AI Systems Are Software Systems
Why production AI requires the same engineering discipline as any distributed system, and how this wiki covers the full stack of AI …
Guides
Step-by-step guides for implementing AI systems, from prototyping to production.
In-depth implementation guides covering every stage of the AI development lifecycle.
GitHub Actions Security: Risks, Exploits, and Hardening
A comprehensive guide to GitHub Actions security vulnerabilities, common exploit patterns, and how to audit and harden your CI/CD pipelines …
Everything as Code: Treating All Artifacts as Software
The principle of defining infrastructure, configuration, documentation, policy, video, and design as version-controlled code artifacts - and …
A/B Testing for AI Systems
How to design and run A/B tests for AI models and features, covering experiment design, traffic splitting, metrics selection, and …
Agile for AI Projects - Adapting Agile to Machine Learning
How to apply Agile principles to AI and ML projects, addressing the unique challenges of experimentation, data dependencies, and uncertain …
AI Audit Readiness
A practical guide to preparing your organization and AI systems for internal and external audits, covering documentation, evidence …
AI Cost Accounting and Chargeback Models
How to implement cost tracking, allocation, and chargeback models for AI workloads including token-based billing, GPU hour accounting, and …
AI for Legacy System Modernization
How to use AI to accelerate legacy system modernization, covering code analysis, documentation generation, migration assistance, and …
AI for Software Engineering
Using AI for code generation, bug detection, test generation, code review, and other software engineering tasks.
AI Go-to-Market Strategy
Launch playbooks for AI products covering positioning, early adopter programs, managing expectations, and scaling from beta to general …
AI Model Governance - Managing Models in Production
How to implement model governance for production AI systems, covering model registries, approval workflows, audit trails, and lifecycle …
AI Monetization Strategies
Usage-based pricing, credit systems, freemium models, and other monetization approaches for AI-powered products.
AI Product Management - Managing Products with Machine Learning
How product management changes for AI-powered products, covering requirements definition, success metrics, user experience design, and …
AI Product Metrics - Dual Tracking Product and Model Performance
How to track both product metrics and model metrics for AI products, bridging the gap between business outcomes and technical performance.
AI Regulatory Compliance Checklist
A cross-regulation compliance checklist covering GDPR, EU AI Act, NIS2, DORA, and key standards for organizations deploying AI systems in …
AI Security Best Practices
Security considerations for AI systems, covering prompt injection, data poisoning, model theft, access control, and building …
AI Team Structure - Building Effective AI Organizations
How to structure AI teams within an organization, covering centralized vs embedded models, role definitions, reporting structures, and …
AI Total Cost of Ownership
Full lifecycle cost modeling for AI platforms covering compute, data, personnel, and hidden costs that affect AI project budgets.
AI Transparency Obligations Across EU Regulations
Guide to transparency requirements for AI systems under the EU AI Act, GDPR, and related regulations, covering disclosure, explainability, …
AI User Research - Testing and Measuring Trust
User research methods for AI products including Wizard-of-Oz testing, measuring user trust in AI, and designing studies for probabilistic …
API Design for AI Services
Best practices for designing APIs that serve AI workloads, covering streaming responses, versioning, error handling for probabilistic …
API Versioning Strategies for AI Services
How to version AI APIs as models evolve: URL path versioning, header versioning, model version pinning, backward compatibility, and …
AWS Cloud Governance for AI Workloads
Practical guide for implementing cloud governance on AWS for AI and ML workloads, covering Organizations, SCPs, tagging, cost management, …
Backlog Prioritization for AI Projects
Frameworks and techniques for prioritizing AI project backlogs, balancing business value, technical risk, data readiness, and research …
Building AI Chatbots - From Prototype to Production
A practical guide to building production AI chatbots, covering architecture, conversation design, context management, guardrails, and …
Building an AI Ethics Board
A practical guide to establishing an AI ethics review board, from composition and charter to review processes and decision-making …
Building an Internal AI/ML Platform for Your Organization
How to design and build a shared platform that enables ML teams to develop, deploy, and operate models without reinventing infrastructure …
Building an ML/AI Internal Developer Platform
How to build an internal developer platform for AI/ML teams: service catalogs, golden paths for model deployment, self-service GPU …
Building and Operating a Feature Store
How to implement a feature store that serves consistent features for both training and inference, reducing duplication and preventing …
Building gRPC Microservices for ML Inference
How to build gRPC-based microservices for ML inference: proto definitions, streaming token delivery, load balancing, health checks, and …
Building Knowledge Graphs for AI Applications
How to design, populate, and query knowledge graphs that enhance AI systems with structured relational knowledge.
