How Exactly Are AI Models Deployed?

In the past few months, we've seen a lot of AI models being released. From Gemini's Nano Banana to OpenAI's ChatGPT 5.2, different kinds of models are out there and available to use. However, not many people know exactly how these models are deployed. If you're also one of these people, don't worry I gotchu.

AI model deployment is the process of making AI models work in a production environment. It comprises of different processes that ensures models can perform effectively and efficiently in real-world applications.

Before a model is deployed, it must go through a cycle and that cycle usually includes:

• Data Collection
• Preprocessing
• Model Training
• Evaluation
• Deployment
• Monitoring and Maintenance

Each cycle has its own challenges and requirements. Let me break them down.

1. Data Collection: The Foundation of AI

The first step in deploying an AI model is gathering data. This data usually consists of texts, images, audio, videos, etc and is usually gotten from databases, web scraping, APIs, surveys, user behavior, etc.  

Although, the data needs to be relevant, diverse, and of high quality because without good data, even the best AI algorithms won’t perform well.

2. Preprocessing: Cleaning and Preparing the Data

After gathering all the data, it's definitely certain that it will contain some errors such as missing values, duplicates, or even irrelevant information. This is where preprocessing comes in. Preprocessing is mainly about:

• Cleaning and organizing that data.  
•  Fixing errors in the data.  
•  Filling in missing values or removing incomplete entries.  
•  Converting data into a usable format (e.g., turning text into numbers for analysis).  
•  Normalizing values so everything is on the same scale.    This ensures that the data is thoroughly cleaned and ready for use.

3. Model Training: Teaching the AI

After cleaning up the data, it is then used to train the AI model and through continuous (reinforced, supervised or sometimes unsupervised) training, the model is now able to identify patterns, make predictions, solve problems, etc.
This process is usually repeated multiple times until the model performs well to a certain degree of excellence and produces desired outcomes.

4. Evaluation: Testing the AI

After the model is trained using gathered data, it is then trained using new, unused data to see how well it can use it's training data to address real-world scenarios, questions, etc.

It's worth noting that there are some metrics used in this process which include:

• Accuracy: How often is the model correct? 
• Precision/Recall: Does it correctly identify important cases?
• F1 Score: A balance between precision and recall.  
• Latency: How fast can the model process inputs?  
• Scalability: Can it handle a large number of requests simultaneously?
• Robustness: How well does it handle edge cases or unexpected inputs?  

If these metrics aren't met, developers go back to the training data or model architecture to make changes until desired outcomes are reached.

5. Deployment: Putting the AI to Work

Once a model passes evaluation, it’s ready to be deployed in the real world but before that, there are some things to consider. The first being, how and where the model will run. Some models use a lot of resources and this plays a major factor in choosing where to deploy them.

1. Cloud Deployment:  Some models are hosted on platforms like AWS, Google Cloud, or Azure. This option makes the model easy to access and scale. A common example are AI chatbots.
2. Edge Deployment: This is another method of deploying models is through IoT devices such as smartphones, tablets, etc. This method offers offline functionality and the most common example is the face recognition we have on our phones.
3. Hybrid Deployment: This method is a combination of the cloud and edge methods. It's used in some electric cars like Tesla which process data locally and uploads it to the cloud.

After selecting the preferred deployment method, the model is then integrated into a larger system or application. The model could be integrated into:  

• APIs: Wrapping the model in an API (Application Programming Interface) so other systems can communicate with it.  
• Microservices: Deploying the model as an independent service that interacts with other components.  
• Real-Time Pipelines: For systems requiring instant predictions (e.g., fraud detection), the model is integrated into streaming pipelines.  

This integration ensures the model works seamlessly with existing workflows, applications, etc.

Once the deployment and integration methods have been selected, the model is then deployed in the real world where regular users like you and me can start interacting with it. E.g. A recommendation engine on an e-commerce site suggesting products to customers.  

6. Monitoring and Maintenance

Deployment isn’t where it all ends, the AI models need to be constantly monitored to ensure they perform well in real-world conditions. Over time developers track the model's performance by regularly assessing it's accuracy against real-world data, identify and fix errors or biases that emerge after deployment.

From time to time, they periodically update the model based on feedback form users and changes in data patterns to improve the model's output quality.

Deploying AI models is a complex process but then it transforms algorithms into solutions that solve real-world problems, most of which we enjoy today e.g. autonomous vehicles, AI Chatbots, etc.

I hope you now understand how these models are usually deployed. If you did, leave a comment, like and don't forget to follow ❤️.