How to Implement Machine Learning Correctly

5 Common Mistakes When Applying Machine Learning

Nowadays, companies manage significantly larger volumes of data than in the past. As a result, technologies such as Artificial Intelligence (AI) and Machine Learning are becoming increasingly important in all business sectors. According to an IDC study, the segment of Artificial Intelligence related to Machine Learning is estimated to generate revenues exceeding 500 billion dollars by the end of 2023.

How to Implement Machine Learning Correctly

In a constantly evolving environment, where new technologies are playing an increasingly important role in the business world, it is crucial to avoid a series of common mistakes when implementing these technologies. In this regard, Knowmad Mood has identified some of the most frequent mistakes to consider for successfully implementing a Machine Learning strategy:

Infrastructure, Data Ingestion, and Preprocessing

Before starting to implement Machine Learning solutions in the company, it is necessary to have a well-organized and standardized data storage infrastructure. This allows new data to be automatically incorporated into the training dataset with minimal human interaction.?

The first step is to establish a solid data architecture and perform adequate preprocessing to ensure the data can be correctly fed into the algorithms. This involves handling factors such as outliers, encoding categorical variables, managing missing data, or normalizing data. It is crucial to eliminate these errors to avoid training models with data that does not reflect reality, thus preventing errors in the model.

Data Volume and Variable Selection

Machine Learning generally requires large volumes of data to train and validate models. However, it is not uncommon for companies to lack sufficient data, or the data may not be representative or balanced.?

It is important to note that not all available variables are essential for inferring the target variable. Including a large number of variables does not guarantee better model performance. Therefore, it is crucial to perform good variable selection using various statistical and data analysis methods.

Model Consideration and Metric Interpretation

It is not only essential to choose the right data for the model to infer correctly, but also to select the appropriate algorithm to solve the intended problem. There are many different algorithms, each with its own strengths and weaknesses, and in each case, the most suitable one for the company's objective must be chosen.

Additionally, it is important to correctly evaluate the model's performance by selecting the appropriate evaluation methodology and paying attention to relevant metrics according to the type of problem to be solved. Comparing different experiments minimizes the risk of overfitting the model to the training data and ensures good performance in production.

Model Maintenance

The dataset used to build models can change over time, and the characteristics of its variables may be modified. Therefore, Machine Learning models require monitoring and maintenance once deployed. This allows identifying when the model is degrading and needs retraining through the defined metrics.

Collaboration During the Process

Effective collaboration between the different teams involved in implementing any Machine Learning-based application is essential. These teams may include data scientists, engineers, domain experts, or IT personnel. Lack of collaboration and communication between these professional teams can hinder the proper development of a Machine Learning-based solution.

Conclusion

Implementing Machine Learning in a company can offer enormous benefits but also presents several challenges that must be carefully addressed. From infrastructure and data preprocessing to variable and algorithm selection, correct metric interpretation, continuous maintenance, and team collaboration, each step is crucial for success.?

Avoiding common mistakes and following best practices will allow companies to fully leverage the potential of Machine Learning, leading to greater efficiency and competitiveness in the market.