One of the biggest challenges a project manager faces when managing a machine learning (ML) project is dealing with data-related issues. The importance of good quality, well-represented and well-distributed data cannot be overstated. In this article, we delve into the complex world of managing ML projects that could be plagued with data issues. From data quality, data shortage and missing data issues, we explore these common pitfalls and offer strategies to overcome them, ensuring your ML projects stay on track and deliver meaningful results.

First and foremost, it’s essential to acknowledge the pivotal role data plays in ML projects. The quality, distribution and integrity aspects of data are the bedrocks upon which the success of ML projects stands. Garbage in, garbage out, and in the world of machine learning, this statement couldn’t be more true. Data directly affects the performance of the models. If the data for a ML project is problematic, the performance of the models would be subpar. This would affect the outcome of the business value that comes from the models’ performance.

An effective way to mitigate the project risks resulting from data issues in a ML project is to ensure that there is a working data pipeline created early by the project team and made available throughout the project stages to process the many different versions of data, i.e. starting with initial versions which inevitably contain data issues, through intermediate improved versions which progressively address the data issues to the final version which is used to train the champion model.

A well-designed data pipeline enables repeatable seamless movement of data from the data sources to the models. A well-structured data pipeline ensures data quality, data consistency and data accessibility in each repeat cycle. Without a data pipeline, the project team will have to invest significantly more time and effort to deal with the data issues.

Below are the typical components that make up a data pipeline:

• Data Ingestion: Data is extracted from the data sources into the pipeline.
• Data Cleaning: Data is cleaned, missing data values either imputed or dropped.
• Data Processing: Data is preprocessed and transformed to become ready for model training and evaluation.

Data issues may appear as formidable adversaries to a ML project, but with a well-thought-out strategy, active collaboration among the project team and the right tools, the associated project risks can be mitigated and the challenges can be overcomed. As a project manager, your role is not just about overseeing the project timeline and budget but also about orchestrating the harmonious convergence of data, technology, and human expertise. By proactively addressing data issues, you pave the way for success in your machine learning endeavors, ensuring that you can harness the true potential of data-driven insights and innovations.