Creating an analytic model is a time-consuming and costly project. At times, it may feel like only magic can successfully do the job of bridging the gap between IT and Data Science teams.
Although the task of sending an analytic model into model deployment may seem almost impossible, it is extremely necessary for generating insights and building a competitive advantage.
Model deployment is fairly achievable when working with one model; however, as problems become more intricate and team problems become more complex, model deployment becomes a more expensive and lengthy task.
I have dedicated the last few articles in this column and in our research to providing solutions for data scientists and IT teams, so that every model built is successfully deployed, error-free.
In this article, I will break down the key components to creating an analytic model and deploying it into production, while providing best practices to avoid most commonly known complications. These components are:
- The Problem – Every data scientist and IT team is well aware of the complex relationship that they share together. Data scientists and IT have different functions; therefore, they require different tools and libraries/packages to complete their tasks. These different resources often result in a collision when transitioning models from data scientists to IT.
- The Production – In order to avoid having to change the production environment, it is crucial to have access to all the required dependencies. This part is a lot easier said than done. The production process is more often than not a cycle of back and forth transitioning from data scientists to IT. The solution that we find the most effective is through implementing a Docker ecosystem.
“Container portability means that models can be validated early, by the data science team, reducing the back-and-forth between data science and IT.”- Stu Bailey, CTO, Open Data Group
- The Roles – Creating an analytic model and deploying it into production is a collaborative project. Knowing how to break down and assign tasks early on in the process will save your teams headache along the way.
- The Testing – Before deploying a model into production, it must be tested over and over again. By focusing validation on smaller components, troubleshooting becomes easier.
- The Monitoring – When it comes to monitoring, data scientists and IT have their own, separate key issues to focus on. To increase efficiency, each team should focus on their own responsibilities and not the tasks of the other team.
- The Handoff – Finally, the part where both teams come together to create their final product. The latest, innovative solution that I have been researching throughout this column, is the break away from a traditional monolithic strategy, into a more flexible, easy to implement, and best practices strategy.
Make sure to continue following us along our series of posts to discover more key best practices to deploying more models into production, faster!