Data Engineering for AI Training in Auckland, New Zealand

This course offers a deep dive into the principles and practices of data engineering as applied to AI training workflows. Participants will explore the entire data lifecycle, from ingestion and storage to transformation and validation, focusing on creating high-quality datasets that drive successful AI models. The curriculum covers essential technologies, architectural patterns, and best practices for building scalable and reliable data pipelines.

Introduction to Data Engineering for AI

• Role of data engineering in AI lifecycle
• Key concepts: data pipelines, ETL, ELT
• Overview of data sources and formats
• Data governance and compliance basics

Data Ingestion and Collection

• Batch vs. streaming data ingestion
• Connecting to various data sources
• Data extraction techniques
• Handling schema evolution

Data Storage and Management

• Relational and NoSQL databases
• Data lakes and data warehouses
• Data partitioning and indexing
• Data versioning and cataloging

Data Transformation and Cleaning

• Data wrangling and normalization
• Handling missing values and outliers
• Data deduplication
• Feature scaling and encoding

Data Quality and Validation

• Defining data quality dimensions
• Automated validation checks
• Data profiling and monitoring
• Error handling and logging

Feature Engineering for AI

• Feature extraction from raw data
• Creating derived features
• Feature selection techniques
• Handling imbalanced data

Data Pipeline Orchestration

• Workflow management tools
• Dependency and scheduling
• Pipeline monitoring and alerting
• Scaling pipelines

Data Security and Compliance

• Data encryption and access control
• Anonymization and masking
• Regulatory considerations (GDPR, CCPA)
• Audit trails

Capstone: End-to-End Data Pipeline

• Integrating all concepts
• Building a complete pipeline for AI training
• Testing and validation
• Best practices review