Managing large-scale data science and machine learning projects is challenging because they differ significantly from software engineering. Since our goal is to discover patterns in data without explicitly coding them, there is greater uncertainty, which can lead to various problems, such as:
- High stakeholder expectations may not be met
- Projects may take longer than initially anticipated.
Uncertainty arising from machine learning projects is one of the main causes of setbacks. And when it comes to large-scale projects (which typically come with higher expectations), these setbacks can be amplified and have catastrophic consequences for organizations and teams.
This blog post was born after my experience managing large-scale data science projects with ai/” rel=”noopener ugc nofollow” target=”_blank”>Bold factsI have had the opportunity to manage a variety of projects across multiple industries, collaborating with talented teams that have contributed to my growth and success along the way; it is thanks to them that I was able to compile these tips and put them into writing.
Below are some basic principles that have guided me to achieve success in many of my projects. I hope you find them useful…
