Key Takeaways
- Build strong programming foundations — Learn Python, SQL, Git, Linux basics, REST APIs, and core data structures before moving deeply into AI.
- Master machine learning and deep learning — Understand data handling, model evaluation, neural networks, transformers, and practical ML workflows.
- Develop Generative AI and LLM skills — Learn embeddings, RAG, vector search, tool calling, structured outputs, LLM evaluation, and AI agents.
- Learn deployment, MLOps, and AI security — AI engineers need to understand Docker, APIs, cloud deployment, monitoring, model versioning, cost management, security, and responsible AI.
- Build a practical AI portfolio — Three to five strong, deployed projects can demonstrate your ability to turn AI concepts into reliable, real-world applications.
