I'm wondering how your work rationalizes connecting SageMaker/Jupyter to "business" as opposed to, say, well-known use cases for machine learning.
In the 2020 Gartner Magic Quadrant analysis for Machine & Learning & Data Science, for example, they didn't evaluate SageMaker because "it was primarily marketed to and used by application developers at the time of evaluation". From my pov, SageMaker is a powerful choice for companies adopting AWS that are already have solid technical staff capable of switching to this platform easily. What I don't get is whether businesses not already invested in this technology see this approach as something they can easily adopt and integrate.
Disclaimer 1: I work for Dataiku, which sells a product called Data Science Studio (DSS). Disclaimer 2: we do not consider SageMaker/Jupyter competitive with DSS. I'm just curious what your sense is of businesses that would embrace this approach to ML.
Make visible what, without you, might perhaps never have been seen. - Robert Bresson
Hi Michael, I've used Dataiku and really like it. Congrats on a great product. I don't see Dataiku losing a ton of business to Sagemaker. AWS is great at making foundational tools and not so good at making the next level up in usability. The target market for Sagemaker is the do-it-yourself company that wants to build their own tools without having to manage infrastructure.