What I’m Learning as an AI Developer in 2023
AI Summary
- AI Development Evolution
- AI development skillset has changed since 2018.
- Success now requires different skills.
- The speaker earns about $10k/month by adapting to these changes.
- Key Learning Areas for AI Developers in 2023
- Software Architecture
- Focus on design patterns and architectural styles.
- AI can handle coding but not design and architecture.
- Importance of understanding trade-offs in architecture.
- AI models are not built from scratch; use commercial/open-source solutions.
- Codebases must be adaptable for integrating different AI models.
- Recommended resources:
- “Fundamentals of Software Architecture” by Mark Richards and Neal Ford.
- Software architecture courses on Coursera.
- Edge AI
- Running AI on local devices (e.g., smartphones, IoT).
- Trend towards utilizing device compute power for AI.
- Potential for AI to control apps and perform actions.
- Importance of Apple’s Core ML and advancements in machine learning frameworks.
- Recommended reading: “AI at the Edge.”
- Web Development
- Most AI projects require a user interface.
- Backend AI implementations are often covered by tools like Google’s AutoML.
- Success depends on user interface convenience.
- Web development allows broad access through browsers.
- Focus on frameworks like Next.js built on React.
- YouTube as a resource for web development learning.
- Python libraries like Streamlit and Gradio for simpler UIs.
- Integration of AI solutions is key, not building AI from scratch.
- Leveraging OpenAI for business growth while addressing data privacy concerns.
- Microsoft Azure’s OpenAI service ensures data privacy.
- Conclusion
- The speaker focuses on integrating existing AI solutions.
- Emphasizes the irrelevance of training models from scratch compared to OpenAI.
- Encourages watching further videos for insights and encourages subscribing.