Software engineers are always looking for new, fast ways to update their models once deployed into production. Whether this involves running a new system, creating a new code, or utilizing a new software, programmers need to find fast and accurate ways to update their models that are already in production in order to maintain efficiency.
Rapid iteration is crucial in speeding this process up as much as possible. Rapid iteration places updates to existing well-performing models into production in a timely manner. This process helps to ensure that there are no models laying around not being used because of needed updates and changes.
Rapid iteration benefits the implementation process as a whole, but there are important benefits provided to Information Technology, Data Science, and the Business team that we should bring attention to. Here are a few significant factors of rapid iteration that create a competitive advantage for the team.
Utilization of Models
A Data Scientist’s process of building a well-performing model is spent by: prototyping, tweaking, testing, re-prototyping, more testing, converging on a solution and finally creating a model that performs well. This process can take weeks, months, and sometimes longer to complete.
However, once a model has been approved, it needs to be deployed into production quickly to create any benefits to the company. Rapid iteration ensures that models are being placed into production as soon as possible, and not going unused. Once these models have been placed into production, updates can be made rapidly when necessary, increasing use time of the model.
Increased Movement to Production
After Data Scientists have created a model that they believe to be sufficient and high performing, IT must approve it. If models are not approved, they are further being delayed going into the production environment, which results in IT having less time to focus on other tasks. This delay creates a burden on IT, as they now have an excess number of models that they must rapidly deploy into production.
The process of moving models into production without any delays, swiftly reduces the extra burden IT faces of having to rush multiple models into production at once.
The longer it takes for the creation and deployment of a model, the more money a company is spending waiting on a process that can be shortened through a more efficient iteration process. Every minute this process is prolonged, and a model is stuck waiting to be deployed into production, businesses are losing money. Between business loss, wasting resources, increased labor and wasted time, it’s worth implementing a process that factors in rapid iteration.
As a result, rapid iteration brings models into production as soon as they are ready to be used. Therefore, the business team no longer has to keeping investing money on projects that are ready to run but not being utilized, and can begin bringing in money through the use of those models.
Open Data Group is leading the innovation of rapid iteration with our product, FastScore. FastScore increases time to iterate by 2x through its provided flexibility and ease of usage.
FastScore is a microservice that relies on Docker containers. These components do not create any restrictions on complex model usage, and have the flexibility of working with multiple programming languages. This feature greatly increases the Data Science teams speed of production by accommodating to their needs.
By increasing speed of production, IT is able to acquire these models and gain an immediate migration of analytics to commodity hardware and other architectures by an increased rate. This faster process allows for faster results.
To learn more about FastScore and rapid iteration, click here!