How to Route LLM Traffic by Cost and Complexity Using Intelligent Model Routing
Learn to build a multi-model router that sends simple queries to cheap LLMs and complex ones to GPT-4o or Claude, with fallbacks and cost monitoring.
MLOps and Deployment
How to Automate Model Retraining Pipelines with Prefect
Build hands-off retraining pipelines that fetch data, train, evaluate, and promote models only when metrics improve
How to Autoscale LLM Inference on Kubernetes with KEDA
Scale LLM inference pods up and down automatically based on request queue depth and GPU utilization
How to Build a Model A/B Testing Framework with FastAPI
Ship ML models confidently by A/B testing them with a FastAPI traffic-splitting framework
How to Build a Model Batch Inference Pipeline with Ray and Parquet
Run HuggingFace model predictions on large Parquet datasets with Ray Data parallelism and write results back efficiently
How to Build a Model Canary Analysis Pipeline with Statistical Tests
Automatically decide whether to promote or rollback a canary model using Mann-Whitney U, KS tests, and effect sizes
How to Build a Model CI Pipeline with GitHub Actions and DVC
Automate model training and evaluation in CI with GitHub Actions, DVC pipelines, and CML reports
How to Build a Model Compression Pipeline with Pruning and Quantization
Shrink your PyTorch models dramatically by chaining magnitude pruning with quantization in a single pipeline.
How to Build a Model Configuration Management Pipeline with Hydra
Stop hardcoding hyperparameters and use Hydra to manage model configs, run sweeps, and track experiments cleanly
How to Build a Model Dependency Scanner and Vulnerability Checker
Scan Python ML environments for CVEs, pin safe versions, and automate vulnerability checks in CI pipelines
How to Build a Model Deployment Pipeline with Terraform and AWS
Automate ML model deployments to SageMaker with Terraform configs you can version and reproduce
How to Build a Model Drift Alert Pipeline with Evidently and FastAPI
Detect data and prediction drift in production ML models using Evidently reports served through a FastAPI monitoring API
How to Build a Model Endpoint Load Balancer with NGINX and FastAPI
Distribute ML inference traffic across multiple model servers with NGINX, FastAPI, and Docker Compose
How to Build a Model Explainability Dashboard with SHAP and Streamlit
Create a self-service dashboard where stakeholders can explore model predictions and feature importance
How to Build a Model Feature Store Pipeline with Redis and FastAPI
Serve ML features at sub-millisecond latency using Redis as an online feature store with a FastAPI interface
How to Build a Model Gradual Rollout Pipeline with Feature Flags and Metrics
Ship new models safely with percentage-based routing, real-time metrics, and automated promotion or rollback logic.
How to Build a Model Health Dashboard with FastAPI and SQLite
Create a self-hosted model health dashboard with FastAPI, SQLite, and simple HTML charts
How to Build a Model Input Validation Pipeline with Pydantic and FastAPI
Prevent model inference failures by validating request data with Pydantic models and custom validators
How to Build a Model Load Testing Pipeline with Locust and FastAPI
Find your model API’s breaking point with Locust load tests and automated performance reports.
How to Build a Model Metadata Store with SQLite and FastAPI
Create a self-hosted model registry API that tracks metrics, parameters, and deployment status with SQLite
How to Build a Model Monitoring Dashboard with Prometheus and Grafana
Instrument a FastAPI model server with prometheus_client and build Grafana dashboards that catch latency spikes and distribution shifts
How to Build a Model Performance Alerting Pipeline with Webhooks
Set up real-time performance monitoring that sends alerts to Slack when your model metrics drop
How to Build a Model Rollback Pipeline with Health Checks
Automatically detect failing ML models in production and roll back to the last known good version
How to Build a Model Serving Pipeline with LitServe and Lightning
Serve HuggingFace models as fast APIs using LitServe with batching, GPU acceleration, and Docker deployment.
How to Build a Model Serving Pipeline with Ray Serve and FastAPI
Serve ML models in production with Ray Serve’s auto-scaling and FastAPI’s request handling combined
How to Build a Model Versioning Pipeline with DVC and S3
Track, store, and switch between model versions using DVC pipelines backed by S3 remote storage
How to Build a Model Warm Pool with Preloaded Containers on ECS
Cut ML inference cold starts from minutes to milliseconds with preloaded ECS containers that keep models in memory.
How to Build a Model Warm-Up and Health Check Pipeline with FastAPI
Eliminate cold-start latency in ML APIs by warming up models at startup and adding proper health checks with FastAPI.
How to Build a Shadow Deployment Pipeline for ML Models
Route production traffic to both primary and shadow models concurrently, log results, and decide when to promote the new model
How to Build Blue-Green Deployments for ML Models
Run two model versions side by side, validate the new one with health checks, and swap traffic instantly with rollback
How to Build Feature Flags for ML Model Rollouts
Safely deploy ML models with percentage-based traffic splitting, shadow mode, and instant rollback using feature flag systems
How to Build Model Caching and Request Batching for ML Inference
Speed up your ML API with prediction caching and smart batching. Cut response times by 90% and double your GPU throughput with working code.
How to Implement Canary Deployments for ML Models
Deploy new model versions to a small percentage of traffic first and promote or rollback based on real metrics
How to Monitor ML Model Performance with Evidently AI
Set up Evidently AI to track data drift, model quality, and deploy automated monitoring for your ML pipelines.
How to Serve ML Models with BentoML and Build Prediction APIs
Turn your trained models into scalable REST APIs with BentoML’s model serving framework and one-click containerization
How to A/B Test LLM Prompts and Models in Production
Split traffic between prompt variants, collect quality metrics, and pick winners with statistical confidence
How to Build ML Pipelines with Kubeflow on Kubernetes
Step-by-step guide to creating reproducible ML workflows with KFP v2 on Kubernetes, from local setup to production pipelines.
How to Deploy LLMs to Production with Docker and FastAPI
Ship a containerized LLM inference server with streaming, concurrency handling, and production hardening
How to Detect Model Drift and Data Drift in Production
Catch silent model degradation early using drift detection, statistical tests, and automated monitoring pipelines
How to Load Test and Benchmark LLM APIs with Locust
Find your LLM API’s breaking point before your users do by running realistic load tests with Locust
How to Monitor LLM Apps with LangSmith
Track every LLM call, measure quality with evaluations, and catch regressions before users notice them
How to Serve LLMs in Production with SGLang
Get an SGLang server running, send requests via the OpenAI SDK, and fix the errors you’ll actually hit
How to Serve LLMs in Production with vLLM
Set up vLLM to serve open-source LLMs with an OpenAI-compatible API endpoint
How to Set Up CI/CD for Machine Learning Models with GitHub Actions
Automate model training, testing, and deployment using GitHub Actions workflows with CML and DVC
How to Version and Deploy Models with MLflow Model Registry
Go from trained model to versioned, aliased, and served artifact using MLflow’s Python SDK