Section 1
AI and ML Fundamentals
Preview
Preview: Fundamentals of AI and ML
- 3 of 3 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Learning Paradigms and Inference
- Deployment and Inference Options
- ML Lifecycle and Monitoring
Overview
The exam tests the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), essential for understanding how AWS AI services function and how to apply them effectively. It covers different learning paradigms, deployment strategies, and the lifecycle of ML models, emphasizing practical considerations for building and maintaining AI solutions.
Core Concepts
- **Learning Paradigms:** Understand the distinctions between supervised learning (labeled data), unsupervised learning (unlabeled data), and reinforcement learning (agent-environment interaction). Recognize when each paradigm is appropriate.
- **Inference:** This is the process of using a trained model to generate predictions on new data. It's distinct from training, which adjusts model parameters.
- **Deep Learning:** A subset of ML using multi-layered neural networks to learn complex patterns. It often requires significant computational resources.
Exam Tips
- Carefully analyze the scenario to determine whether labeled data is available, which will guide your choice of learning paradigm.
- Consider the latency requirements and traffic patterns when selecting a deployment option.