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MLOps (ML Operations)

⬢ 層級 2技術
高
薪資影響
8 個月
學習時長
困難
難度
5
職業
一覽

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). 87% of ML projects never reach production, MLOps closes the gap. $126B market by 2025. Used by Netflix, Uber, Airbnb for production ML systems.

什麼是 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.

🔧 工具與生態
MLflowKubeflowApache AirflowDVCBentoMLSeldon CoreTriton Inference ServerGoogle Vertex AIAWS SageMakerWeights & BiasesFeastEvidently

💰 各地區薪資

地區初級中級高級
USA$120,000$165,000$220,000
UK£75,000£105,000£160,000
EU€80,000€115,000€175,000
CANADACA$125,000CA$170,000CA$265,000

❓ 常見問題

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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