Categories › MLOps
MLOps
Deployment, monitoring, CI/CD, and the day-to-day work of keeping ML systems healthy.
Serving and inference, model versioning and experiment tracking, feature stores, data and concept drift, shadow and canary rollouts, A/B testing, and observability for both classical models and LLM systems.
37 posts, from beginner to advanced. Each ends with what to read next.
Open-Weight and Small Models in 2026: When to Self-Host
Open-weight models are competitive, small models run on a phone, and the API-for-everything default is no longer obviously right. Here is a decision framework for self-hosting versus API, where small models win, what mixture-of-experts changes about the parameter count, and the hybrid most teams end up with.
ML Model to Production: A Complete Walkthrough
Most ML models die in notebooks. Walk through the full path from trained model to live API endpoint serving real traffic - packaging, containerizing, deploying, and monitoring.
Model Versioning with MLflow: Practical Guide
Without model versioning, you cannot reproduce results, roll back broken deployments, or compare experiments. MLflow gives you a practical registry - here is how to use it well.
Feature Stores Explained: Do You Actually Need One?
Feature stores promise to solve training-serving skew and enable feature reuse. But they add real complexity. Understand what they actually do, when they pay off, and when they do not.
Data Drift Detection: Keeping ML Models Honest in Production
Your model was accurate when you deployed it. Six months later, it is quietly making bad predictions. Data drift is usually why. Here is how to detect it before users notice.
CI/CD for ML Pipelines: Automating the Path to Production
Software CI/CD is well understood. ML CI/CD is trickier - you are testing models, not just code. Here is how to build a pipeline that catches problems before they reach production.
Shadow Mode ML Deployment: Testing Without Risk
Before replacing your production model, run the challenger in shadow mode - it receives real traffic but its predictions do not affect users. The safest way to validate a new model.
A/B Testing for ML Features: Measuring What Actually Matters
Offline AUC does not prove business value. A/B testing does. Here is how to design and analyze experiments that measure whether your ML improvement actually moves the metrics you care about.
Canary Releases for ML: Gradual Rollout Patterns
Canary releases let you expose a small fraction of traffic to a new model, watch for problems, and roll back instantly if something goes wrong. Here is how to implement the full pattern.
Interview Readiness and Capstone Launch
Consolidate everything into one flagship capstone and a hiring-ready interview packet.
Portfolio and ML/AI Interview Readiness
Your ML/AI portfolio is the translation layer between the work you did and the judgment you are selling. This guide covers what to build, how to document it, and how to talk about it in interviews at top product companies.
Portfolio Architecture, Project Storytelling, and Resume Evidence
Teach learners to convert projects into hiring evidence instead of random GitHub links.
MLOps Fundamentals for Production
A model that cannot be deployed, monitored, and updated safely is not a production ML system - it is a prototype. This guide covers the CI/CD, serving, versioning, monitoring, and retraining patterns that make ML systems trustworthy.
AI System Design and Product Tradeoffs
Train the learner to reason like an ML/AI engineer under realistic product constraints.
Capstone: Build and Operate a Production-Style AI System
Provide final proof of integrated ML/AI engineering maturity through a production-style system build.
Observability, Monitoring, Drift, and LLM Evals
Teach how shipped systems fail and how to monitor them honestly.
Interview Readiness for ML/AI Engineering Roles
Turn skill into actual interview performance across theory, practical coding, and architecture discussions.
MLOps, CI/CD, Testing, and Safe Releases for ML Systems
Bridge software engineering discipline and ML lifecycle rigor.
Portfolio Conversion: Turning Engineering Work Into ML Evidence
Help transitioners reframe prior engineering work into a credible ML/AI narrative.
Model Serving, APIs, Inference, and Performance Tradeoffs
Connect trained models to real product interfaces with latency and throughput awareness.
Search, Ranking, Recommendation, and Personalization Systems
Prevent over-specialization into LLM-only work by teaching high-value ML product systems.
Data Pipelines, Feature Pipelines, and Training Workflows
Explain how datasets, labels, features, and training jobs become repeatable team workflows.
AI System Design: Quality, Cost, Latency, and Safety Tradeoffs
Prepare the learner for architecture work and system design interviews with realistic AI tradeoffs.
Observability, Drift, Feedback Loops, and LLM Evals
Teach how to see failures clearly once AI systems are live.
MLOps and CI/CD for ML Teams
Connect strong SWE habits to ML lifecycle discipline through automated validation and release workflows.
Serving Models and LLM Systems in Production
Focus on inference systems and product integration, where many applied AI roles spend most of their time.
Data Pipelines, Labeling, and Training Workflows
Teach the lifecycle rigor around dataset construction, labeling, retraining, and artifact management.
Product-Grade End-to-End AI Platform (Capstone)
Build and defend a production-grade AI platform from data to operations.
Top Product-Company Interview Readiness
Prepare for top-company ML/AI loops with system design, modeling depth, and communication.
Product Impact and Stakeholder Communication
Translate model metrics into business impact and stakeholder decisions.
Observability, EvalOps, and Governance
Operationalize quality through observability, eval pipelines, and governance controls.
Milestone Gate 2: Production Readiness
Validate that your systems engineering and ML operations are production-ready.
MLOps and CI/CD for ML Systems
Implement production CI/CD for ML with testing, lineage, and rollback guarantees.
Product-Grade End-to-End AI Product (Capstone)
Deliver a full AI product with architecture, evaluation, operations, and hiring narrative.
Portfolio + Interview Narrative
Convert technical projects into compelling portfolio evidence and top-tier interview stories.
Monitoring, Drift, and LLM Evaluation
Implement monitoring and evaluation loops that detect drift and quality regression early.
MLOps Deployment and Serving
Ship ML models with CI/CD, versioning, rollback, and reliable serving infrastructure.