A/B Testing Patterns for Machine Learning Models
Designing and running A/B tests for ML model changes. Traffic splitting, metric selection, statistical rigor, and common pitfalls.
Deployment
Docker
What Docker is, how containers package applications, and best practices for containerizing AI workloads.
Edge AI Deployment Guide
How to deploy AI models on edge devices, covering hardware selection, model optimization, deployment strategies, and managing edge AI at …
Edge MLOps
Device-aware CI/CD for edge ML models: model optimization, over-the-air deployment, device fleet management, and monitoring at the edge.
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 …
GitOps
What GitOps is, how it uses Git as the single source of truth for infrastructure and deployments, and practical implementation.
Helm Chart
What Helm charts are, how they package Kubernetes deployments, and best practices for managing charts in production.
Hugging Face vs Amazon Bedrock - Model Access Comparison
Comparing Hugging Face and Amazon Bedrock for accessing and deploying AI models, covering model selection, deployment options, cost, and …
Immutable Infrastructure
What immutable infrastructure means, how it replaces mutable servers with disposable instances, and why it improves reliability.
MLflow - ML Lifecycle Management
A comprehensive reference for MLflow: experiment tracking, model registry, deployment, and lifecycle management for enterprise ML and AI …
Model Registry
What a model registry is, how it provides versioned storage and lifecycle management for trained ML models, and why it is essential for …
Monolithic Architecture
A software architecture where all components are built and deployed as a single, self-contained unit.
Production Readiness Checklist for AI Systems
A concrete checklist covering model quality, infrastructure, security, monitoring, documentation, compliance, and rollback planning for …
Progressive Delivery
What progressive delivery means, how feature flags, canary releases, and automated rollback combine to reduce deployment risk for AI …
Progressive Delivery for AI Deployments
Combining feature flags, canary releases, and automated rollback for AI model deployments: AI-specific metrics, shadow mode testing, and …
Prompt Template Management Patterns
Version control, testing, and deployment patterns for managing prompt templates at scale. Treating prompts as code.
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.
Shadow Deployment Pattern for AI Models
Running new AI models in parallel with production models to compare outputs without affecting users. Implementation, comparison strategies, …
Blue-Green Deployment
What blue-green deployment is, how it works, why it matters for zero-downtime AI model updates, and how it compares to canary and rolling …
Blue-Green Deployment for AI Services
Zero-downtime model updates using blue-green deployment: how it works, AWS implementation with Lambda aliases and SageMaker variants, and …
Canary Deployment
What canary deployment is, how gradual traffic shifting works, which metrics to watch, and how to configure automatic rollback triggers for …
Canary Deployment for AI Models
Gradual traffic shifting to new model versions: how to implement canary deployments with Lambda weighted aliases and SageMaker production …
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 …
Feature Flags
What feature flags are, how they enable safe AI model rollouts, A/B testing, and instant rollback - and the tools available for implementing …
GitHub Actions - CI/CD for AI Projects
GitHub Actions workflow syntax, Hugo deployment pattern, Python testing pipelines, Docker builds, Terraform plan/apply, and model evaluation …
Model Versioning and Artifact Management
Why model versioning matters and how to implement it: S3 for artifacts, Git for configuration, SageMaker Model Registry, Bedrock model …
AWS Amplify - Full-Stack App Development
Using AWS Amplify to deploy front-end applications, host static sites, and connect to AWS AI backends.
Continuous Integration and Continuous Delivery
The discipline of keeping software in a releasable state at all times through automated build, test, and deployment pipelines. CI/CD is the …