Machine learning is one of those areas that every business invests in today. The complete lifecycle of a machine learning application begins with data collection and ends with model deployment. In this article, I’ll walk you through what model deployment in machine learning is.
What is Model Deployment?
In machine learning, the deployment of a model is the most important step in the entire lifecycle of the machine learning model to serve it as an end-to-end application. After training a model and evaluating it on the test set, we need to serve it in a format so that we can use it when needed.
First, we train a model and then we save it in Pickle format. So, model deployment means consuming the predictions made by a machine learning model in the form of an application. Depending on how you want to use this model to make predictions, you can deploy the model for batch consumption or real-time consumption.
Batch consumption means planning a model’s forecast, such as after every hour, day or week depending on the problem. In real-time consumptions, a trigger is defined to initiate the process of the machine learning model to make predictions. For example, deciding whether a transaction is a fraud or not when a transaction is initiated.
Tools and Services for Model deployment
There are so many cloud platforms and frameworks today that can be used for the task of deploying models. Some of the most popular services and tools used in the market for deploying machine learning models are:
- Functions-as-a-Service (FaaS): These platforms are used for the fully managed deployment of machine learning models as HTTP endpoints. One of the popular examples of FaaS includes services like IBM Cloud, Amazon AWS, Google Cloud, Microsoft Azure, etc.
- DIY: If you don’t want to use the FaaS platforms I just mentioned above, you can deploy a machine learning model on your own using frameworks like Flask or Django. For example, watch this comprehensive tutorial on how to deploy a machine learning model using HTML, CSS, and Python.
The process of presenting a machine learning model as an end-to-end application that can be used by anyone is known as model deployment. It is used either for batch consumption or for real-time problems depending on the problem we have trained a model for. There are two popular methods for deploying models: Faas (Google Cloud, Amazon AWS, Microsoft Azure, etc.) and DIY using Flask or Django.
I hope you liked this article on what is model deployment in machine learning. Feel free to ask your valuable questions in the comments section below.