48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

Data science is about much more than jupyter notebooks, because data science problems are about more than machine learning.

What data should I collect? How good does my model need to be to be “good enough” to solve my problem? What form should my project take for it to be useful? Should it be a dashboard, a live app, or something else entirely? How do I deploy it? How do I make sure something awful and unexpected doesn’t happen when it’s deployed in production?

None of these questions can be answered by importing sklearn and pandas and hacking away in a jupyter notebook. Data science problems take a unique combination of business savvy and software engineering know-how, and that’s why Emmanuel Ameisen wrote a book called Building Machine Learning Powered Applications: Going from Idea to Product. Emmanuel is a machine learning engineer at Stripe, and formerly worked as Head of AI at Insight Data Science, where he oversaw the development of dozens of machine learning products.

Our conversation was focused on the missing links in most online data science education: business instinct, data exploration, model evaluation and deployment.

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Episoder(130)

130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

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129. Amber Teng - Building apps with a new generation of language models

129. Amber Teng - Building apps with a new generation of language models

It’s no secret that a new generation of powerful and highly scaled language models is taking the world by storm. Companies like OpenAI, AI21Labs, and Cohere have built models so versatile that they’re...

5 Okt 202251min

128. David Hirko - AI observability and data as a cybersecurity weakness

128. David Hirko - AI observability and data as a cybersecurity weakness

Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape your investment decisions and determine your outcome...

28 Sep 202249min

127. Matthew Stewart - The emerging world of ML sensors

127. Matthew Stewart - The emerging world of ML sensors

Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processing pow...

21 Sep 202241min

126. JR King - Does the brain run on deep learning?

126. JR King - Does the brain run on deep learning?

Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial neural network, as you probably already know if you...

14 Sep 202255min

125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

It’s no secret that the US and China are geopolitical rivals. And it’s also no secret that that rivalry extends into AI — an area both countries consider to be strategically critical. But in a context...

7 Sep 202248min

124. Alex Watson - Synthetic data could change everything

124. Alex Watson - Synthetic data could change everything

There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website’s name suggests, the...

18 Mai 202251min

123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning entry i...

12 Mai 202254min

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