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A Complete Guide to Machine Learning Model Deployment

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Saurabh K Shah
January 5, 202415 min read
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A Complete Guide to Machine Learning Model Deployment

The Deployment Gap

Building a machine learning model is only half the battle. Getting it into production — and keeping it running reliably — is where most teams struggle. This guide covers the full deployment lifecycle.

MLOps Fundamentals

MLOps brings DevOps principles to machine learning. The core practices:

  • Version control for models, data, and experiments
  • Automated training pipelines with reproducible results
  • Model registries for tracking deployed models
  • A/B testing infrastructure for comparing model versions

Deployment Patterns

There's no one-size-fits-all deployment pattern. Choose based on your requirements:

  • **Batch prediction:** Process large datasets on a schedule. Simple and cost-effective.
  • **Real-time inference:** Serve predictions via API. Requires careful latency optimization.
  • **Edge deployment:** Run models on-device. Great for privacy and offline use.
  • **Shadow deployment:** Run new models alongside old ones to compare without risk.

Monitoring in Production

Models degrade over time. Your monitoring should catch:

  • Data drift — when input data distributions shift from training data
  • Concept drift — when the relationship between inputs and outputs changes
  • Performance degradation — accuracy, latency, and resource usage
  • Prediction distribution shifts — unexpected changes in model outputs

Model Versioning

Treat models like software artifacts. Every deployed model should have a clear lineage back to the training data, code, hyperparameters, and evaluation metrics that produced it.

Tools like MLflow, DVC, and Weights & Biases make this manageable.

Conclusion

ML deployment is an engineering discipline, not just a data science task. Invest in infrastructure, monitoring, and process. The teams that treat model deployment as a first-class engineering concern ship faster and with fewer surprises.

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Frequently Asked Questions

Regular software is deterministic — the same input always gives the same output. ML models are probabilistic, their performance depends on the data they see, and they degrade over time as real-world patterns shift. That means you need monitoring infrastructure, retraining pipelines, A/B testing frameworks, and rollback mechanisms that traditional software doesn't require. It's basically software deployment plus data pipeline management plus statistical monitoring.
Wrap it in a REST API using Flask or FastAPI, containerize it with Docker, and deploy it to a managed service like AWS ECS or Google Cloud Run. Don't overcomplicate things with Kubernetes or custom serving infrastructure until you actually need the scale. For batch predictions, a simple scheduled job that writes results to a database is often the most practical first step.
It depends entirely on how fast your data distribution shifts. For something like fraud detection where patterns change weekly, you might retrain daily. For something like image classification where the domain is stable, monthly or quarterly might be fine. The real answer is: set up drift monitoring and let the data tell you. Retrain when performance drops below your threshold, not on an arbitrary schedule.
Not until you have at least 5-10 models in production. Before that, your ML engineers should be able to handle deployment with good tooling and some DevOps support. Once you cross that threshold, a dedicated MLOps function starts paying for itself by standardizing pipelines, reducing deployment time, and preventing the "works on my laptop" problem at scale.

Written by

S

Saurabh K Shah

Founder & CEO

Saurabh has spent 20+ years building enterprise software. He's deep into AI/ML integration and digital transformation, and he's helped companies on four continents scale their tech operations from early stage to global reach.

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