Competency Taxonomy

AI Pattern Recognition Fundamentals

Identifying recurring patterns in AI system behavior — training curves, inference patterns, data distribution shapes

Feedback Loops in AI Systems

Recognizing reinforcing and balancing feedback loops in ML pipelines — data flywheel, model drift, performance decay

Data Quality Patterns

Common data quality patterns and anti-patterns — bias amplification, distribution shift, labeling inconsistency

System Boundary Identification

Drawing boundaries around AI systems — what is inside vs. outside the system, interface contracts, dependency mapping

Mental Models for AI

Common mental models practitioners use for AI — black box vs. glass box, statistical vs. symbolic, narrow vs. general

Causal Loop Diagramming for AI

Building causal loop diagrams to map AI system dynamics — identifying leverage points, delays, and unintended consequences

MLOps as a System

Understanding MLOps through systems thinking — CI/CD for ML, monitoring feedback, retraining triggers, model versioning

Responsible AI Framework

Frameworks for responsible AI development — fairness metrics, transparency requirements, accountability structures

AI Evaluation Frameworks

Systematic approaches to evaluating AI systems — benchmarks, A/B testing, human evaluation, safety testing

AI Architecture Patterns

Common architectural patterns for AI systems — RAG, agent loops, ensemble methods, multi-model orchestration

Cloud Cost Patterns

Recognizing common cloud cost patterns — spiky workloads, steady-state costs, growth curves, seasonal variations

Cloud Billing Structures

Understanding how cloud providers bill — on-demand, reserved, spot, savings plans, commitment discounts

Cloud Waste Signals

Identifying signals of cloud waste — idle resources, over-provisioning, unused commitments, orphaned storage

FinOps Stakeholder Patterns

Recognizing how different stakeholders relate to cloud costs — engineering, finance, executive perspectives

Cost Data Patterns

Patterns in cloud cost data — allocation, tagging, showback/chargeback, cost anomaly detection

The FinOps Framework

The FinOps Foundation's framework — Inform, Optimize, Operate phases; maturity model; personas; capabilities

Unit Economics for Cloud

Building unit economics models — cost per transaction, per user, per API call; mapping costs to business value

Cloud Optimization Framework

Systematic approach to optimization — rightsizing, scheduling, commitment strategy, architectural optimization

Cost Allocation Models

Frameworks for allocating cloud costs — direct allocation, proportional sharing, fixed vs variable, shared services

AI-Powered Cost Anomaly Detection

Using AI for detecting cost anomalies — baseline modeling, variance detection, alert tuning, false positive management

AI Capability Recognition

Recognizing what AI can and cannot do today — language, vision, reasoning, generation, classification capabilities

AI Market Signal Detection

Detecting market signals related to AI — adoption curves, vendor announcements, regulatory changes, talent shifts

AI-Era User Needs

Understanding how user needs evolve with AI — automation expectations, conversational interfaces, personalization demands

AI Competitive Patterns

Recognizing competitive patterns in AI — feature parity races, data moats, network effects, platform dynamics

AI Value Creation Patterns

Common patterns of value creation through AI — automation, augmentation, analysis, generation, prediction

AI Risk Patterns

Recurring risk patterns in AI products — hallucination, bias, privacy, security, dependency on third-party models

AI Opportunity Assessment

Frameworks for assessing AI opportunities — feasibility, desirability, viability analysis for AI features

AI Build vs Buy Framework

Decision framework for building vs buying AI — model selection, API vs fine-tuning, open source vs proprietary

Product-AI Fit

Evaluating product-AI fit — when AI adds genuine value vs when it is a gimmick, matching AI capabilities to user problems

AI Product Pricing Models

Pricing strategies for AI products — usage-based, outcome-based, freemium, credit systems, cost pass-through

AI Risk Management Framework

Systematic approach to managing AI product risks — risk registers, mitigation strategies, monitoring frameworks

AI Product Measurement

Measuring AI product success — metrics frameworks, user value metrics, quality metrics, business impact measurement