Lesson series

Solution Architecture in AI

Once you have sliced your AI problem, you combine the solutions for the parts into an architecture which brings them together to solve your original problem. This module shows you how.

The Hero Methods Course (Free)



Starts at

Your convenience



Content Duration

120 minutes



Architecture in AI is Experimental 

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Solution Architecture is hard. It's technical - you'll need to really know the domain & the tech. It's leadership - you'll need to shepherd doers & stakeholders through the build.
Add AI to the mix, and it gets harder. AI is experimental.  You won't know whether it works until you actually pilot it. 

It's about building a sense for which methods and configurations work well for your problems. It's about running an experimental setup across your problems while gauging how you are doing at the moment. It's about better managing the resulting data explosion that arises.

And this while setting & managing the expectation that - a) this might not actually work, and b) even if it works now, it may degrade as you go.
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Best for Learning

Not the usual Machine Learning course!

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Design Thinking

Ponder different ways in which a top-level problem can be structured, and solutions  woven together in an architecture
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No hands-on exercises, Jupyter Notebooks or datasets here
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Open-ended questions, discussions and deep thinking are best for learning, and are our primary method
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Peer Learning

Interacting with peers and learning from their experience is better than exams, quizzes or certificates. 

Jagannath Rajagopal

I teach Design Thinking & Architecture for a whole universe of methods of which Machine Learning is but a part. Between high level use case content and hands-on coding, there exists a knowledge gap today. At Kado, my team of overachievers, and I have melted our brains and put down everything we know about Hero Methods towards a knowledge offering that is different from the usual run-of-the-mill Machine Learning course. Our goal is to bridge this gap.

I've wrestled Tensorflow to the ground, learned Python from scratch in the process, crashed and burned in 2 start-ups, launched a successful YouTube channel, failed at selling Scuba gear, taught R to many, and succeeded several times in implementing large scale Supply Chain Planning systems for big companies.
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