The success of machine learning projects depends primarily on the three initial phases of implementation: understanding the problem, defining objectives, and preparing the data. These first three steps consume approximately 80% of the total project time.
After defining the business requirements, the process advances to the modeling phase and application of the most appropriate machine learning techniques.
This sequence of steps is inspired by the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology – a set of best practices that guides the data mining process.
To understand the journey of implementing a machine learning model in a wide range of businesses, we spoke with Lucas Abreu, a partner at dhauz. Read on.
First step: defining the objective
The process begins with the ideation stage, where the problem to be solved and its feasibility for development and application in the company’s day-to-day operations are detailed. “In this phase, we define the project objectives and design the solution to assess whether it truly solves the business problem,” explains Lucas.
Furthermore, it’s important to align the objective with feasibility. This is the time to ask the right questions: What do we want to predict? Is there data available? How will we measure the results?
Data collection and preparation
Data preparation is the stage where data is cleaned and structured. This is a meticulous process, where data must be analyzed and processed carefully to avoid unsuccessful results.
At this stage, it is also time to detect and correct irregularities, such as incomplete or inconsistent data, before implementing the machine learning model.
The strategic choice of the model
With the data ready, it’s time to choose the right machine learning algorithm for your project’s objective. There are several algorithms available for different types of problems and objectives.
Lucas explains that dhauz applies a progressive approach to its projects, moving from the simplest to the most advanced techniques, taking into account performance, applicability, and execution speed. “It’s important to choose the right evaluation metric and analyze model errors to meet business objectives,” he emphasizes.
MLOps
The final stage involves model operation , also known as MLOps. During this phase, the developed algorithm is applied to the business’s day-to-day operations through integrations with existing applications that utilize the model’s output, for example.
An important step beyond deploying the algorithm is evaluating the model to compare the generated predictions with actual values and develop metrics to measure its effectiveness. Lucas emphasizes the importance of the MLOps process for good governance and monitoring results.
As we can see, understanding how machine learning works involves mastering modeling techniques, but primarily understanding the problem to ensure that the model developed meets business objectives.
With the right guidance and a structured approach, machine learning models can become powerful allies in solving a variety of business challenges.
