MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
MLOps bridges the gap between training a model in a notebook and running it reliably in production. It covers the full lifecycle: data versioning, experiment tracking, model training pipelines, evaluation, deployment, monitoring, and retraining triggers.
The MLOps maturity spectrum ranges from Level 0 (manual everything — Jupyter notebook to production) to Level 3 (fully automated CI/CD for ML — automatic retraining triggered by data drift detection). Most growth teams should aim for Level 1-2: automated training pipelines, version-controlled experiments, automated evaluation against test sets, and basic model monitoring.
Key MLOps tools include experiment trackers (Weights & Biases, MLflow), feature stores (Feast, Tecton), model registries (MLflow, Vertex AI), serving platforms (BentoML, Seldon), and monitoring solutions (Evidently, Arize). For teams using primarily LLMs and APIs, "LLMOps" is an emerging subset focused on prompt management, cost tracking, evaluation pipelines, and guardrails — with tools like LangSmith and Helicone filling this niche.
Related Terms
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.
Blue-Green Deployment
A release strategy that runs two identical production environments, switching traffic from the current version (blue) to the new version (green) once it passes validation, enabling instant rollback.
Further Reading
LLM Cost Optimization: Cut Your API Bill by 80%
Spending $10K+/month on OpenAI or Anthropic? Here are the exact tactics that reduced our LLM costs from $15K to $3K/month without sacrificing quality.
AI-Driven A/B Testing: From Manual Experiments to Automated Optimization
Stop running one test at a time. Learn how to use multi-armed bandits, Bayesian optimization, and LLMs to run 100+ experiments simultaneously and find winners faster.