Working with Data: Collecting, Processing, and Storing Data for AI

Overview

Get equipped with the end-to-end data management skills needed for successful AI initiatives. This comprehensive learning path covers topics from strategic data planning through advanced processing.

Learn how to implement practical techniques for collecting, modeling, and preparing high-quality data for AI applications. Master the complete data management lifecycle required to support sophisticated AI systems in production environments.Build comprehensive AI data strategies.Design, develop, and maintain high-quality data pipelines.Implement secure scalable data architectures for AI systems.

Syllabus

• Introduction to Data Management for AI
Overview of the Data Management Lifecycle
Importance of Data in AI Systems
Course objectives and outcomes
• Strategic Data Planning
Understanding data requirements for AI
Designing a data strategy
Data governance and ethics
Data privacy and compliance
• Data Collection Techniques
Data sources for AI
Data acquisition methodologies
Web scraping and API integration
Sensor and IoT data collection
Handling and monitoring data collection processes
• Data Storage Solutions
Structured vs. unstructured data storage
Relational databases and NoSQL databases
Cloud-based storage options
Data warehousing and data lakes
Design considerations for scalability and security
• Data Processing and Transformation
Data cleaning and pre-processing
Data integration and ETL (Extract, Transform, Load) processes
Feature engineering for AI models
Data normalization and augmentation
Handling imbalanced datasets
• Building and Maintaining Data Pipelines
Pipeline design principles
Tools and frameworks: Apache Kafka, Apache Airflow, etc.
Real-time vs. batch processing
Monitoring and maintaining data pipelines
• Data Quality and Validation
Defining data quality metrics
Data validation techniques
Ensuring data consistency and reliability
Tools for data quality monitoring
• Implementing Scalable Data Architectures
Designing for scalability in AI systems
Distributed data systems and processing
Utilizing big data technologies: Hadoop, Spark, etc.
Case studies of scalable AI architectures
• Final Project: Designing a Comprehensive Data Strategy for AI
Developing a project proposal
Implementing data collection and pipeline design
Creating a scalable architecture plan
Presentation and feedback session
• Conclusion and Future Trends in AI Data Management
Emerging technologies in data management for AI
Future challenges and opportunities
Course summary and key takeaways

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Taught by

Dan Sullivan, Joe Squire, Brandeis Marshall, PhD, EMBA, Janani Ravi and Kumaran Ponnambalam

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Subjects

Computer Science