Limitations to AI - Compute Management
Uncover the hidden challenges of AI, focusing on the computational resources required to train and deploy models. This course highlights the limitations of AI and strategies for efficient compute management.
Learning Objectives:
- Understand the computational demands of AI training and deployment.
- Identify the trade-offs between model size, accuracy, and compute efficiency.
- Explore techniques to optimize compute resources and reduce environmental impact.
Private Course
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Responsible | Kodecoon AI (Hazel) |
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Last Update | 01/07/2025 |
Completion Time | 18 minutes |
Members | 10 |
AI Content Creation
Supporting Module
🧠Theory / fundamentals
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The Power Behind AI Models4Lessons ·
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The Basics of AI Computation: What Powers AI?
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How AI Models Use Data and Process Information
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What Happens When AI Models Get Too Big?
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Quiz yourself!10 xp
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The Cost of Training AI4Lessons ·
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Why Does Training AI Need So Much Power?
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Exploring the Time and Energy Required to Train AI Models
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How Training Scale Affects Compute Resources
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True or False?10 xp
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Striking a Balance: Size, Accuracy, and Efficiency5Lessons · 6 min
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What Are Model Size and Accuracy, and How Are They Linked?
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The Dilemma: Bigger Models or Faster Results?
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Why Smaller AI Models Outshine Oversized LLMs: The Big Problem with Big AI!
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How to Maintain Accuracy While Managing Compute Resources
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Quiz yourself!10 xp
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Optimising AI for Efficiency6Lessons · 8 min
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You Ask, I Answer: How to Make AI More Energy Efficient?
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The Role of Algorithms in Reducing Compute Needs
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How AI Engineers Solve Compute Bottlenecks?
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How Context Windows Impact AI Performance
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What is Prompt Caching, and How Does It Save Resources?
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Quiz yourself!10 xp
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Environmental Impact and Future Solutions3Lessons · 4 min
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How big tech is addressing the rising energy demands of its data centers
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How AI Affects the Environment?
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Quiz yourself!10 xp
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