7 Common Pitfalls to Avoid in Machine Learning Projects

You have spent months or quarters working on a machine learning project, having countless discussions with your manager, stakeholders, and others involved. But suddenly, your project fails! It can be a frustrating experience. So, how can you anticipate risks and avoid this from happening?

Paying attention to the following seven common pitfalls can help ensure your machine learning project succeeds with a much higher chance:

The first step of an ML project is problem framing. Understanding the business objective is crucial. It is essential to define the problem clearly and precisely, including the business objectives and the success criteria. Otherwise, the project can end up focusing on the wrong problem or delivering irrelevant results to the stakeholders.

2. Data issues:

The quality of the data used for training the model is critical for the project’s success.

# 1: Data quality issues

Wrong data source, unreliable data source, unstable data, poor quality of data, incomplete data, insufficient data, data inconsistency between offline experimentation and online production.

# 2: Data Labels

Inaccurately labeled data, inconsistency among human annotators, low volume of positive samples will cause poor model performance.

# 3: Not checking for data leakage

All of the above data issues can lead to biased or unreliable models that do not generalize well to new data.

3. Overfitting

Overfitting occurs when the model is too complex or the data is too noisy, leading the model to memorize the training data instead of generalizing to new data. This can result in poor performance on new data and is a common pitfall in machine learning projects. A good practice is to always have a simple benchmark model (rule) to compare against.

4. Model interpretability: Many machine learning models are complex and opaque, making it difficult to understand how they arrive at their predictions. This can be a risk in situations where the model’s decision-making process needs to be explainable or justifiable.

5. Lack of domain expertise: Understanding the domain and the problem context is critical for developing effective machine learning solutions. Without domain expertise, the model may not capture the nuances and complexities of the problem, leading to poor performance.

6. Lack of stakeholder engagement: Machine learning projects often involve multiple stakeholders, including business owners, subject matter experts, and end-users. Failure to engage with stakeholders and understand their needs and requirements can lead to solutions that do not meet their needs or are difficult to implement. For example: fail to include essential signals that stakeholders want to be used in the model, outcome variable definition is not what the stakeholders want, opportunity volume too big, too small.

7. Bias and discrimination: Machine learning models can perpetuate bias and discrimination if they are trained on biased data or incorporate biased assumptions. This can have serious ethical and legal implications, and it is important to take steps to identify and mitigate bias in machine learning projects.

To mitigate these risks and avoid these pitfalls, it is important to approach machine learning projects with a rigorous and structured process, including problem definition, data preparation and exploration, model development and testing, and deployment and monitoring. Additionally, it is important to collaborate closely with domain experts, engage with stakeholders, and adopt a responsible and ethical approach to machine learning.

What are the pitfalls you observed at work with machine learning projects?

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