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MLOps (ML Operations)

⬢ TIER 2Tech
High
Salary impact
8 months
Time to learn
Hard
Difficulty
5
Careers
TL;DR

MLOps Engineer bridges machine learning and DevOps: automated training pipelines, model versioning, reproducible deployments, continuous monitoring, and retraining workflows. Career path: Practitioner (experiment tracking, basic CI/CD, $120-145k) → Senior (feature stores, model serving, A/B testing, $145-180k) → Staff (distributed training, Kubernetes ML, multi-model serving, $180-260k) over 6-9 months. 87% of ML projects never reach production—MLOps closes the gap. $126B market by 2025. Used by Netflix, Uber, Airbnb for production ML systems.

What is MLOps (ML Operations)

MLOps bridges machine learning and production systems. While DevOps automates code deployment (build → test → release), MLOps automates the full ML lifecycle: data pipelines → training → evaluation → deployment → monitoring → retraining. The critical difference: ML models degrade over time (data drift, concept drift) and require continuous monitoring, not just one-time deployment. MLOps engineers own experiment tracking (MLflow, Weights & Biases), feature pipelines (Feast, Tecton), model serving (FastAPI, Ray Serve, KServe), and monitoring systems that detect model degradation and trigger retraining. In 2026, 87% of ML projects still fail to reach production—MLOps is the discipline that closes that gap. The market recognizes this: MLOps engineers command $120–260k salaries depending on seniority and company. Tools like Kubeflow, Apache Airflow, and Seldon Core are industry standard; mastery of them is non-negotiable for any ML platform team.

🔧 TOOLS & ECOSYSTEM
MLflowKubeflowApache AirflowDVCBentoMLSeldon CoreTriton Inference ServerGoogle Vertex AIAWS SageMakerWeights & BiasesFeastEvidently

đź’° Salary by region

RegionJuniorMidSenior
USA$120k$165k$220k
UKÂŁ75kÂŁ105kÂŁ160k
EU€80k€115k€175k
CANADAC$125kC$170kC$265k

âť“ FAQ

MLOps vs DevOps — what's the difference?
DevOps automates software deployment: code → build → test → release. MLOps extends this for ML: data → train → evaluate → deploy → monitor → retrain. The key difference: ML models degrade over time (data drift, concept drift) and require continuous monitoring + retraining, not just deployments. DevOps engineers own infrastructure; MLOps engineers own the model lifecycle.
How do I detect and handle model drift?
Model drift = prediction accuracy drops without code changes. Detect via: (1) Monitor actual labels vs predictions (post-hoc), (2) Track feature distributions (input drift), (3) Monitor prediction confidence (uncertainty drift). Tools: Evidently, WhyLabs, Arize. Response: retrain on recent data, A/B test new model, trigger alerts. For real-time: use Weights & Biases or custom monitoring dashboards.
What's a feature store and why do I need one?
Feature store (Feast, Tecton, Hopsworks) is a centralized registry of ML features: reusable transformations + computed values shared across models and teams. Why: (1) avoids training-serving skew (same features in both), (2) enables feature reuse across models, (3) manages feature freshness and SLA. For startups, skip it. For 3+ models, it pays for itself in prevented bugs and faster iteration.
Online vs offline serving — which model should I use?
Offline: batch scoring on a schedule (e.g. nightly). Fast, cheap, no SLA pressure. Use for: recommendations, reports, ETL. Online: serve predictions in real time via API. Use for: user-facing rankings, fraud detection, real-time personalization. Most mature systems use both: online for user-facing, offline for batch analytics.
How do I set up A/B testing for ML models?
Route traffic: X% to model A (baseline), Y% to model B (new). Measure: conversion, engagement, latency, cost. Tools: Seldon Core, Ray Serve, custom Flask/FastAPI logic. Duration: min 2 weeks for 100+ conversions. Canary deploys are safer: start at 5% new model, ramp to 100% if metrics hold. Track via PostHog or custom dashboards.
How do I manage GPU costs in ML pipelines?
GPUs are expensive (~$3/hour on-demand). Strategies: (1) Spot instances (70% discount, preemption risk), (2) Batch jobs overnight (cheaper off-peak), (3) Model quantization (use smaller models), (4) Multi-GPU per job to amortize startup, (5) Kubernetes autoscaling (scale down idle clusters). Monitor via SageMaker, Vertex AI dashboards. For training: Spot is safe. For serving: mix on-demand + Spot with traffic spilling.
What's training-serving skew and how do I prevent it?
Training-serving skew: model is trained with features computed one way, but served with features computed differently. Example: training uses 90-day average, serving uses 30-day average. Result: model works offline, fails in production. Fix: (1) Use a feature store to guarantee same logic, (2) Unit test feature pipelines, (3) Monitor input distributions post-deploy, (4) Freeze training code and replicate it exactly in serving code.

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