Imagine that you created a model that runs without any errors. There were no miscalculations the first time Data Science tested it, IT could easily read and replicate it, and it was deployed within a few hours of being trained and approved. In a perfect world, this process may be a bit more realistic. However, this ease of creating a well-performing model is not always seen.
Creating a high-performing model is a process that can often be tedious and time consuming. There’s back and forth editing between Data Science and IT, as well as the many tests and errors that can come along the way to create chaos to various parts of the process.
The process of creating a standard model can be broken down into four parts:
- Define the problem
- Model creation
Each part of the process faces its own hurdles, but there are also best practices that can be implemented to ensure that each part of the process runs as smoothly as possible. Let’s take a deeper look into how this process unravels when creating a model.
Defining the problem
Before a model is created, an organization has to first identify what kind of problem they are trying to solve. After they have identified their problem and brainstormed solutions as to what model will best tackle it, they either have to:
- Collect data for their model if they do not already have it.
- Or sift through databases of storage and figure out which data is useful for this specific model.
It is often time-consuming sifting through the loads of data a company has, because not all data will be relevant to each model. This instance creates the task of useful data collection, which is often complicated from mass amounts of data, lost data, or data that does not have a purpose.
Once the Data Science team has collected enough data that they believe to be relevant, they can begin determining which resources and features will be best to put together a model. If a feature within a model is irrelevant to the structure of the model they are building, it is more efficient to not use that feature. Now that Data Scientists have everything they need, they can move onto building the model.
There are a few steps Data Scientists can take to ensure an efficient process.
- The first and most useful step in ensuring an organized process is to clean up their data. This task involves looking for features that will not be useful to the model and making sure they will not interfere.
- The second step is to label code. This step helps the user easily identify what line of code they are reading.
- The third best practice is to set up features that organize data. This step will help the user keep track of information and not lose any important data due to clutter.
Once the model has been developed, tested, and checked for any errors, it can now be sent to IT. The process of moving a model from DS to IT is often a lot of back and forth edits, however, there are a few best practices that can ease along this process.
- Getting a model from one language to another is difficult, especially if the code for that model is just a list of numbers. Having a descriptive code eases the burden of IT having to encode and then recode the data that the DS team has given them.
- The Data Science team needs to do extensive testing before their models can go to IT. Making sure that the model is well tested and free of any errors speeds up the deployment process.
- Acquiring an external engine that can efficiently encode data eases the task of IT having to encode unreadable math. At Open Data Group, our product, FastScore, uses PFA, a model interchange format to encode the math making sure it is readable to all parts of the team, and ensures that the code does not contain any errors.
The final stage of building a model, is successfully deploying it into production. The biggest obstacle that most organizations face is deploying their model within a reasonable time of being trained. Of course, there are many different factors that play into how long it will take to deploy a model such as: purpose of the model, complexity, and urgency to name a few. However, when the “normal” timeframe to deploy a model is anywhere more than a few days, if we’re being optimistic, this delay can begin to bring about many setbacks to the company.
It is crucial to the organization that the models are placed into production in a timely matter. Time is an important factor of the process, because it allows the team to be able to collect results and begin working on maintaining and improving those models to pursue more growth.
Once a model is deployed, updates can be made to increase productivity and efficiency of the model.
When deployment is not delayed, the business gains a competitive advantage by being able to utilize their models to acquire analytics. However, when deployment is slowed down, the organization has a harder time moving forward with future projects.At Open Data Group, we thrive on making sure that models are deployed fast. In fact, we have improved time to deploy by 2x creating more value for our clients. To learn more about how we achieve this click here!