Deploying analytic models into production can often prove to be a difficult and tedious process. In an ideal world, data scientists create a model, they hand it off to IT, and IT puts that model into the production environment. Seems simple enough, right? However, as many data science and IT teams know, there are many complications that can turn this process from a simple one, to a highly complex back and forth.
A lot of time is spent on creating analytic models in organizations. These models allow companies to generate insights and additional value from their data. Companies get the most value out of their machine learning models when they are deployed quickly. If deploying machine learning models to production is a long, complex process, it’s possible that insights from the model will be out of date by the time they are generated. In order to get the most value from data, the deployment process should be quick and simple.
To simplify this process of deploying models into production, let’s look at one important cause of complications along the way. When creating models, data scientists often use many tools and leverage the latest open source packages to make an efficient machine learning model. Although this model would likely give the organization valuable insights, it is possible that IT cannot deploy that model into production. The model must have all of the required dependencies to go into the production environment approved with IT. If it doesn’t, the model will get sent back to be written in a different language supported by the production environment, or data scientists will be told they must create a model that doesn’t have certain libraries. This puts the data scientist back to square one, resulting in wasted time and delaying the possible insights generated from the model.
So, how do we fix this relationship between data science and IT, and deploy our analytic models into production more efficiently?
- Clearly define the roles of each department
Data science and IT both have important, but different roles in the deployment process. Each role should be well defined, and each department should know the exact parts of the process that they need to focus on. This will get things done more quickly, and allow the departments to collaborate more effectively.
- Establish a check list for moving a model into production
As previously mentioned, IT and data science teams should know what they need to do to put the model into production. The actions and requirements for production should be documented, and the tooling should be provided to prove that a model is ready for promotion to production. Some of these actions will be process driven (is the code in our Git rep), and some will be action driven (did you test the model with the right data set).
- Use an agnostic deployment engine to solve the issue of differing coding languages
The most time-consuming part of the deployment process is the handoff from data science to IT, primarily because the two departments typically work with different coding languages. This problem can be solved by using an agnostic engine that executes all model and code types. This avoids having models sent back to data science because they are unusable in production, and will allow models to be deployed into production on the first try.
Implementing the right deployment technology allows IT and data science teams to collaborate more efficiently. Here at Open Data Group, our analytic deployment technology, FastScore, saves companies time and money by easing the transition from the creation environment to production environment. FastScore can deploy models of any language, no matter where they were created. To learn more about how FastScore can benefit your organization, check out our product page here.