Sustainable AI for Developers: Strategies, Techniques, and Best Practices
1h 19mIntermediate2024-10-23
Authors
Fawad Qureshi
Technology Strategist | Analytics, Cloud, Big Data
Course details
AI has moved from curiosity to a necessity. It is no longer a side hustle of IT departments within organizations—it’s a board-level agenda. This means AI will become ubiquitous in all walks of life. When discussing aspects of responsible AI, the environmental impact of AI proliferation must be considered.
This comprehensive course explores design approaches to AI that balance environmental impact, innovation, and profitability. Instructor Fawad Qureshi delves into the concept of sustainable AI, exploring its significance and impact. Gain insights into the background and recent developments in this field, particularly with the rise of generative AI. The course provides a holistic understanding of AI's environmental footprint, emphasizing the importance of optimizing resource consumption and adopting sustainable practices. Following these best practices will not only save energy consumption but also reduce your AI expenses.
Learning objectives
Understand the core concepts and principles of sustainable AI, its expansion from responsible AI, and its increasing relevance in the era of generative AI.
Analyze the investment trends and adoption rates of AI across various industries, highlighting the urgency of addressing sustainability concerns.
Examine the environmental impact of AI systems, including energy consumption, hardware requirements, and the consequences of resource overcompensation.
Explore the fundamentals of AI, machine learning, deep learning, computer vision, natural language processing, large language models, blockchain, and GeoAI.
Assess the data flow and resource utilization within AI systems, identifying bottlenecks and opportunities for optimization.
Implement strategies and techniques for reducing resource consumption.
Evaluate scenarios and contexts where AI technologies are most beneficial.
This comprehensive course explores design approaches to AI that balance environmental impact, innovation, and profitability. Instructor Fawad Qureshi delves into the concept of sustainable AI, exploring its significance and impact. Gain insights into the background and recent developments in this field, particularly with the rise of generative AI. The course provides a holistic understanding of AI's environmental footprint, emphasizing the importance of optimizing resource consumption and adopting sustainable practices. Following these best practices will not only save energy consumption but also reduce your AI expenses.
Learning objectives
Understand the core concepts and principles of sustainable AI, its expansion from responsible AI, and its increasing relevance in the era of generative AI.
Analyze the investment trends and adoption rates of AI across various industries, highlighting the urgency of addressing sustainability concerns.
Examine the environmental impact of AI systems, including energy consumption, hardware requirements, and the consequences of resource overcompensation.
Explore the fundamentals of AI, machine learning, deep learning, computer vision, natural language processing, large language models, blockchain, and GeoAI.
Assess the data flow and resource utilization within AI systems, identifying bottlenecks and opportunities for optimization.
Implement strategies and techniques for reducing resource consumption.
Evaluate scenarios and contexts where AI technologies are most beneficial.
Skills covered
Sustainability AwarenessBusiness StrategyArtificial Intelligence FoundationsArtificial Intelligence for BusinessArtificial Intelligence (AI)Professional DevelopmentBusiness Analysis and StrategyLeadership and ManagementOne-Off
Concepts
0. Introduction
- 01 - From rivers to AI
- 02 - Don't throw hardware at a software problem
- 03 - Waves of AI ethics
1. Artificial Intelligence
- 04 - What is artificial intelligence
- 05 - Classifying the world of AI
2. Managing AI Energy Demand
- 06 - Managing AI energy demand
- 07 - High-level approaches
- 08 - Common design practices
3. Machine Learning
- 09 - What is machine learning
- 10 - Types of machine learning algorithms
- 11 - When best to use and not use machine learning
- 12 - Best practices to optimize consumption
4. Deep Learning
- 13 - What is deep learning
- 14 - Types of deep learning models
- 15 - When to use and not use deep learning
- 16 - Best practices to optimize consumption
5. Computer Vision
- 17 - What is computer vision
- 18 - Types of computer vision algorithms
- 19 - Best practices to optimize consumption
6. Natural Language Processing
- 20 - What is natural language processing
- 21 - Approaches to NLP
- 22 - Best practices to optimize consumption
7. GenAI and Large Language Models (LLM)
- 23 - GenAI vs. LLM vs. foundation model
- 24 - When to use and not use GenAI and LLMs
- 25 - Best practices to optimize consumption
8. Geospatial Analysis
- 26 - What is geospatial analysis
- 27 - What is GeoAI
- 28 - Why is geospatial analysis resource intensive
- 29 - Best practices to optimize consumption
9. Capstone Project - Insurance Underwriting Case Study
- 30 - Challenge - Identify optimization opportunities
- 31 - Solution - Identify optimization opportunities
Conclusion
- 32 - Measure to manage
- 33 - Next steps