Expectations Debt: Developing a Machine Learning Model From Start to Finish

Expectations Debt: Developing a Machine Learning Model From Start to Finish

In today's fast-paced and competitive world, expectations play a significant role in our personal and professional lives. We set expectations for ourselves, our relationships, and even our work. However, what happens when these expectations become like a debt that needs to be repaid before we can truly find joy in what we are doing? This phenomenon, known as "expectations debt," can have a profound impact on our well-being and satisfaction.

When a company is on a winning streak, everyone is happy. Employees are rewarded with promotions, financial gains, and a sense of contributing to something bigger than themselves. It feels great to be part of a winning team, but there is a hidden cost to these successes. The expectations that come with winning can create a debt that must be repaid in the future.

Imagine a scenario where your investment portfolio surges during a market bubble. You feel like a financial genius, riding the wave of success. However, when the bubble bursts, reality catches up, and you are left with a debt to repay. The same can happen in a professional setting. You negotiate a salary that exceeds your actual abilities, only to realize that you are now expected to perform at a level that is beyond your capabilities. The joy and satisfaction you initially felt are overshadowed by the pressure to meet these inflated expectations.

This concept of expectations debt can also be observed in the process of developing a machine learning model from start to finish. The journey of creating a successful model involves several stages, each with its own set of expectations and potential debts to be repaid.

The first stage is ideation. Here, the key problem to be solved is identified, and potential data inputs are considered for the solution. It is crucial to align on the problem and gather the necessary data to move forward. However, this initial step can also create expectations. The more ambitious the problem, the higher the expectations for a groundbreaking solution. It is important to manage these expectations and focus on the feasibility of the project.

Once the problem and data inputs are defined, the next stage is data preparation. This involves collecting and formatting the data in a way that can be easily digested and learned from by the model. Data preparation is a critical step in the process, but it can also be time-consuming and tedious. It is important to set realistic expectations for the time and effort required to prepare the data properly.

After the data is prepared, the prototyping and testing phase begins. Models are built and tested to solve the identified problem, and iterations are made until satisfactory results are achieved. This stage can be both exciting and challenging. There is a sense of progress and potential success, but there are also expectations to deliver a high-performing model. It is important to balance the pressure to meet expectations with the need for experimentation and iteration.

Once a model is developed and tested, the next step is productization. This involves stabilizing and scaling the model, as well as the data collection and processing, to produce useful outputs in a production environment. This stage requires a deep understanding of the key factors that determine the model's quality. Business and product people play a crucial role in ensuring that the model meets the expectations of the end-users. It is important to involve these stakeholders throughout the process to avoid falling into the trap of expectations debt.

In the world of machine learning, there is a lot of art in the science. While the process may be scalable as a whole, there can be small but important populations that the model doesn't work well for. It is crucial to check for outliers and ensure that the model is reliable across different scenarios. Setting up an on-demand way to outsource tasks that arise can be a practical approach to address these challenges.

Before concluding, let's explore three actionable pieces of advice to navigate the world of expectations debt when developing a machine learning model:

• 1. Manage expectations from the start: Be realistic about the problem you are trying to solve and the resources available. Set clear expectations with stakeholders and communicate the potential risks and challenges.

• 2. Embrace iteration and experimentation: Developing a high-performing model takes time and effort. Don't be afraid to experiment and iterate. Learn from failures and use them as opportunities for growth.

• 3. Involve business and product people: The success of a machine learning model is not just about the technical aspects. Business and product people have valuable insights into the end-users' expectations and requirements. Involve them throughout the process to ensure that the model meets the desired outcomes.

In conclusion, expectations debt is a phenomenon that can impact various aspects of our lives, including the development of machine learning models. By managing expectations, embracing iteration, and involving key stakeholders, we can navigate the challenges of expectations debt and create models that meet the desired outcomes. Remember, it's not just about winning in the short term; it's about building sustainable success in the long run.