A prompt can be three paragraphs long and still change the behavior of an entire application. It can decide whether an assistant answers, abstains, calls a tool, or invents the argument that the tool receives. Yet it is often reviewed with less discipline than a change to a configuration file. Let’s start there. If editing a sentence can change what your software does, that sentence belongs in your release process. ...
CPU · GPU · TPU · Edge Computing The problem is not that AI is expensive. It’s that for years, people paid to train models as if that were the main cost — when the real cost, the one that never stops, is serving every response. TL;DR Inference costs exceed training by 15x–20x over a model’s operational lifetime. Optimizing for training while ignoring inference is optimizing the wrong problem. CPU (Intel Xeon AMX): the correct choice when the model lives alongside the data. Network latency kills any compute gain from moving to a GPU cluster. NVIDIA GPU (Blackwell/Hopper + TensorRT-LLM): still the default for research and heterogeneous production. CUDA is a 20-year moat. Don’t lock in at peak prices. Google TPU v6/v7: the right answer for high-volume, predictable inference. Midjourney cut monthly costs from $2.1M to $700K. The CUDA migration barrier no longer exists. Edge AI: thermodynamics, not algorithms, sets the limits. Pi 5 + Hailo-10H delivers 320 ms TTFT (6.4× faster than CPU-only) with a PCIe x1 bottleneck you need to design around. The right hardware is not the most powerful. It is the one that matches the problem topology to the silicon architecture without wasting energy or budget. Introduction In 2023, Nvidia published a post titled What Is AI Computing? focused on handling intensive computations — particularly useful for embedding design and optimization processes in Machine Learning — and advancing toward hardware acceleration to find patterns in immense amounts of data, thereby updating the assumptions of ML or AI models. All of this typically runs on GPUs. ...
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). ...