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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Optimizers in Machine Learning, Featuring Maciej Balawejder - ML 077

Optimizers in Machine Learning, Featuring Maciej Balawejder - ML 077

Ben and Michael interview Maciej Balawejder, a mechanical engineering student passionate about AI, ML, and robotics.  As an active contributor on Medium.com, Maciej has already made significant contri...

23 Kesä 202248min

Part 2: Exploratory Data Analysis (EDA) Next Steps - ML 076

Part 2: Exploratory Data Analysis (EDA) Next Steps - ML 076

After ensuring your data has surpassed the hyper parameter tuning phase, what is the next step in your EDA protocol? Today on the show, Ben and Michael continue the discussion on EDA methodology withi...

16 Kesä 202250min

Exploratory Data Analysis (EDA) in Machine Learning - ML 075

Exploratory Data Analysis (EDA) in Machine Learning - ML 075

EDA is primarily used in machine learning to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationsh...

9 Kesä 202249min

Apache Spark (Pt. 2): MLlib - ML 074

Apache Spark (Pt. 2): MLlib - ML 074

MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best M...

2 Kesä 202252min

Apache Spark Integration and Platform Execution for ML - ML 073

Apache Spark Integration and Platform Execution for ML - ML 073

Apache Spark is a lightning-fast unified analytics engine for large-scale data processing and machine learning. In this episode, Ben and Michael unpack Spark by ping-ponging questions and answers, su...

26 Touko 202236min

Two Case Studies: Production ML infrastructure and Recommendation Engines - ML 072

Two Case Studies: Production ML infrastructure and Recommendation Engines - ML 072

Ben and Michael walk through two different cases studies relative to production ML infrastructure and recommendation engines. The first is about a free on-line tutoring service for underserved commun...

18 Touko 202253min

Using AI and ML to Help Humans, Not Replace Them - ML 071

Using AI and ML to Help Humans, Not Replace Them - ML 071

Ben interviews Michael Griffiths, Director of Data Science at ASAPP, a company leveraging AI and ML to augment and automate human work, improve operational efficiencies and customer experiences, and u...

12 Touko 202243min

AutoML Discovery and Approach - ML 070

AutoML Discovery and Approach - ML 070

AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approac...

4 Touko 202243min

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