Model deployment is the step in the machine learning lifecycle that integrates the model into the business process. In other words, the automated machine learning platform is moved into a production environment, and the outputs are used to drive strategic business decisions.
This is one of the last stages in the lifecycle, but it is one of the most important. Model deployment allows your algorithm to start providing insights into the way your consumers behave and other trends in your industry – helping you gain a competitive advantage in the market.
Proper deployment of an automated machine learning platform is essential for it to be successful in your business. Remember that the goal of this model is to give your leaders practical insights that will allow them to make data-driven decisions – if the model is not reliable, it will not be able to meet these needs.
This process can be complicated because it typically requires the data scientists who constructed the model to work closely with the business professionals who will be using the predictions to implement changes in the organization.
There can often be a discrepancy between the programming language that the machine learning model uses and those understandable by production systems – this requires additional development to correct and delay the deployment of the model.
The goal is to have a seamless machine learning model deployment so that the leaders in your organization can start benefitting from it right away.
At LogicPlum, we strive to make the deployment of your automated machine learning model seamless – in other words, we want to reduce the time and effort it takes for the model to move into production.
Our tools allow you to make predictions on data for machine learning in one place, eliminating the need for information to be transferred from server to server. You can also export these easily, and downloads of your data are regularly available for you to review.
The LogicPlum API endpoint makes it simple to integrate with your existing enterprise applications so that you can incorporate the insights you gain from your model as soon as possible.