DATA & AI

MLOps

Machine Learning Operations—the practices, tools, and workflows that take ML models from notebooks to production and keep them running reliably at scale.

The ML Production Gap

MLOps addresses a critical challenge: most machine learning models never make it to production. Data scientists build impressive models in Jupyter notebooks, but those models never serve real users because deployment, monitoring, and maintenance are too complex.

It's like DevOps but for machine learning—the practices and tools for deploying models reliably, monitoring their performance, retraining them as data changes, and managing the entire ML lifecycle. MLOps bridges the gap between data science experiments and production systems.

Without MLOps, even great ML models fail in production. With MLOps, average models can deliver significant business value.

Core MLOps Practices

1

Version Control for Everything

Track not just code, but also data versions, model versions, and experiment configurations. When a model performs poorly, you need to know exactly what changed.

2

Automated Pipelines

Automate the entire workflow from data ingestion to model deployment. Changes trigger automatic retraining and testing. No manual steps means fewer errors.

3

Continuous Monitoring

Track model performance in production—accuracy, latency, data drift, prediction distribution. Models degrade over time as data patterns shift; monitoring catches this.

4

Model Governance

Document who built each model, what data it uses, how it was validated, and who approved it for production. Critical for compliance and debugging.

5

A/B Testing & Rollouts

Deploy new models gradually, comparing against existing versions. If the new model performs worse, roll back automatically. No big-bang deployments.

When You Need MLOps

Single model in production: You might not need full MLOps infrastructure. Manual deployment and monitoring can work for simple cases.

Multiple models or frequent retraining: As complexity grows, manual processes break down. MLOps becomes essential for reliability and efficiency.

Regulated industries: Finance, healthcare, and other regulated sectors require model governance, audit trails, and reproducibility—core MLOps capabilities.

Business-critical predictions: When model failures directly impact revenue or customer experience, you need MLOps-level monitoring and rollback capabilities.

The MLOps Ecosystem

Experiment Tracking
MLflow, Weights & Biases
Model Serving
TensorFlow Serving, Seldon
Orchestration
Airflow, Kubeflow, Prefect
Monitoring
Prometheus, Grafana

Questions About MLOps?

If you're struggling to get ML models to production or need help building reliable ML infrastructure, let's discuss what MLOps practices would help.

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