Written by Oren Atia, Co-founder and Co-CEO, Seenity
“Using an outdated model is like trying to predict what happened yesterday and feeling satisfied with the result” Oren Atia, Co-founder and Co-CEO, Seenity
DevOps strives to streamline software development by bridging the gap between development and operations teams, delivering high-quality, reliable software faster and with less risk. Similarly, MLOps aims to manage the entire life cycle of machine learning projects, including data management, model creation, deployment and monitoring. By automating these processes, MLOps can help ensure that models are accurate, reliable and scalable.
However, implementing risk assessment models – which are inherently sensitive – within a company’s core business process is a complex task. Can these models be created automatically? Can they seamlessly integrate into existing workflows without introducing unnecessary risks? These are important questions that require careful consideration before embarking on an MLOps journey.
During a recent meeting, a guest inquired about MLOps and the level of support provided by the Seenity platform. Initially, our instinct was to provide a quick positive response, as Seenity does actually support MLOps. However, we recognized that this question deserves a more thorough and detailed explanation:
Working to maintain a model 24/7 is impractical, just as leaving it stagnant without updates or real-time data would be detrimental. Additionally, updating the model shouldn’t become a time-consuming and cumbersome project in itself. Ultimately, the goal should be to ensure that the model serves the company’s objectives and needs, rather than the other way around.
No one can deny the suspicion and fear surrounding the use of risk assessment AI models, compounded by the challenges associated with their integration into core systems. This is especially true in cases where the models are integrated into automated processes. The concern lies in the possibility of unforeseen variables entering the model and altering its results. As we are well aware, even a single digit error can prove to be very costly.
When it comes to automating a complex process, building the model correctly from the start is crucial. For example, in the MLOps process, real-time data must be continuously fed into the model for it to function properly. This may sound trivial, but it can be very challenging to ensure that all the information components are compatible and synchronized. Mismatched information, such as data derived from events occurring at different times, can lead to misleading results and undermine the effectiveness of the model. It’s important to pay close attention to these details during the MLOps process to avoid any real-time deception caused by unsynchronized data.
The second crucial point, when constructing a real-time risk assessment model, is to consider its maintenance beyond the initial creation. One key factor to examine is how often the model will need updating. There isn’t a universal answer, as the frequency depends on various aspects of the information used and how often these change. It’s essential to keep control over the update process and understand the impact of any modifications made to the information used in building the model. Additionally, one should know how to input updated information into the model and what circumstances warrant changing it.
Building a model involves several stages, and one of the most challenging is the information arrangement phase. This is where you create the process that will organize the elements for running data through the model. During this phase, there are many checks and decisions to make. For example, one needs to decide which information to discard, which to complete, and which information should be used to fill in averages. Information preparation tests are an inherent and vital part of this stage. They are a critical step in building the MLOps process and ensuring that your model is accurate and effective.
At this point, beyond relying solely on model metrics (MAE, MSE, R2-Score etc.) to evaluate the effectiveness of a model, we recommend implementing a simple loop that involves removing one variable from the model at a time and testing to see if its absence has a drastic effect. If there is such a variable whose removal significantly impacts the model, it may be unnecessary to build a model, and it can be implemented on its own.
In addition, and after the latter checks have been implemented, it’s crucial to establish a process for verifying the results. When building an updated model, consider the test group: is it random? Does it categorize information by period or data type? While there’s no definitive answer, it’s essential to think about these factors.
Additionally, there are two additional important stages: a rule-based stage that ensures accuracy as well as a demarcation stage that helps avoid illogical results.
We’ll discuss the monitoring phase in more detail in our next article.
Seenity will be unveiling its unique MLOps process designed specifically for insurance companies at this year’s InsureTech in London! If you’re an insurance company, you’ll be able to experience first-hand a real pipeline designed to keep your real-time models up-to-date while addressing all the challenges we’ve outlined above.
