  Machine learning is now used in many applications thanks to its ability to
predict, generate, or discover patterns from large quantities of data. However,
the process of collecting and transforming data for practical use is intricate.
Even in today's digital era, where substantial data is generated daily, it is
uncommon for it to be readily usable; most often, it necessitates meticulous
manual data preparation. The haste in developing new models can frequently
result in various shortcomings, potentially posing risks when deployed in
real-world scenarios (eg social discrimination, critical failures), leading to
the failure or substantial escalation of costs in AI-based projects. This
chapter provides a comprehensive overview of established methodological tools,
enriched by our practical experience, in the development of datasets for
machine learning. Initially, we develop the tasks involved in dataset
development and offer insights into their effective management (including
requirements, design, implementation, evaluation, distribution, and
maintenance). Then, we provide more details about the implementation process
which includes data collection, transformation, and quality evaluation.
Finally, we address practical considerations regarding dataset distribution and
maintenance.