Capacity Planning for AI Inference
How to right-size GPU and TPU clusters, configure autoscaling for inference workloads, manage GPU memory, and plan capacity for variable AI …
Change Management for AI Adoption
How to manage organizational change when introducing AI systems, addressing resistance, training needs, process redesign, and cultural …
Chaos Testing for AI Systems
Chaos engineering for AI: injecting model API latency, simulating provider outages, degraded embeddings, corrupted indexes, and verifying …
CI/CD Testing Strategy for AI Systems
Which tests to run at each CI/CD stage: PR-level unit tests, merge-level eval suites, scheduled regression and drift detection, cost …
Cloud Security Posture Management for AI Workloads
Guide to implementing CSPM for AI and ML workloads, covering misconfigurations, compliance monitoring, and security automation in cloud AI …
Code Review Practices for ML Codebases
Practical guide to code review for ML projects, covering what to look for in training code, data pipelines, serving code, and experiment …
Comprehensive Model Evaluation Beyond Accuracy
How to evaluate ML models holistically, covering performance metrics, fairness analysis, robustness testing, and business impact assessment.
Computer Vision for Enterprise Applications
A practical guide to implementing computer vision in enterprise settings, covering use cases, model selection, data requirements, and …
Conducting AI Risk Assessments for Enterprise Deployments
A structured methodology for identifying, evaluating, and mitigating risks in AI systems before and after deployment.
Conducting DPIAs for AI Systems
Step-by-step guide for conducting Data Protection Impact Assessments for AI and machine learning systems, with templates and practical …
Contract Testing for AI Microservices
Contract testing between AI services: defining input/output contracts, latency SLAs, Pact for AI services, provider vs consumer-driven …
Cost Estimation for AWS AI Services
How to estimate and manage costs for AI workloads on AWS, covering Bedrock, SageMaker, compute, storage, and strategies for cost …
Cross-Border Data Transfers for AI
Guide to managing international data transfers for AI systems under GDPR, covering transfer mechanisms, cloud considerations, and practical …
Data Anonymization Techniques for AI
A guide to data anonymization techniques for AI including k-anonymity, l-diversity, t-closeness, differential privacy, and practical methods …
Data Labeling Strategies, Tools, and Quality Assurance
How to design labeling workflows, choose tools, manage annotators, and ensure label quality for ML training data.
Data Quality Validation for AI Systems
How to implement data quality validation for AI workloads using Great Expectations and Deequ: profiling, expectation suites, pipeline …
Designing a Data Lakehouse for AI/ML Workloads
A practical guide to designing and implementing a data lakehouse architecture optimized for AI and machine learning workloads.
Detecting and Handling Model Drift and Data Drift in Production
Practical approaches to monitoring for data drift, concept drift, and model performance degradation, with strategies for automated response.
Developing a Data Strategy for AI Initiatives
How to assess, prepare, and govern your organization's data assets to support AI projects effectively.
Disaster Recovery for AI Systems
How to plan disaster recovery for AI systems: RTO/RPO targets, multi-region model serving, model artifact backup, and failover strategies …
Documenting AI Systems for Compliance and Maintainability
What to document for AI systems, how to structure it, and how to keep documentation current as models and data evolve.
DORA Compliance Guide for Financial AI
Practical guide for implementing DORA requirements in financial services organizations that deploy AI systems for trading, risk management, …
Edge AI Deployment Guide
How to deploy AI models on edge devices, covering hardware selection, model optimization, deployment strategies, and managing edge AI at …
Embedding Model Comparison and Selection Guide
How to choose embedding models for semantic search, RAG, and similarity tasks, comparing popular models across quality, speed, cost, and …
End-to-End Testing AI-Powered Products
How to E2E test AI applications: browser automation for chatbot UIs, testing streaming responses, handling non-deterministic outputs, visual …
EU AI Act Compliance Guide
Practical steps for achieving compliance with the EU AI Act, covering risk classification, conformity assessment, documentation, and …
Evaluating RAG System Quality
How to measure and improve both retrieval quality and generation quality in RAG systems, with practical metrics and evaluation frameworks.
