How Amazon makes Machine Learning Trustworthy[System Design Sundays]
With all the discussion around Bias in ChatGPT and Machine Learning, these techniques might be very helpful
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On Sundays, I will go over various Systems Design topics⚙⚙. These can be mock interviews, writeups by various organizations, or overviews of topics that you need to design better systems. 📝📝
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GPT 4 is out!!!
And Friday’s piece on influencers using the hype around AI received a lot of kind messages from you.
So I figured I’d continue with the AI theme to end up this week. Today, we are talking about Amazon and specifically how they make their AI safer and more trustworthy. Their system setup is really interesting because they manage to balance safety, privacy, and costs in large-scale distributed systems. Since many of you are not technical AI folk, I’ll keep the AI very brief. To those that want to learn more, I have a full breakdown of their techniques in my second Substack newsletter- AI Made Simple. You will find the breakdown here.
Amazon 3 Pillars for Safe AI
Privacy-preserving Machine Learning- Privacy Preserving ML is based on a simple idea- the output of a model should not give you any hints about the input. This is accomplished by replacing some parts of the input with other related values. This allows them to keep the structure of their input intact while masking specific details. An example can be seen in the image below.
Federated Learning- Think of all the Alex Devices, Prime Video apps, and different devices people use for their Amazon accounts. If Amazon directly sent the data back to the centers, their costs would spiral out of control. Not to mention, the huge privacy red flag of Amazon Data centers storing your conversations, shopping, etc. Clearly, this is not a good idea. But then, how would you update the models based on new user interactions?
What if you just let the models be updated on the local device? Say, one day I watch a lot of horror movies on Prime on my phone. So we update the recommendation systems on my phone to account for my new tastes. Once these updates have been made, I share the updates with the Amazon centers. You, my love, have just learned about federated learning.
Fairness in ML- Datasets and sources are inherently biased. By oversampling and making a few data substitutions, Amazon balances the bias in their datasets to create more balanced training data. This helps them create fairer AI.
If you’re interested in designing large-scale intelligent systems, these techniques will definitely help you out a ton. If you want to learn more about them, including a deeper analysis of their pros and cons, check out my article about it below.
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