What Is Machine Learning Model Deployment 

When data scientists start designing machine learning (ML) models, their end goal is the deployment of the model. Deploying a model refers to placing an ML model into an environment where it can do the job it was created to do. 

Deploying a model into production requires a proper machine learning deployment architecture involving various teams of ML professionals, software, and machinery. In this blog, we will delve into the action of model deployment and identify the challenges it currently faces. 

What Is Model Deployment in ML?

Model deployment is the process of implementing a fully functioning machine learning model into production where it can make predictions based on data. Users, developers, and systems then use these predictions to make practical business decisions. Models can be deployed in multiple ways, but they’re usually integrated with apps through APIs so that all users can gain access to them. 

Model deployment is the fourth stage in the model development life cycle after planning, data preparation, and model development. However, this stage is usually the most cumbersome for data scientists as it takes time and resources. Any given model typically undergoes countless modifications until it is ready to be released into a production environment. Also, some models don’t even make it to the deployment stage if they don’t meet the desired objectives.

How to Deploy a Machine Learning Model Into Production

As mentioned earlier, deploying an ML model is a lengthy process, often broken down into four main stages.

1. Prepare to Deploy

All types of data science models need to be trained first before deployment. Similarly, data scientists select algorithms, set parameters, and train machine learning models. They train a model by feeding it data and working to increase its prediction accuracy and minimize biases. 

Preparing the model involves various experiments until it demonstrates efficient performance. While the development team works on the model’s training, the deployment team analyzes the environment to find the application that best suits the deployment, identify what resources it will need, and how data will be fed. 

2. Validate the Model

After the development team is satisfied with the model’s performance, they pass it on to the validation team. In this stage, the professionals make sure that the model’s successful performance is long-lasting by testing it on a new data set. Then they compare the new results to the previous stage’s results and choose the most successful one to send to deployment. 

In reality, only a few models pass the validation stage. The model is validated mainly due to compliance and organizational governance requirements to ensure that all development procedures meet the organization’s rules and requirements and that the data used corresponds to the needs of end users.

3. Deploy the Model

Once the validation team chooses the best operational model, it finally moves into the hands of the deployment team. The process of actually deploying the machine learning model consists of several steps: 

• First, the deployment team moves the model to a deployment environment, which usually includes specific servers or middleware. Here the model can gain access to the hardware and other resources it needs to draw data from. 

• Then, the team integrates the model into a process. One way of doing this is by connecting it to the end user’s computer via APIs or incorporating it into the user’s software.

• Finally, the end users activate the model, access its data, and implement its output. 

4. Perform Model Monitoring

The process of machine learning deployment doesn’t finish with model integration. Any machine learning model can deteriorate over time due to multiple reasons: 

• Data drift – changes in the data and the industry can cause the current model’s data to no longer be relevant to its operations. 

• Concept drift – end users may change their expectations about correct predictions making the model less relevant over time.

• Changes in data integrity – data being fed to the model may change in format over time, which can affect model performance. 

• Variance in deployed data –  sometimes, the data may not always be cleaned and filtered during implementation like during training, resulting in lower prediction accuracy. 

This is why constantly overseeing the operations of the ML model is essential to keep it running smoothly and accurately. Model evaluation is the last but not least important stage in model deployment. It allows data scientists to tackle issues before they cause damage to the model and hinder business operations. 

Challenges in Machine Learning Model Deployment

If a machine learning model isn’t properly deployed into production, it cannot provide accurate predictions. In turn, professionals cannot utilize the information to implement efficient business strategies. 

It is estimated that only 10% of all models eventually make it into production. This is because many companies aren’t yet adequately equipped with machinery and software to support many models. At times, there can be a discrepancy between the model’s programming language and the company’s production system, making it impossible or too costly to find a solution. 

Sometimes models don’t see the light of day due to a lack of access to data and a disconnect between IT, data science, and engineering teams. Software developers, data scientists, as well as business professionals need to coordinate more transparently to address problems before they escalate and exchange valuable information for better model development and deployment.

Bottom Line 

Deploying a machine learning model is the stage in the machine learning lifecycle where it is integrated into the company’s production systems and starts identifying patterns from data. These patterns are later used to make viable decisions about the company’s operations. 

However, before deploying the model, different teams of IT professionals, data scientists, and engineers train and test the model, then send it for validation to ensure that it will run successfully with real data. Finally, periodic check-ups and monitoring after deploying the model are also essential for its longevity and success.