Feature Engineering Guide
Systematic approaches to feature creation, selection, and transformation for building effective machine learning models.
Feature Stores for Machine Learning - A Practical Guide
What feature stores are, why they matter, how to choose one, and practical implementation guidance for ML feature management.
Federated Learning - Training Without Centralizing Data
A practical guide to federated learning, covering how it works, when to use it, implementation approaches, and challenges for enterprise …
Fine-Tuning LLMs - A Practical Guide
When and how to fine-tune large language models, covering data preparation, training approaches (full fine-tuning, LoRA, QLoRA), evaluation, …
Framework for Evaluating and Selecting AI Vendors
A structured approach to evaluating AI vendors covering technical capabilities, data handling, compliance, pricing, and long-term viability.
From AI Proof of Concept to Production
How to navigate the journey from AI proof of concept to production deployment, covering the common pitfalls, decision gates, and engineering …
Full-Stack Observability for AI Systems
How to implement comprehensive observability for AI applications covering traces, evaluations, metrics, and alerting across the entire …
GDPR Compliance for AI/ML Teams
A practical guide for AI and machine learning teams on meeting GDPR requirements across the ML lifecycle, from data collection through model …
Getting Started with MLOps - From Notebooks to Production
A practical guide to adopting MLOps practices, moving ML models from experimental notebooks to reliable, automated production systems.
Handling Imbalanced Data - A Practical Guide
Strategies for building effective classifiers on skewed datasets, from sampling techniques to algorithm-level adjustments and evaluation …
Hiring AI Engineers - A Practical Guide
How to hire AI and ML engineers effectively, covering role definition, sourcing, technical evaluation, and common hiring mistakes in the AI …
Hyperparameter Tuning Guide
Practical guide to grid search, random search, and Bayesian optimization for finding optimal model configurations.
Implementing a Data Catalog for AI Teams
How to implement metadata management with DataHub or OpenMetadata: automated ingestion, data lineage, ownership, classification, and …
Implementing AI Governance in Your Organization
A framework for establishing AI governance structures, policies, and processes that balance innovation velocity with risk management.
Implementing Continuous Training for ML Models
How to set up automated retraining pipelines that keep ML models current as data distributions and business conditions change.
Implementing Data Mesh for AI at Scale
A practical guide to applying data mesh principles for decentralized data ownership and governance in organizations scaling AI across …
Implementing the NIST AI Risk Management Framework
A practical guide to implementing the four core functions of the NIST AI RMF: Govern, Map, Measure, and Manage across your AI portfolio.
Incident Management for AI Systems
How to handle incidents in AI systems: on-call rotations, escalation policies, AI-specific runbooks, and post-incident reviews for model and …
Incident Response Playbook for AI System Failures
A structured approach to detecting, triaging, mitigating, and learning from AI system failures in production.
Integration Testing AI Pipelines
How to integration test AI systems: testing RAG retrieval pipelines, model inference chains, tool-call sequences, and contract testing …
ISO/IEC 42001 Implementation Guide
A practical guide to implementing an AI management system and achieving ISO/IEC 42001 certification for responsible AI governance.
Kanban for AI Operations - Flow-Based Management
Implementing Kanban for AI operations teams managing model deployments, monitoring, retraining, and incident response in production ML …
LLM Evaluation Methods - Measuring Language Model Quality
A comprehensive guide to evaluating large language models, covering automated metrics (BLEU, ROUGE, BERTScore), LLM-as-judge, human …
LLM Gateway Architecture
How to design a centralized LLM access layer that handles routing, rate limiting, cost tracking, caching, and logging across multiple model …
Managing Organizational Change During AI Adoption
How to lead AI adoption efforts that succeed by addressing the human side: stakeholder alignment, workforce readiness, and cultural change.
Managing Prompts at Scale: Versioning, Testing, Deployment
How to treat prompts as first-class software artifacts with version control, testing, review processes, and safe deployment practices.
