Mlops

Complete MLOps Series: Part 1 (current) | Part 2: Deployment → | Part 3: Production → Anatomy of an MLOps Pipeline - Part 1: Pipeline and Orchestration Why This Post Is Not Another Scikit-Learn Tutorial Most MLOps posts teach you how to train a Random Forest in a notebook and tell you “now put it in production.” This post assumes you already know how to train models. What you probably don’t know is how to build a system where: ...

Complete MLOps Series: ← Part 1: Pipeline | Part 2 (current) | Part 3: Production → Anatomy of an MLOps Pipeline - Part 2: Deployment and Infrastructure 8. CI/CD with GitHub Actions: The Philosophy of Automated MLOps The Philosophical Foundation: Why Automation Isn’t Optional Before diving into YAML, let’s address the fundamental question: why do we automate ML pipelines? The naive answer is “to save time.” The real answer is more profound: because human memory is unreliable, manual processes don’t scale, and production systems demand reproducibility. ...

Complete MLOps Series: ← Part 1: Pipeline | ← Part 2: Deployment | Part 3 (current) Anatomy of an MLOps Pipeline - Part 3: Production and Best Practices 11. Model and Parameter Selection Strategies The Complete Flow: Selection → Sweep → Registration This pipeline implements a three-phase strategy for model optimization, each with a specific purpose: Step 05: Model Selection ├── Compares 5 algorithms with basic GridSearch (5-10 combos/model) ├── Objective: Identify best model family (Random Forest vs Gradient Boosting vs ...) ├── Primary metric: MAPE (Mean Absolute Percentage Error) └── Output: Best algorithm + initial parameters Step 06: Hyperparameter Sweep ├── Optimizes ONLY the best algorithm from Step 05 ├── Bayesian optimization with 50+ runs (exhaustive search space) ├── Objective: Find optimal configuration of best model ├── Primary metric: wMAPE (Weighted MAPE, less biased) └── Output: best_params.yaml with optimal hyperparameters Step 07: Model Registration ├── Trains final model with parameters from Step 06 ├── Registers in MLflow Model Registry with rich metadata ├── Transitions to stage (Staging/Production) └── Output: Versioned model ready for deployment Why three separate steps? You don’t have computational resources to do exhaustive sweep of 5 algorithms × 50 combinations = 250 training runs. First decide strategy (which algorithm), then tactics (which hyperparameters). ...

Exploring the intersection of machine learning and DevOps - from model versioning to automated deployments. Featured Posts 📦 Artifact Design and Pipeline in MLOps Part I Introduction to artifacts, MLproject manifests, and pipeline orchestration for reproducible ML workflows. 🤖 MLflow for Generative AI Systems Learn how to use MLflow for tracing, evaluation, and versioning of LLM applications and Agentic AI systems. 🍓 Raspberry Pi 16GB, Servers, and MLOps Using Raspberry Pi as a development server for MLOps testing and edge deployments. ...