
"Agentic AI systems are changing what's possible for professionals without engineering backgrounds. Tasks that required hiring a developer, like pulling data from APIs, cleaning datasets, and building recommendation systems, can now be done by people who understand their domain but have never written production code. The constraint isn't coding ability anymore. It's understanding how these systems work and how to direct them effectively."
"This shift creates an opportunity, but only for those who understand the fundamentals. Prompting an agent to "build a RAG system" without knowing what embeddings are or how vector databases work produces unreliable results. The people who will benefit from agentic AI aren't those who treat it as magic. They're the ones who understand data preparation, evaluation methods, and system architecture well enough to verify outputs and debug problems."
"This session establishes the core concepts behind modern AI and machine learning. Participants learn how supervised learning, unsupervised learning, and deep learning differ, when each approach applies, and what tradeoffs they involve. The session covers why models need properly structured data, how training and evaluation work, and what determines model performance."
Agentic AI systems enable professionals without engineering backgrounds to perform tasks like pulling data from APIs, cleaning datasets, and building recommendation systems. The primary constraint shifts from coding ability to understanding agent behavior, embeddings, vector databases, data preparation, evaluation methods, and system architecture. Prompting agents without those fundamentals yields unreliable results. ODSC AI East 2026 Mini Bootcamp provides eight weekly sessions from February 24 through April 9 that teach AI and machine learning fundamentals, Python programming with coding assistants, and practical techniques for verifying outputs, debugging agentic systems, and directing agents to build reliable applications.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]