Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178 cover art

Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

Listen for free

View show details

About this listen

 In this episode, Ben and Michael explore burnout, particularly in machine learning and data science. They highlight that burnout stems from exhaustion, cynicism, and inefficiency and can be caused by repetitive tasks, overwhelming workloads, or being in the wrong role. They also tackle strategies to combat burnout, including collaborating with others, mentoring, shifting focus between tasks, and hiring more people to distribute the workload. A key takeaway is the importance of knowledge sharing and not hoarding tasks for job security, as this can lead to burnout and inefficiency. They also discuss managing burnout and its components, particularly exhaustion, cynicism, and inefficiency, through personal experiences. Finally, they talk about how burnout can lead to inefficiency and physical manifestations, like a lack of motivation to engage in activities outside of work.


Socials
  • LinkedIn: Ben Wilson
  • LinkedIn: Michael Berk


Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
No reviews yet