6 December 2021
re:Invent: How Amazon.com Transforms Customer Experiences Through AI/ML
This is an overview of a session that I went to during re:Invent 2021. I start by providing the notes I took during the session, and then I will give my take and comments if I have any at the end.
Choong Lee and Laura Squier
How Amazon approaches the idea of applying AI/ML to everything
- Key use cases
- Lessons learned
AI/ML can be used now because we have enough data volume and velocity
- What powers Prime one-day shipping
- 400 million products are forecasted everyday down to the zip code level
- Fulfillment centers, robots, trailers, and delivery vans all come into play
- Well-placed fulfillment centers
- Know how many trailers and delivery vans are needed to get products out
- Some product have obvious demand
- Sun lotion
- Cleaning products
- Some products are not obvious at all
- New product or a demand spike
- Seasonal products
- Goal is to anticipate what products will be needed at what time in what locations
- Forecasting models
- Traditional approach has input and output with a bunch of if-else conditions
- Bunch of rules and are manually coded
- AI/ML models are trained by data points and provide output in probability
- Models used to be decision trees and are now neural networks
- Nonlinear relationships
- Automated feature engineering
- There is a lot of interconnected information that was not used by decision trees that can be with neural networks
- After the forecast comes the fulfillment center
- Items are randomly stored in cubbies which are moved by robots
- Robots/computers know which cubbies have which items
- Robots move to pickers (humans) based on the probability that they will need a certain item they hold
- Robots present the correct cubby to the picker
What other customer experiences are transformed through AI/ML
- Moving from a reactive approach to a proactive approach
- Food allergen or health and safety risks on items
- They used to gather feedback from customers, identify and tag items with concerns, then suppress items and notify regulatory bodies
- Had to wait for customer feedback or recalls
- Goal was to predict health and safety risks
- Built model around key parameters and assign a probability of concern before anything is shipped
- Shipping packaging size reduction
- ML model based on business rules and visual inspections
- That model also knows about “collectibles” and can predict if an item is collectible
- If collectible, that model knows whether or not the item needs additional protective packaging
- Teams at Amazon use AWS tech to build and develop new models in weeks
What did Amazon learn was most important to get right in the ML journey?
- The hallmark of Amazon is speed and scale
- At that speed and scale, traditional algorithms won’t work
- There are hurdles for businesses that are not already using AI/ML
- Crossing those hurdles
- ML-first mindset and culture
- Start with one question: how will you use ML?
- Enabling teams for the mission
- Break down the silos between business domain experts and technical experts
- Evolve the teams as you grow and scale
- Start small and build within the team before scaling across teams
- Apply the right tool for the job
- Simple, off-the-shelf, pretrained AI service; ML platform ready to be trained; or individual algorithms like tensorflow
- Powerful data platform
- Data fuels ML-driven innovations
- Teams closest to the customer will be able to apply ML with the largest impact
- Amazon uses a big data marketplace for AI/ML teams to have self-service discovery and subscription to data
- Choosing the right first project
- Does it solve a real and significant prob for your customer?
- Where/how does ML unlock new capabilities?
- If traditional BI tools do a majority of what’s needed, ML might not be a good first project
- Are there places where we already have a lot of untapped data?
- Can we achieve success in the first 6-10 months?
- Get something to prod and prove value to customers
- Is is important enough to get sustained attention and support?
- Remove the blockers to the path to deploying solutions to production
I am new to AI/ML. This session made me think that AI/ML was more of a business-related technology than a developer-related one. I am sure that there are applications for both, but I found it strange that they presented much more of the business side of things especially given the venue.