Working with data is one thing, but deploying a machine-learning model to production can be another.<\/p>\n\n\n\n
Data engineers are always looking for new ways to deploy their machine-learning models to production. They want the best performance, and they care about how much it costs.<\/p>\n\n\n\n
Well, now you can have both!<\/p>\n\n\n\n
Let’s take a look at the deployment process and see how we can do it successfully!<\/p>\n\n\n\n
Grab your aromatic coffee <\/a>(or tea<\/a>) and get ready…!<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n Most data science projects deploy machine learning models<\/a> as an on-demand prediction service<\/b> or in batch prediction<\/b> mode. Some modern applications deploy embedded models<\/b> in edge and mobile devices.<\/p>\n\n\n\n Each model has its own merits. For example, in the batch scenario, optimizations are done to minimize model compute costs<\/i>. There are fewer dependencies<\/i> on external data sources and cloud services. The local processing power is sometimes sufficient for computing algorithmically complex<\/i> models<\/i>.<\/p>\n\n\n\n It is also easy to debug an offline model when failures<\/i> occur or tune hyperparameters<\/i> since it runs on powerful servers.<\/p>\n\n\n\n On the other hand, web services can provide cheaper<\/i> and near<\/i> real-time predictions<\/i>. Availability of CPU power is less of an issue if the model runs on a cluster or cloud service. The model can be easily made available to other applications through API calls<\/i> and so on.<\/p>\n\n\n\n One of the main benefits of embedded machine learning is that we can customize it to the requirements<\/i> of a specific device<\/i>.<\/p>\n\n\n\n We can easily deploy the model to a device, and its runtime environment cannot be tampered with by an external party. A clear drawback is that the device needs to have enough computing power and storage space.<\/p>\n\n\n\n The simplest way to deploy a machine learning model is to create a web service for prediction<\/a>. In this example, we use the Flask web framework<\/a> to wrap a simple random forest classifier<\/a> built with scikit-learn.<\/p>\n\n\n\n To create a machine learning web<\/a> service, you need at least three steps.<\/p>\n\n\n\n The first step is to create a machine learning<\/a> model, train it and validate its performance. The following script will train a random forest classifier. Model testing and validation<\/a> are not included here to keep it simple. But do remember those are an integral part of any machine learning<\/a> project.<\/p>\n\n\n\nHow to deploy a machine learning model in production?<\/b><\/h2>\n\n\n\n
Deploying machine learning models as web services.<\/b><\/h2>\n\n\n\n