MLOps & Model Deployment
MLOps brings DevOps practices to machine learning — automating model training, deployment, and monitoring. A production ML system is ~5% model code and ~95% surrounding infrastructure.
40 min•By Priygop Team•Last updated: Feb 2026
MLOps Pipeline
- Feature store — Centralized feature computation and storage. Feast, Hopsworks, Tecton. Consistent features between training and serving
- Experiment tracking — MLflow, Weights & Biases. Track: metrics, hyperparameters, artifacts, code version. Reproducibility
- Model registry — Versioned model storage. Staging → Production promotion workflow. MLflow Model Registry, Hugging Face Hub
- Model serving — FastAPI with ONNX/TorchScript. TorchServe, TensorFlow Serving. Triton Inference Server for GPU serving
- CI/CD for ML — Retrain trigger: schedule, data drift detection, performance degradation. Automated validation before promotion
- Data drift monitoring — Statistical tests: KL divergence, PSI (Population Stability Index). Alert when input distribution shifts from training data
- Model drift monitoring — Track prediction distribution and business KPIs. Evidently AI, Arize, WhyLabs for production monitoring
- A/B testing models — Shadow mode: new model runs but results discarded. Canary: small % real traffic. Champion/Challenger experiments
AI/ML Career Paths
- Data Scientist ($80-160K) — EDA, model building, A/B testing. Python, statistics, business insight. Most common ML role
- ML Engineer ($90-175K) — Production ML systems, MLOps, scalable training. Software engineering + ML skills
- AI Research Scientist ($110-250K) — Novel algorithms, publications. PhD usually required. FAANG or research labs
- LLM/GenAI Engineer ($95-185K) — Fine-tuning, RAG systems, prompt engineering, LLM application development
- Data Engineer ($80-160K) — Build data pipelines (ETL), data warehouses, lakehouse architecture. SQL, Spark, dbt
- MLOps Engineer ($85-165K) — ML infrastructure, feature stores, model monitoring, CI/CD for ML