ai-ml-planning

Conduct an AI safety assessment using EU AI Act classification, NIST AI RMF, and bias/fairness evaluation.

Design an agentic AI workflow including orchestration patterns, tool design, memory management, and error handling.

Design an end-to-end ML system from requirements using CRISP-DM methodology and MLOps best practices.

Design a Retrieval-Augmented Generation (RAG) pipeline including chunking, embedding, retrieval, and context assembly strategies.

Estimate AI/ML costs including token usage, model pricing, infrastructure, and optimization opportunities.