DevOps
The Complete MLOps Platform: 25 Posts, 8 Layers, One Architecture
Series finale. 25 posts of MLOps for DevOps engineers, condensed into one 8-layer architecture. Every tool. Every layer. The full picture in one post.
MLOps Maturity Model: From Notebooks to Platform in 5 Levels
Level 0 is Jupyter in production. Level 4 is a fully automated ML lifecycle. Most teams think they are in the middle. Most teams are wrong. Here is why.
5 Questions to Ask Before Every ML Model Deployment
A data scientist hands you a model.pkl. Before deploying, ask these 5 production-ready questions every DevOps engineer should know.
DevOps Thinking Applied to MLOps: 5 Essential Tools
You already know 80% of MLOps. Here are 5 open-source tools that map directly to your existing DevOps skills.
DVC: Git for Your ML Training Data
You version code with Git. DVC does the same for ML training data. Here is your weekend starter guide to data version control.
Feature Stores: The Package Registry for ML Features
Your training pipeline computes 'average amount' as 30-day mean. Your API computes it as 7-day mean. Same name, different values. Feature stores fix this.
ML Retraining Pipelines: From Drift Alert to Production Model
Your drift detector triggered. Now what? Here is the retraining pipeline every MLOps team needs, with quality gates to prevent deploying garbage.
MLflow in 60 Seconds: The Complete ML Model Lifecycle
From training to production in 5 steps. How MLflow tracks experiments, versions models, and enables instant rollbacks with zero code changes.
MLOps for DevOps Engineers
A 25-part series bridging DevOps skills to MLOps. Same mindset, different artifacts.