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.
