Machine learning models are mainly developed offline but must be deployed in a production environment to process real-time data and handle the problem they were designed to solve.
In this blog post, we will explore the fundamentals of deploying an ML model, discuss the challenges you may encounter, and provide steps to streamline the process for greater efficiency.
What is the machine learning model deployment?
Experts estimate that up to 90 percent of ML models never reach the production stage. It is not uncommon for models that performed excellently during the development stage to encounter issues like data skews, scalability limitations, or sudden drops in prediction accuracy when launched into production.
Even for the few machine learning models that do make it to production, the deployment process can be time-consuming, as they require constant attention to ensure quality and efficiency of predictions.
While the main objective of building a machine learning application is to address a problem, an ML model can only fulfill this purpose when dealing with real data. Therefore, deployment is just as crucial as the development phase for any AI project.
Deployment involves transitioning an ML model from an offline environment into an existing production system. This step is pivotal for the model to fulfill its intended purpose and effectively tackle the challenges it was designed for. Deployment establishes an online learning mechanism where the model is continuously updated with new data.
Below is a helpful video with tips on how to manage deployment.
The specific process of ML model deployment may vary depending on the system environment, the model’s type, and the DevOps practices adopted by individual organizations. The best fit depends on factors such as scalability requirements, budget constraints, data privacy regulations, and more.