privacy Preservation: Keep data local
•Communication Efficiency: Minimize data transfer

Online Learning

•Incremental Learning: Update models with new data
•Stochastic Gradient Descent: Online optimization
•Adaptive Learning: Adjust to changing data
•Memory Management: Handle streaming data

Model Parallelism

•Data Parallelism: Distribute data across nodes
•Model Parallelism: Distribute model across nodes
•Pipeline Parallelism: Parallel processing stages
•Hybrid Parallelism: Combine multiple strategies

🎯 Practice Exercise

Test your understanding of this topic:

I understand the basic concepts covered in this topicI can apply the concepts in practical scenariosI'm ready to move to the next topic

Data Engineering for ML

Learn data engineering practices, ETL pipelines, and data infrastructure for machine learning systems.

Content by: Nirav Khanpara

AI/ML Engineer

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Data Pipeline Architecture

•Data Ingestion: Collect data from various sources
•Data Processing: Transform and clean data
•Data Storage: Store processed data efficiently
•Data Serving: Provide data to ML models

ETL/ELT Processes

•Extract: Pull data from source systems
•Transform: Clean and transform data
•Load: Load data into target systems
•Data Quality: Ensure data accuracy and completeness

Data Infrastructure

•Data Lakes: Store raw data in native format
•Data Warehouses: Store processed data for analytics
•Data Marts: Store data for specific use cases
•Streaming Platforms: Process real-time data

Data Governance

•Data Lineage: Track data flow and transformations
•Data Catalog: Metadata management
•Access Control: Manage data access permissions
•Compliance: Ensure regulatory compliance

🎯 Practice Exercise

Test your understanding of this topic:

I understand the basic concepts covered in this topicI can apply the concepts in practical scenariosI'm ready to move to the next topic

Big Data & Distributed ML

Select Topics Overview

Apache Spark & PySpark

Distributed Computing

Scalable ML Algorithms

Data Engineering for ML

Apache Spark & PySpark

Distributed Computing

Scalable ML Algorithms

Data Engineering for ML

Apache Spark & PySpark

Spark Architecture

PySpark Programming

Spark Operations

Performance Optimization

🎯 Practice Exercise

Distributed Computing

Distributed Systems Concepts

Cluster Management

Distributed Algorithms

Fault Tolerance

🎯 Practice Exercise

Scalable ML Algorithms

Distributed ML Algorithms

Federated Learning

Online Learning

Model Parallelism

🎯 Practice Exercise

Data Engineering for ML

Data Pipeline Architecture

ETL/ELT Processes

Data Infrastructure

Data Governance

🎯 Practice Exercise

Module 9: AI Research & Innovation

Additional Resources

📚 Recommended Reading

🌐 Online Resources

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