Technical breakdowns of the frameworks powering modern AI systems.
AI Frameworks
Deep dives into AI frameworks, libraries, and development platforms.
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Waterfall for AI Projects - When Sequential Planning Works
Understanding when and how waterfall methodology applies to AI projects: regulatory environments, fixed-scope …Wardley Mapping for AI - Strategic Technology Positioning
Using Wardley Maps to visualize the AI value chain, assess component maturity, and make strategic build-vs-buy …Value Stream Mapping for AI - Identifying Waste and Opportunity
Applying value stream mapping to AI project delivery and business processes: visualizing flow, identifying …Team Topologies for AI - Organizing AI Teams
Applying Team Topologies to AI organizations: stream-aligned, platform, enabling, and complicated-subsystem …TDSP: Microsoft's Team Data Science Process
A structured, agile methodology for delivering data science and AI solutions in teams, emphasizing …SWEBOK V4 Knowledge Areas Overview
Overview of the IEEE Software Engineering Body of Knowledge Version 4, covering its knowledge areas and …Stakeholder Mapping for AI - Managing Influence and Alignment
Systematically identifying, analyzing, and managing stakeholders in AI projects: power-interest grids, …Software Requirements Engineering for AI Systems
Elicitation, analysis, and specification techniques adapted for AI and ML projects, where requirements are …Software Quality Assurance for AI/ML Projects
Quality planning, metrics, and gates adapted for AI and ML projects where outputs are probabilistic and data …Shift-Left Testing for ML Systems
Moving testing earlier in the development lifecycle for ML projects: TDD for pipelines, contract-first APIs, …SAFe for AI - Scaling Agile in AI Programs
Applying the Scaled Agile Framework to AI programs: portfolio alignment, PI planning for ML workloads, and …RICE Scoring for AI - Quantitative Use Case Prioritization
Applying the RICE scoring model (Reach, Impact, Confidence, Effort) to prioritize AI use cases with …Responsible AI Framework
A comprehensive framework for implementing responsible AI principles across the organization, from governance …Release Management - Cadences, Trains, and Versioning
Release cadences, release trains, and semantic versioning automation for software and AI/ML systems.Prosci ADKAR for AI Adoption - Change Management for AI Transformation
How the ADKAR change management model applies to AI adoption, addressing the human side of AI transformation …OKR Framework for AI - Objectives and Key Results
Applying OKRs to AI initiatives: setting measurable objectives, defining AI-appropriate key results, and …OECD AI Principles - The International Foundation for Trustworthy AI
How the OECD AI Principles became the most widely adopted international framework for responsible AI, …NIST AI Risk Management Framework - Govern, Map, Measure, Manage
An overview of the NIST AI RMF 1.0 framework, its four core functions, and how organizations use it to …NIS2 Directive Compliance Framework
NIS2 Directive cybersecurity requirements for essential and important entities: risk management, incident …MoSCoW Prioritization for AI - Must, Should, Could, Won't
Applying MoSCoW prioritization to AI project scope: managing stakeholder expectations, defining MVP …Model Risk Management Framework
A comprehensive framework based on SR 11-7 guidance for managing model risk across development, validation, …Mixture of Experts - Routing Queries to Specialist Sub-Networks
How Mixture of Experts architecture enables large-scale AI models by activating only a subset of parameters …Medallion Architecture - Bronze, Silver, Gold Data Quality Layers
How the medallion architecture organizes data lakehouses into progressive quality layers to support analytics …Lean Startup for AI - Validated Learning with AI Products
Applying Lean Startup methodology to AI product development: hypothesis-driven experiments, MVPs with AI, and …
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