Navigating Expertise Gaps - ML 172

Navigating Expertise Gaps - ML 172

In today's episode, Ben and Michael discuss how to handle situations involving individuals lacking expertise in machine learning projects. They explore scenarios where a team lacks expertise, considering approaches for consultants or team members. They discuss various personality types encountered in such situations, including those overly suspicious or resistant to change. Moreover, they discuss how to convince a boss that a proposed project is a bad idea, suggesting a structured approach with clear estimates, risk assessment, and alternative solutions. They emphasize the importance of honesty, transparency, and presenting options with clear pros and cons.
The discussion then returns to the Gen AI time-series case study, suggesting a presentation of multiple options, including established algorithms and the Gen AI approach, to facilitate a data-driven decision.
Finally, the episode addresses the scenario of a teammate being untrained about a system they built, suggesting a combination of direct but constructive feedback and a collaborative approach to identify the root cause of the issue.


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Why Authenticity Beats Algorithms: The New Rules of Digital Marketing - ML 185

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In this episode, we dive deep into the evolving landscape of digital marketing and brand storytelling. We explore how the intersection of authenticity, community, and technology is reshaping how brand...

4 Huhti 202555min

Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

Welcome back to another episode of Adventures in Machine Learning, where hosts Michael Berk and Ben Wilson delve into the intricate process of implementing model serving solutions. In this episode, th...

13 Helmi 202551min

Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

Welcome to another insightful episode of Top End Devs, where we delve into the fascinating world of machine learning and data science. In this episode, host Charles Max Wood is joined by special guest...

24 Tammi 202555min

Cows, Camels, and the Human Brain - ML 182

Cows, Camels, and the Human Brain - ML 182

What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researche...

9 Tammi 202542min

A/B Testing with ML ft. Michael Berk - ML 181

A/B Testing with ML ft. Michael Berk - ML 181

Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setu...

2 Tammi 202545min

Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

In today's episode, we dive into the critical decision-making process of building versus buying technology solutions, especially when it comes to agentic logic-based frameworks. With the industry stil...

26 Joulu 202431min

Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS a...

19 Joulu 202454min

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 ...

12 Joulu 20241h 12min

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