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An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for... show more

Santiago

449,456 subscribers

36,607 просмотров • 1 год назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Santiago

Santiago1 год назад

You can join at

Фото профиля Jeffrey 🐬 confident-ai.com

Jeffrey 🐬 confident-ai.com1 год назад

Evaluating a model sounds just like what we’re doing :)) Built an open-source LLM evaluating framework here 😇

Фото профиля Mohammed Lubbad, PhD

Mohammed Lubbad, PhD1 год назад

That sounds like a comprehensive project. Open-source tools offer great flexibility for such endeavors. What cloud platforms are you considering?

Фото профиля Santiago

Santiago1 год назад

You’ll be able to deploy anywhere. We’ll have sessions on AWS and Azure.

Фото профиля Pau Labarta Bajo

Pau Labarta Bajo1 год назад

I've heard there is another great live course on real world ML... around a mile away 😉

Фото профиля Santiago

Santiago1 год назад

ha ha!

Фото профиля Jeff Amazon

Jeff Amazon1 год назад

I love that !!!

Фото профиля CTS Tech

CTS Tech1 год назад

He speaks the truth!! 🤑🤑

Фото профиля NebutronX 🌩️ 🇹🇼 ネビュトロン

NebutronX 🌩️ 🇹🇼 ネビュトロン1 год назад

I'm working full-time (data engineer) and working on my Master's - but I'm still thinking about signing up!

Фото профиля GillyDaScalper

GillyDaScalper1 год назад

Do you have to have experience in coding or can I learn with some idea of coding?

Фото профиля Santiago

Santiago1 год назад

If you haven't written code before, you shouldn't join.

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21,974 просмотров • 1 год назад

Data preparation! It's crucial for machine learning, and we all hate it. Tools and techniques to reduce this burden? A quick summary of 10 years of R&D on this, from cheap tricks to LLMs and graph neural networks 1/9

Data preparation! It's crucial for machine learning, and we all hate it. Tools and techniques to reduce this burden? A quick summary of 10 years of R&D on this, from cheap tricks to LLMs and graph neural networks 1/9

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