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Five Things To Consider Before Serving Machine Learning Models To Users

In this blog, we will explain ‘Model Serving’, the common hurdles while serving models to production, and some of the key considerations before deploying your model to the production.

Neetika Khandelwal
Five Things To Consider Before Serving Machine Learning Models To Users
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You might wonder if it is enough to train and test your model on various datasets and retrain them on additional data based on previous training results. As a next step, your model is ready for the deployment process.

But, this isn't the case. Model deployment is likely the most challenging aspect of MLOps and the most misunderstood. 

Model training is relatively conventional, with a well-known set of tools and methodologies for utilizing them, such as scikit-learn, xgboost, and others. The deployment of models is the polar opposite. Your deployment strategy and infrastructure are intricately linked to user expectations, business regulations, and existing technology at your company. 

Model serving is one of the critical steps of model deployment and should not be neglected.

What is Model Serving?

Serving of models from training environment to the end user
Serving of models from training environment to the end user

Machine learning has the potential to transform businesses and take them to great heights. Still, it is possible only when models are put into production and consumers can easily interact with them. Deploying machine learning models into production comprises several sub-tasks, each of which is crucial in its own right. 

Model serving is one of these sub-tasks.

It is a technique for integrating a machine learning model into a software system. In other words, you can say that it is the process of exposing a trained model to an endpoint (say, end-users).

Model serving simply means hosting machine-learning models (on the cloud or on-premises) and making their functions available via API so that applications can integrate AI into their systems. With this action, you can use the machine learning model with just a few clicks. Model serving is critical as businesses cannot deliver AI solutions to an extensive user base without making them accessible.

Generally, models take a long time to go from a prototype to a production state. But with model serving, you may easily do that in a few minutes. 

Model Serving accepts data science-standard formats for your models. ONNX (Open neural network exchange), PMML (Predictive model markup language), and TensorFlow are examples of these formats.

Some popular tools to help you in your model serving journey are Cortex, Seldon Core, KFServing, Streamlit, MLFlow, and BentoML.

Refer to our MLOps tools collection for more.

Model Serving Strategies

Following are some of the common model serving strategies used by ML professionals:

  1. Offline Serving: The end-user is not immediately exposed to your model in this method. It works by performing a batch inference job on your test dataset, caching the findings in a high-performance database, and then serving those results to end-users. The most significant downside of offline serving is that it is a "cold" deployment.
    It usually only works in 'push' workflows, where the end-user only accepts requests from the model server but does not create any requests. Offline serving can be used in 'pull' workflows where the end-user can send requests to the model server. It's challenging to implement because end-users often have unrealistic expectations regarding response times.
  1. Online model as service: When a model learns from human input automatically, it is said to be online. A neural network with a batch size of one is a canonical example of online machine learning. A complete backpropagation pass is started based on the input each time a user requests to your model endpoint, changing the model weights simultaneously with serving the request.
  1. Model as Service: The most typical model serving strategy in production environments is to deploy a model as a (micro)service. Clients are given access to a model interface via an API endpoint in this paradigm (REST or otherwise). Clients send POST or GET queries to the endpoint to get what they need. This is a scalable, responsive model service deployment technique with a flexible deployment strategy.
  1. Edge deployment: It refers to serving the model directly on the client device rather than on the server because it shifts the computation from the server to the edge (the client device). It's tricky since the client's hardware (a web browser, a user computer, or a mobile device) is severely limited.

Challenges in Serving ML Models to Production

  1. Lack of consistency with model deployment and serving procedures might cause issues in scaling your model deployments to match the growing number of ML use cases throughout your business after models have been trained and are ready to deploy in a production environment.
  1. In production, the model degrades over time due to various causes such as data drift, environmental changes, and so on. Teams must have access to the information they need to troubleshoot and fix a problem to take action.
  1. Some projects require batch predictions regularly, while others require on-demand forecasts in response to an API request to predict using the model. This is one of the reasons why it's challenging to apply models to multiple use cases; machine-learning projects require a lot more than just models and algorithms to be successful. Most will require infrastructure, alerting, maintenance, and more.

Top Five Considerations before Deploying ML Models to Production

After ample training and testing of your machine learning model, you are ready to serve the model to the end-users. But before proceeding with this step, there are some critical points that you need to keep in mind and make decisions over it so that it becomes easy for you to deploy your model and the end-users to use the model.

Here are some key considerations:

  1. Model Inference Type: A primary consideration is how you intend to service your models. The easiest option is to run the model as a service, which is often an HTTP microservice to which you make requests and receive responses. Managed solutions make model deployment and monitoring considerably more accessible in this situation. Another, and perhaps most prevalent, use case is using ML models to enrich a data pipeline. It's possible to accomplish this in batches or in real-time.

  1. Service location: Another important consideration is the locations where you provide your services, whether on-premises or cloud. When you run in the cloud, you have access to a variety of services. AWS SageMaker is a popular example, as it handles both the model serving and monitoring aspects, making things a lot easier. You'll need corporate solutions like Seldon or data pipelines like Spark if you're running on-premises. This technique, however, necessitates more work.

  1. Automation testing: You'll require automated testing to prove that your model works as expected. These tests will serve as an early warning system for you. If they fail, your model is invalid, and you won't be able to release the software or features that rely on it. Make sure that the tests strictly enforce the model's minimal performance requirements. Over time, collect outlier and fascinating cases from activities that create unexpected outcomes that could break the system. These must be comprehended and included in the regression test suite. After each model update and before each release, run the regression tests. 

  1. Autoscaling: It's critical to ensure that your model can handle the appropriate amount of predictions. The volume of required projections can be surprising (e.g., online users). When the system is static, peaks in traffic might also create server overload.

  1. Stress and data-edge testing: The team runs stress testing to see how responsive and reliable the model is when faced with many prediction requests over a short period. They will be able to benchmark the model's scalability under stress and identify its breaking point this way. The test also lets them see if the model's prediction service achieves the service-level objective and responds on time.

With data edge testing, you can ensure that your system can handle data edge cases. This will assist you in identifying weak points and writing hard-coded rules for scenarios where the sample differs significantly from previous ones.


Now you have come to the end of this blog, and you must have got an overview of how crucial is the model serving step. Model deployment showcases your model functionality to the end-users. Your model mustn't misbehave on real-world data, so it's essential to be cautious before deploying your model into production.

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