Managing Technical Debt in ML Systems
How to identify and manage technical debt specific to machine learning systems, covering data debt, pipeline debt, configuration debt, …
Managing Test Environments for AI Systems
Test environment strategies for AI: local dev with mocked models, staging with real models, Docker Compose for local AI stacks, cost …
Migrating AI Workloads to the Cloud
A practical guide for migrating on-premise AI and ML workloads to cloud platforms, covering assessment, planning, execution, and …
ML Engineer vs Data Scientist - Roles, Skills, and When You Need Each
A clear comparison of ML Engineer and Data Scientist roles, covering responsibilities, skills, career paths, and guidance on which to hire …
ML Pipeline Automation - From Manual to Continuous
How to automate machine learning pipelines for training, evaluation, and deployment, moving from manual notebook workflows to production …
Mocking AI Services for Testing
Strategies for mocking LLM APIs, embedding services, and vector databases in tests: fixture responses, VCR pattern, deterministic stubs, and …
Model Interpretability Guide
Practical guide to SHAP, LIME, feature importance, partial dependence plots, and other techniques for understanding ML model behavior.
Monitoring AI Systems in Production
A comprehensive guide to monitoring production AI systems, covering model quality, data drift, infrastructure health, and alerting …
Multi-Cloud AI Strategy
How to design and implement a multi-cloud AI strategy covering portable ML pipelines, abstraction layers, vendor lock-in avoidance, and data …
Multi-Modal AI - Working with Text, Images, and Beyond
A practical guide to building multi-modal AI applications that process text, images, audio, and video, covering architectures, use cases, …
NIS2 Implementation Guide
Step-by-step guide for implementing NIS2 Directive compliance, covering risk assessment, security measures, incident reporting, and supply …
NLP Pipeline Design - From Raw Text to Actionable Insights
How to design and build NLP pipelines for enterprise applications, covering text processing, entity extraction, classification, and …
OWASP Top 10 for LLM Applications (2025)
Practical guide to the OWASP Top 10 vulnerabilities for LLM applications, covering prompt injection, data leakage, supply chain risks, and …
Performance Engineering for AI Systems
A comprehensive guide to latency optimization, GPU memory management, throughput engineering, and model acceleration techniques for …
Playwright Testing Guide for AI Applications
Comprehensive Playwright guide: setup, page objects, selectors, assertions, network interception for mocking AI APIs, visual comparison, …
Practical Steps for EU AI Act Compliance
What the EU AI Act requires, which of your AI systems are affected, and concrete steps to achieve and maintain compliance.
Production Readiness Checklist for AI Systems
A concrete checklist covering model quality, infrastructure, security, monitoring, documentation, compliance, and rollback planning for …
Project Estimation for AI Initiatives
Techniques for estimating AI project timelines, budgets, and resource requirements, accounting for the inherent uncertainty of machine …
Prompt Chaining - Breaking Complex Tasks into Steps
How to design and implement prompt chains for complex AI tasks, covering chain architecture, error handling, optimization, and practical …
RAG with Images, Tables, and Mixed Document Types
How to build RAG systems that handle documents containing images, tables, charts, and mixed content alongside text.
Rate Limiting for LLM and AI Endpoints
How to implement rate limiting for AI API endpoints: token bucket and sliding window algorithms, per-user and per-model limits, token-based …
Real-Time Data Pipelines for AI Workloads
Implementation guide for real-time streaming data pipelines: four-layer architecture, Flink feature computation, late-arriving data handling …
Red Teaming and Adversarial Testing for AI Systems
How to plan and execute red team exercises that systematically probe AI systems for vulnerabilities, biases, and failure modes before …
Reducing LLM Inference Costs in Production
Practical strategies for reducing LLM API and hosting costs without sacrificing quality, from caching and routing to model selection and …
Release Management for AI Model Deployments
Release strategies for AI model deployments including canary releases, shadow mode, A/B testing, and rollback procedures for ML systems.
Requirements Engineering for AI Projects
Practical guide to gathering, documenting, and managing requirements for AI projects where outputs are probabilistic and data availability …
Responsible AI - A Practical Implementation Guide
How to implement responsible AI practices including fairness, transparency, accountability, and privacy in enterprise AI systems.
