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Release of Pragun Bhutani's talk on analytics engineering at Blinkit and Intercom This update is for participants only
The meet-up will showcase examples of how tools are used to build your data stack.
The meet-up will have three talks and a hands-on workshop (post-lunch).
This meet-up is for practitioners, including AI/ML/data engineers, software engineers, data scientists, and product managers.
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Submitted Oct 14, 2023
A common dilemma for teams or executives is - to “build internally” or “buy from outside”. In software, both strategies have their own pros n cons. However, when it comes to AI, while the reasoning is much more convoluted, the answer is very simple - always Buy before Build!
In this talk, I will argue that when it comes to AI it is not a question of this or that. Instead Buy before Build in AI should be the default answer for most organizations.
I will further argue that this is a great strategy, even if you have a great AI team internally! Contrary to most AI leaders’ opinions who resist “buying” AI solutions from outside and prefer to “build” since they not only see “external solution” as a threat to their charter but also “buying from outside” as a question mark on their n their AI team’s ability.
I will show why this is a very wrong thinking and how a smart AI leader can leverage the “external AI solution” to eventually “build a great internal AI solution” that is not only technically superior but also economically viable.
The only case where this does not make sense is - if you are an “AI first” organization i.e. organization where the “AI product” in question is the primary offering itself.
Takeaways
Thanks
Anuj
https://www.linkedin.com/in/anujgupta-82/
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