Tue. Jul 23rd, 2024

What is machine learning model deployment, and how to deploy ML models? In this Qwak guide, we will talk about these in detail. 

Machine learning deployment is the process of deploying an ML model in a live environment. The said model can be deployed across a number/range of varying environments – while often integrated with an app via an API. Deployment is a key step in gaining operational value from machine learning for an organization. 

The ML models are developed in a local or offline environment, so they will need to be deployed for using live data. How to deploy ML models – deployment from an offline environment to a real-world application can become complex. 

This guide will explore some basic steps needed for machine learning deployment in a container environment; also the challenges an organization might face, and the tools needed to streamline the process. 

How to Deploy ML Models

ML deployment can be a complex task and shall vary depending on the system environment and the type of ML model. Every organization has existing DevOps processes, which need adaptation for the machine learning deployment. So, how to deploy ML models in a containerized environment? 

Below are four broad steps for the general deployment process for ML models in a containerized environment. 

Steps to Machine Learning Deployment 

  1. Create and develop a model inside a training environment.
  2. Test and clean code to get it ready for deployment 
  3. Prepare for container deployment 
  4. Ensure and plan continuous maintenance and monitoring after ML deployment 

Creating ML model in Training Environment 

Often, data scientists develop many ML models, and only a few make it to the deployment phase. The models are built in an offline or local environment and are fed by training data. The models differ depending on the task the algorithm is being trained for. 

Organizations might be using ML models for a variety of needs and reasons. For example, streamline the monotonous administrative tasks, such as tweaking marketing campaigns, growing system efficiency, or the initial stages of research and development. 

Ready for deployment – Test & Clean Code

This step concerns checking if the code is of satisfactory quality to be deployed and ensuring that the model functions well in a new live environment; this is also important so the members of the organizations can see and understand the model’s creation process. The code for the ML model will need scrutiny and streamlining where needed. 

Three basic steps to prepare for deployment

  1. Make an in-depth read-me file to explain the model ready for deployment 
  2. Securitize and clean code and functions while also clearly naming conventions using a style guide
  3. Run tests on the code to see if the model functions as expected. 

Next on how to deploy ML models in a containerized environment is ML model preparation for deployment;

ML Model Preparation for Container Deployment 

Containerization is a potent tool in the realm of machine learning deployment. Containers are possibly the ideal environment for ML deployment – and can further be described as operating system visualization. Containerization is a popular choice because it makes scaling easy and makes depoying and updating models straightforward. 

Containers have all elements required for the machine learning code to function properly, providing a consistent environment. ML model architecture is often made with numerous containers and container orchestration platforms that aid with container management automation like monitoring, scaling, and scheduling. 

Past the ML Model deployment

A successful ML deployment ensures that the ML model is functioning initially in a live setting. Constant and iterative monitoring and governance are required to ensure the model stays on track and keeps working effectively. Machine learning models require set processes to monitor them, which can be challenging. Yet, making sure that the model runs successfully ensures ML deployment’s ongoing success – and models can be optimized to avoid outliers or data drifts. 

Once these processes are planned and streamlined, data drifts and inefficiencies can be detected early and resolved early. Monitoring a model post-deployment ensures that it will stay effective for an organization in the long term. 

Now, as we have seen in detail how to deploy ML models in a containerized environment, let’s look into some challenges it poses. 

Machine Learning Deployment Challenges 

An ML model developed in a local or offline environment while deployed in a live environment will almost always bring new risks and challenges. One of the bigger ones is bridging the gap between data scientists who have developed the model and the developers who will essentially deploy the model. As skills set almost and may not overlap in these unique areas, efficient workflow management is necessary. 

Primary Challenges of ML deployment

  1. Limited or lack of communication between data scientists and the development team can cause inefficiencies in the deployment process 
  2. Make sure to bring the right infrastructure and environment in place for ML deployment 
  3. The constant monitoring of model efficiency and accuracy in a real-work setting is difficult for achieving vital optimization.  
  4. Scaling ML models from training to real-world data, most importantly when the capacity needs to be elastic. 
  5. Better explanation of predictions and results, so the algorithm is considered reliable in the organization. 

There are many products and tools to streamline machine learning deployment. We will talk about these tools in our next article on Qwak Blog