Category: MLOps & Deployment
-
Kubernetes Autoscaler for ML: Build HPA from Scratch
Build Kubernetes HPA for ML workloads from scratch. Learn custom metrics, scaling strategies, and real-world GPU optimization patterns.
-
ONNX Export Pitfalls: 7 PyTorch โ Production Gotchas
Fix ONNX export failures fast. Learn the 7 critical PyTorch-to-production gotchas that silently break modelsโand how to catch them before deployment.
-
GitHub Actions ML Pipeline: First CI/CD Portfolio Project
Build an automated ML training pipeline with GitHub Actions, MLflow tracking, and live metrics dashboard โ proof you ship production-ready code.
-
Feature Stores Are Overengineered: When SQL Is Enough
Most teams waste months on Feast or Tecton when Postgres + cron does the job. Here's when SQL beats feature stores โ with real latency numbers.
-
FastAPI Model Serving: 5 Steps to 50ms Inference
Cut FastAPI model inference from 800ms to 47ms with 5 changes: lifespan handlers, warmup, ThreadPoolExecutor, Pydantic validation, and response models.
-
MLflow Experiment Tracking: Portfolio Project in 30 Min
Build a real MLflow experiment tracking portfolio in 30 min: 40+ logged runs, CV metrics, model registry, and a live UI โ not a toy notebook screenshot.
-
Docker vs Kubernetes for First ML Model: When to Use Each
Deploy ML models with Docker for simplicity or Kubernetes for scale. Learn when each tool fits your workflow and avoid common pitfalls.
-
DVC vs MLflow vs W&B: Which Saves More Debug Hours?
Tested DVC, MLflow, and W&B on real projects. Here's which breaks first, what actually costs money, and the hybrid setup that works.