An Amazon Sr. Manager's Guide to Managing AI Teams effectively [Storytime Saturdays]
How to build teams that can help you take abstract ideas to tangible AI products.
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Nvidia’s stock is on a tear right now. They are close to touching the Trillion Dollar mark, which would put them in a rare league with Google, Apple, and other heavy hitters. Their growth has been funded by one thing in particular- AI Hardware. As everyone and their grandmas continue to get hot and bothered about AI, Nvidia is uniquely positioned to profit. More and more teams will continue to set up AI teams and integrate Data into their operations.
However, this leads to an interesting challenge. Managing anyone is hard- but AI teams can be a special kind of nightmare to handle. The multi-disciplinary and opaque nature of the field adds extra challenges, ones that need very robust systems to tackle. Fortunately, the internet can be a great source of knowledge. Christopher Walton, a Sr Applied Science Manager at Amazon, shared 5 amazing insights about how he recommends AI teams be managed. In this article, I will be expanding upon his tips, while also adding some others that will be useful to you. Use these to get ahead of the curve in adopting AI the right way. If you don’t want to fall behind in the AI race, keep reading 📖📖.
How to Effectively Manage AI/ML Teams
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