Definition
Model deployment is the process of putting machine learning models into production. That is, it is the process of putting your trained machine learning (ML) model to work so that other systems or real users can use it. This makes the model’s predictions available to users, developers or systems, so they can make business decisions based on data, interact with their application (such as predicting sales or analyzing an image) and so on [1].
For example, imagine a scenario where JAMB's AI team developed a cheating detection model that identifies malpractice with excellent precision during simulated testing scenarios. Although achieving high detection rates is encouraging, if the model is just sitting in the research lab, it means its value is just only theoretical and can't catch actual exam cheaters. Even if it's the most advanced proctoring technology in West Africa, the model only provides value after deployment in real examination centers, where it can monitor actual candidates and flag suspicious behavior during UTME sessions.
Origin
Model deployment
originated in the early days of artificial intelligence and machine learning,
when computing power were limited and deployment was a manual and resource-demanding
process. Machine learning models were basically academic experiments during the
1960s and 1970s, when deployment simply meant executing statistical
calculations on mainframe computers.
Early pioneers like Arthur Samuel, who developed checkers-playing programs, and researchers at institutions like IBM and MIT, initiated research into how computational models transition from research labs to solve practical problems. These initial efforts faced major technical limitations: limited computing power, minimal data storage capabilities, and basic infrastructure [3].
Context and Usage
Model deployment
is fundamental to implementing machine learning across industries. With
successful deployment, machine learning models can provide actionable insights
and achieve significant outcomes across these industries. Some of their
real-world applications are as follows:
- Fraud Detection: Financial institutions can spot suspicious activities by using models to scrutinize transactions in real-time.
- Personalized Recommendations: Deploying models enable E-commerce platforms to use user behavior to recommend products or services.
- Healthcare Diagnostics: Deployed models help in diagnosing diseases by examining medical imaging and patient data.
- Customer Support: AI-driven chatbots powered by deployed models provide instant, accurate responses to user queries.
- Supply Chain Optimization: Retailers take advantage of deployed models for demand forecasting and inventory management.
- Predictive Maintenance: In manufacturing, deployed models predict equipment failures, reducing downtime and maintenance costs.
Why it Matters
In machine
learning lifecycle, model deployment is a very important step. It converts a conceptual
model into a practical tool. It is when a model is deployed, that it can reveal
valuable insights and solutions to various problems. Using these insights,
businesses can then improve their operations, make better decisions, and provider
better services to their customers [4].
In Practice
A real-life case study of a company offering model deployment services can be seen in the case of Domo. Domo stands out for making AI accessible to business users—not just data scientists. Their strategy centers on operationalizing AI by embedding model outputs directly into dashboards, apps, and automated workflows. This is very suitable for organizations that want to incorporate predictions into day-to-day decisions without needing a dedicated MLOps function. With built-in visualization, automation, and alerting, Domo helps close the gap between insight and action, fast [5].
Learn More
Related Model
Training and Evaluation concepts:
- Model Compression: Techniques for reducing model size and computational requirements while maintaining performance
- Model Evaluation: Process of assessing how well a model performs on test data and other metrics
- Model Explainability: Techniques and methods for making AI model decisions transparent and understandable
- Model Interpretability: Ability to understand and explain how a model makes decisions
- Model Monitoring: Ongoing tracking of model performance and behavior in production environments
- Iguazio. (2025). What is Model Deployment?
- Geeksforgeeks. (2025). Machine learning deployment
- Johnson, L. (2025). The origins of model deployment in machine learning
- Alooba. (2025). Model Deployment
- Domo. (2025). 10 AI Model Deployment Platforms to Consider in 2025