In the field of machine learning, many teams face a critical challenge: developing models that, despite achieving high levels of accuracy during validation phases, fail to generate business results once implemented. This phenomenon has been evident in situations where, after achieving a 94% accuracy rate in the validation set, a recommendation engine has not been able to drive the expected business results six months after implementation.
The core of the problem lies in the fact that the traditional approach to model development focuses too much on technical perfection. This process involves data collection, model training, performance evaluation, and ultimately, implementation. Success is often measured through technical metrics such as F1 score and Area Under the Curve (AUC). However, focusing solely on the technical aspects of a model development ignores a vital element: user experience and integration of the model into their workflow.
Adopting a product-oriented approach requires a shift in perspective. Instead of starting the process with data and algorithms, it would be more beneficial to prioritize the needs and problems faced by users. This shift redefines the concept of success: it is no longer just about technical accuracy, but about how a model enhances user experience and business results. For example, a recommendation system should not only evaluate click-through rates, but also whether it truly helps users achieve their goals, avoiding filter bubbles.
Development should include an essential phase of defining problems, ensuring that technical solutions address users’ real concerns. By dedicating time initially to understand user pain points and define success criteria, the risk of creating solutions that, while technically impressive, do not solve relevant problems is mitigated.
Furthermore, when implementing a model, it is crucial to consider user acceptance, pay attention to how they interact with the results, and ensure they receive adequate training to make the most of the tool. A recent case in a development team highlighted that by shifting their focus towards a simpler model, designed to identify patterns of churn risk and provide recommended actions, customer retention increased by 15%. This demonstrates that, often, a less complex solution can have a more significant impact than an advanced model that does not provide tangible value to the business.
Success measurement should go beyond technical metrics and include user satisfaction indicators, adoption rates, and concrete business results. Establishing mechanisms for continuous feedback from the earliest stages of the process becomes crucial, allowing for improvements based on user experience.
In this way, future teams will need to adopt a more interdisciplinary approach, integrating the perspectives of data scientists, product managers, user experience designers, and experts from various business areas. This collaboration across disciplines can lead to a deeper understanding of user needs, resulting in machine learning products that truly solve problems.
This paradigm shift in model evolution is not limited to adjustments in existing processes, but requires an essential reimagining of what it means to build successful systems in the field of machine learning. By considering models as products, it is possible to design systems that generate real value for users, reaffirming that the true measure of success lies in user satisfaction, and not solely in technical results achieved.
via: MiMub in Spanish