Risk Management for AI Projects
Identifying, assessing, and mitigating risks specific to AI and ML projects, from data quality to model failure to organizational …
Scaling AI Infrastructure
How to scale AI infrastructure for growing workloads, covering compute scaling, model serving at scale, data infrastructure, and cost …
Scrum for Machine Learning Teams - A Practical Guide
How to implement Scrum in ML teams, covering sprint cadence, role adaptations, backlog structure, and ceremony modifications for data …
Secrets Management for AI Pipelines
How to manage API keys, credentials, and sensitive configuration in AI pipelines using vault integration, rotation policies, and secure …
Security Scanning in AI/ML CI/CD Pipelines
How to integrate security scanning into AI/ML CI/CD pipelines: dependency scanning, container image analysis, model file validation, secrets …
Setting Up an AI Ethics Board
A practical guide to establishing an AI ethics board including composition, charter development, review processes, and escalation procedures …
Setting Up Model Versioning and Registry
How to implement a model registry that tracks model versions, metadata, lineage, and approval status across the ML lifecycle.
Snapshot Testing for AI Systems
Snapshot and golden file testing for AI: capturing expected outputs, managing updates, structural snapshots, semantic similarity assertions, …
Software Architecture for AI Systems
Architecture decisions, ADRs, and trade-offs for AI systems covering serving patterns, training infrastructure, and system decomposition.
Software Quality Practices for ML Projects
How to apply software quality practices to ML projects: code coverage for non-model code, quality gates in CI/CD, static analysis, testing …
Sprint Planning for AI Projects - Getting It Right
How to run effective sprint planning sessions for AI and ML teams, covering estimation techniques, capacity planning, and handling research …
Stakeholder Management for AI Projects
How to manage stakeholder expectations, communicate uncertainty, and build trust throughout AI project delivery from proof of concept to …
Synthetic Data Generation for AI
How to generate and use synthetic data for AI training, covering techniques, quality validation, privacy considerations, and practical use …
Systematic Experiment Tracking with MLflow and W&B
How to set up experiment tracking that makes ML research reproducible, comparable, and auditable across your team.
Technical Debt in AI Systems
Understanding and managing technical debt specific to AI and ML systems, covering data debt, model debt, pipeline debt, and strategies for …
Technical Writing for AI Systems
Writing API documentation, model cards, design documents, and runbooks for AI/ML systems.
Test Data Management for AI Systems
Managing test data for AI: synthetic data generation, fixture design, golden datasets for regression, data versioning, anonymization, and …
Testing AI Agent Tool Calls
How to test AI agents that use tools: mocking tool responses, testing tool selection logic, error handling, multi-step workflows, sandboxed …
Testing and Evaluating AI Agent Performance
Frameworks for evaluating AI agents that plan, use tools, and take actions, covering correctness, reliability, safety, and cost efficiency.
Testing LLM Applications
LLM-specific testing strategies: prompt template testing, structured output validation, guardrail verification, token limit testing, model …
Testing Non-Deterministic Systems
Strategies for testing AI systems where the same input produces different outputs: statistical assertions, distribution testing, confidence …
Testing RAG Systems
How to test Retrieval-Augmented Generation systems: unit testing chunking, integration testing retrieval quality, testing citation accuracy, …
Time Series Analysis Foundations
Comprehensive guide to time series forecasting methods including ARIMA, SARIMA, Prophet, seasonal decomposition, and practical …
Time Series Forecasting with AI
A practical guide to time series forecasting for business applications, covering classical methods, machine learning approaches, deep …
Unit Testing AI Applications
How to unit test AI codebases effectively: testing prompt templates, output parsers, data validation, chunking functions, and embedding …
User Acceptance Testing for AI Systems
How to conduct UAT for probabilistic AI outputs, including test design, success criteria, and managing stakeholder expectations around error …
User Training and AI Adoption
Strategies for driving AI adoption through structured change management, effective training programs, trust-building, and measurable …
Vector Database Selection Guide
How to choose the right vector database for your AI application, covering performance requirements, managed vs self-hosted options, and …
Voice AI Implementation Guide
How to build voice-enabled AI applications, covering speech-to-text, text-to-speech, voice assistants, and real-time voice processing …
Waterfall vs Agile for AI Projects - When Each Approach Works
