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

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

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.


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The Disruptive Power of Artificial Intelligence - ML 100

The Disruptive Power of Artificial Intelligence - ML 100

Have you ever wondered about the most promising industries in Machine Learning? Today we will learn from Avi Goldfarb, the chair of AI at the University of Toronto, about...The most promising AI indus...

19 Jan 20231h

A History Of ML And How Low Code Tooling Accelerates Solution Development - ML 099

A History Of ML And How Low Code Tooling Accelerates Solution Development - ML 099

In this episode, Ben talks with Rosaria Silipo, a Software Engineer and Developer Relations advocate at Knime. They discuss the benefits of low-code ML, delve into the history of ML development work a...

6 Jan 20231h 12min

Moving from Dev Notebooks to Production Code - ML 098

Moving from Dev Notebooks to Production Code - ML 098

In this week's episode we meet with Mike Arov, committer to the MLOps tool framework lineapy. From the benefits of notebooks as development tooling for Data Science work to the complex refactoring nee...

22 Dec 20221h 11min

How to Edit and Contribute to Existing Code Base - ML 097

How to Edit and Contribute to Existing Code Base - ML 097

Let's be honest. We've all copied and pasted code from the internet. There are many great code sources and in this episode, Ben and Michael discuss how to leverage existing code. They explain how to u...

15 Dec 202254min

MLflow 2.0 And How Large-Scale Projects Are Managed In The Open Source - ML 096

MLflow 2.0 And How Large-Scale Projects Are Managed In The Open Source - ML 096

Corey Zumar talks about the new release of MLflow, 2.0, and what the new major features that are included in the release. Bilal and Corey then discuss managing feature implementation priorities, and s...

1 Dec 202249min

Should you Context Switch when Writing Code? - ML 095

Should you Context Switch when Writing Code? - ML 095

Do you multitask? If so, you'll want to check out this episode. We'll cover...The difference between good and bad context switchingSome personal stories about context switching on projectsPractical ti...

24 Nov 202231min

How To Recession Proof Your Job - BONUS

How To Recession Proof Your Job - BONUS

Get the Black Friday/Cyber Monday "Focus Blocks Bundle" Deal Coupon Code: "THRIVE" for a GIANT discount   Are you looking at all the layoffs and uncertainty going on and wondering if your company is t...

24 Nov 20221h 12min

Important Questions To Ask When Scoping ML Projects - ML 094

Important Questions To Ask When Scoping ML Projects - ML 094

Have you ever wondered how to prioritize your ML projects? Today we will talk about...The difference between applied ML and ML frameworksHow to effectively scope projects via Heilmeier's Catechism Spo...

17 Nov 202247min

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