MLOps
The practice of deploying, monitoring, and operating machine learning models in production reliably and efficiently.
Definition
What is MLOps?
MLOps (Machine Learning Operations) is the set of practices, tools, and culture that combines ML development (Dev) and ML operations (Ops) to reliably and efficiently deploy and maintain ML models in production. MLOps extends DevOps principles to the ML lifecycle: continuous training (CT), continuous integration for model code and data pipelines, continuous deployment of models, model monitoring for drift and degradation, feature store management, experiment tracking, and model registry governance. A mature MLOps platform enables organizations to move from 'research ML' to 'production ML' — where models are deployed reliably, monitored continuously, and retrained automatically.
Why It Matters
Studies show that 87% of ML projects never make it to production, and many that do degrade rapidly without monitoring. Organizations invest heavily in data science and model development but underinvest in the engineering infrastructure required to make models reliable in production. MLOps closes this gap — enabling data science teams to deploy models in hours rather than weeks, and operations teams to maintain model quality without manual intervention.
How It Works
MLOps platforms typically include: a feature store for consistent feature engineering across training and serving, an experiment tracking system (MLflow, W&B), a model registry for versioning and staging, CI/CD pipelines adapted for model training and evaluation, model serving infrastructure (online and batch), and monitoring for data drift, concept drift, and model performance degradation.
Our Approach
Paxanimi's Approach to MLOps
Paxanimi builds MLOps platforms for enterprises deploying ML models at scale — from initial architecture design through production deployment and monitoring. We implement MLOps tooling on AWS SageMaker, Azure ML, GCP Vertex AI, and open-source stacks (MLflow, Kubeflow, Argo). Our AI security practice ensures that MLOps pipelines include model risk assessment, adversarial testing, and data governance controls appropriate for regulated industries.
Quick Reference
- Category
- AI Security
- Related Services
- Software Development
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