A practical comparison of waterfall and agile methodologies for AI and ML projects, including hybrid approaches and decision criteria for …
Programming Languages for AI - Python, TypeScript, HCL
A practical guide to the three languages used across a modern AI stack: Python for agents and models, TypeScript for frontends and video …
Prompt Engineering for Enterprise AI Applications
Practical prompt engineering patterns for production AI systems: system prompts, few-shot examples, chain-of-thought, structured output, …
Sorting and Search Algorithms for AI Pipelines
How sorting and search algorithms underpin AI pipeline design: complexity trade-offs, partial sorting for top-k selection, tiered analysis …
AI Architecture Patterns - From Monolith to Multi-Agent
How AI system architecture evolves from monolithic single-model deployments through microservices to collaborative multi-agent systems, with …
AI Deployment Models - SaaS, PaaS, IaaS, and Serverless
How the four cloud deployment models apply to AI workloads: when to use managed models, platform endpoints, GPU instances, or serverless …
CI/CD for AI Projects - A Complete Pipeline Guide
A detailed walkthrough of a CI/CD pipeline for AI: source control, Docker builds, model evaluation, staged deployment, and drift monitoring …
CI/CD Pipelines for AI Projects
Building reliable CI/CD pipelines for AI projects: model artifact management, automated evaluation gates, GitHub Actions workflows, and …
Infrastructure as Code for AI Projects
Why IaC matters for AI reproducibility, multi-environment consistency, and cost tracking. Terraform and CDK patterns for Bedrock agents, …
Open Practice Library for AI Projects - Discovery to Delivery
Applying Open Practice Library practices to AI: Event Storming for AI use case discovery, Impact Mapping for AI value, User Story Mapping …
Testing AI Systems - Unit Tests to Production Monitoring
A practical testing strategy for AI systems: property-based testing, integration testing with mocked models, evaluation frameworks, and …
The Shared Responsibility Model for AI on AWS
How AWS shared responsibility applies to AI and ML workloads: data, model, and infrastructure responsibilities across Bedrock and SageMaker.
Twelve-Factor AI - Applying 12-Factor App Principles to AI Systems
How each of the 12 original 12-factor app principles applies to AI and LLM-based systems: model configuration, artifact management, vector …
AI for Document Workflows - From Intake to Archive
End-to-end document automation covering intake, classification, extraction, validation, routing, and archive. AWS services at each stage.
AI for Small Businesses - Where to Start
Low-cost AI tools, quick wins in email automation and document processing, and guidance on when to invest in custom solutions.
AI Fraud Detection Patterns for Insurance and Finance
Common fraud signals, anomaly detection approaches, rule-based versus ML-based detection, and human review workflow design for insurance and …
Budgeting an AI Project - What It Really Costs
A practical cost breakdown for enterprise AI projects - from prototype to production - covering model inference, infrastructure, data, …
Building AI Assistants That Actually Help - A Practical Guide
How to design AI assistants that are genuinely useful rather than technically impressive but frustrating to use. Intake design, context …
Building RAG Systems - A Step-by-Step Guide
Document ingestion, chunking strategies, embedding models, vector stores, retrieval tuning, and generation with context for production RAG …
Conference-Driven Development: Building and Presenting AI Systems in Public
How the discipline of preparing conference talks produces better AI prototypes, clarifies system design, and accelerates learning. Covers …
Data Preparation for AI Projects - A Practical Guide
How to prepare data for AI projects: assessing what you have, cleaning and normalizing it, building evaluation datasets, and setting up …
Getting Started with Amazon Bedrock for Enterprise AI
A practical introduction to Amazon Bedrock: what it is, which models are available, how pricing works, and how to get your first use case …
How to Choose Your First AI Use Case
A practical framework for selecting the right first AI use case - prioritizing for quick wins, avoiding common traps, and setting up for a …
How to Facilitate an AI Workshop - A Practitioner's Guide
Preparation, agenda design, stakeholder management, use case brainstorming techniques, prioritization exercises, and gap management between …
How to Get AWS Funding for Your AI Project
A practical guide to AWS PoC funding (up to 10,000 EUR) and migration funding (up to 400,000 EUR) - eligibility, application process, and …
Multi-Agent AI Systems - When One Model Is Not Enough
A practical introduction to multi-agent AI architectures: when to use them, how they work, and which frameworks are production-ready.
Why Your AI Output Sounds Generic - And How to Fix It With Your Own Data
The difference between prompting and grounding. Five stages from zero context to production-ready assets. The Personal Inference Pack …