Explore AI for Data Engineering

Overview

Unlock the power of AI in the world of data engineering with this comprehensive learning path. This course series is designed for data professionals who want to harness the transformative potential of artificial intelligence in their work. Whether you're looking to enhance your current role or pivot into AI-driven data engineering, this learning path equips you with the knowledge and skills to thrive in the evolving landscape of data and AI.Build a foundation in AI relevant to data engineering.Harness the power of generative AI in your data workflows.Explore vector databases through practical exercises.

Syllabus

• Introduction to AI in Data Engineering
Overview of AI technologies
The role of AI in data engineering
Key benefits and challenges
• Foundations of AI for Data Professionals
Machine learning basics
Deep learning concepts
AI algorithms relevant to data engineering
• AI-Driven Data Processing
Data pre-processing using AI
Automating data cleaning tasks
AI-enhanced ETL processes
• Generative AI in Data Workflows
Understanding generative AI models
Applications of generative AI in data engineering
Case studies: Successful implementations
• Vector Databases and AI
Introduction to vector databases
Applications of vectorization in AI
Hands-on with vector databases: Practical exercises
• Tools and Technologies for AI in Data Engineering
Overview of popular AI tools (e.g., TensorFlow, PyTorch)
AI platforms and cloud services (e.g., AWS, Google Cloud)
Integration of AI tools with existing data pipelines
• Building AI-Powered Data Pipelines
Designing AI-driven architectures
Case studies: AI in large-scale data systems
Implementation strategies and best practices
• Ethical and Responsible AI in Data Engineering
Understanding bias and fairness in AI models
Data privacy and security in AI systems
Developing ethical AI applications
• Future Trends in AI and Data Engineering
Evolution of AI technologies
Emerging trends and innovations
Preparing for the future of AI in data engineering
• Final Project: Applying AI to a Real-World Data Engineering Problem
Project overview and guidelines
Team collaboration and project management
Presentation and evaluation of project outcomes

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Taught by

Sadie St. Lawrence, Aishwarya Srinivasan, Priya Ranjani Mohan, Deepak Goyal, Zain Hasan, JP Hwang and Yujian Tang

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Subjects

Data Science