Balancing Theoretical Knowledge with Hands-on Experience - ML 154

Balancing Theoretical Knowledge with Hands-on Experience - ML 154

Michael Berk and Ben Wilson from Databricks are joined by Brooke Wenig, who has a fascinating background in distributed machine learning. Today’s conversation dives deep into the intersection of AI, environmental science, and career transitions. They explore how individuals like Michael transformed their careers from environmental science to AI, leveraging existing expertise in innovative ways. Ben shares insights on leaping from non-technical roles to data science by embracing automation with Python and machine learning.
We tackle the critical shift in roles, the balance between education and hands-on experience, and the growing disparity between academia and industry. Brooke brings valuable perspectives on project scoping, from aligning success criteria to ensuring real-world value. The discussion revolves around augmenting existing roles with AI, common pitfalls, and transitioning proofs of concept to production.
They also explore the practical applications of language models, the debate over open versus closed source models, and the future of AI in various industries. With a focus on collaboration, the traits of top data scientists, and the implications of integrating AI into non-tech fields, this episode is packed with insights and tips for anyone looking to navigate the exciting world of AI and machine learning.
Join them as they delve into these topics and more, discussing the evolving landscape of AI and how it's shaping careers and industries alike.

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Episoder(209)

Confidence as Data Scientist - ML 130

Confidence as Data Scientist - ML 130

In today's episode, we chat with Adam Ross Nelson - a data scientist, career mentor, and writer. Our primary focus is understanding when you've achieved "good enough" skills. Additionally, we explore ...

19 Okt 20231h 1min

A Case Study: Recommendation Engines - ML 129

A Case Study: Recommendation Engines - ML 129

In today's episode, we delve into a popular topic at Databricks: building recommendation engines. We'll guide you on how to plan such projects, measure your model's success, suggest effective algorith...

5 Okt 202350min

Maximizing Efficiency in ML Project Development - ML 128

Maximizing Efficiency in ML Project Development - ML 128

Ben and Michael delve into the world of data science and software development. They discuss the importance of setting standards and documentation in project development, the struggle of reviewing and ...

21 Sep 202347min

AI that Make You Better - ML 127

AI that Make You Better - ML 127

In today's episode we speak with Paul Allen, the creator of Ancestry.com. Currently, he is CEO of Soar.com, a platform that leverages AI to make you live a more fulfilled and effective life. Expect to...

14 Sep 20231h 15min

Challenges for LLM Implementation - ML 126

Challenges for LLM Implementation - ML 126

In today's episode, we speak with Anand Das, the CTO and co-founder of bito.ai, an LLM-powered code assistant. Expect to learn about managing LLM context, keeping LLMs up-to-date, common user pitfalls...

7 Sep 20231h 16min

ML in the Cannabis Industry - ML 125

ML in the Cannabis Industry - ML 125

In today's episode, we speak with Shivek Sachdev, product owner at a seed-to-sale Cannabis company. Expect to learn about disrupting new industries, applying LLMs to daily work, and much more!Sponsors...

24 Aug 202353min

How AI Impacts Society - ML 124

How AI Impacts Society - ML 124

In today's episode we speak to Eric Daimler, a White House Presidential Innovation Fellow, professor at Carnegie Mellon, and the current CEO of Conexus. Expect to learn about how AI is impacting the m...

17 Aug 20231h

LLMs on Azure - ML 123

LLMs on Azure - ML 123

In today's episode, we speak with Jeff Procise, founder of Wintellect, an Azure-focused software consulting company. Expect to learn about your next LLM MVP on Azure, the societal impact of AI, the bi...

3 Aug 20231h 14min

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