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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). ...

Post Objective Data cleaning and preliminary analysis process Understanding the emotional charge or plot development of texts through semantic archaeology based on PCAs Understanding the connections and most representative ideas within the document set Intention Understanding a story’s behavior at the level of its variance is a challenge addressed by attentional engineering. Therefore, using lesser-known methods such as the vector narrative arc combined with a literary map constitutes an interesting route to address increasingly common problems. ...

MLflow for Generative AI Systems I’ll start this post by recalling what Hayen said in her book Designing Machine Learning Systems (2022): ‘Systems are meant to learn’. This statement reflects a simple fact: today, LLMs and to a lesser extent vision language models are winning in the Data Science world. But how do we measure this learning? RLHF work is always a good indicator that perplexity will improve, but let’s return to a key point: LLMs must work as a system, therefore debugging is important, and that’s where the necessary tool for every Data Scientist, AI Engineer, ML Engineer, and MLOps Engineer comes in: MLflow. ...