Ask HN: Which AI/ML software stack is quickest way from idea to ML provision?

Enterprise use a lot of tooling such as DataRobot, H2O Driveless AI, Alteryx, KNIME, Datameer, Ayasdi, Quantexa etc.

There is even a large list (for Big Data Ecosystem) maintained by Matt Turck which has a section for AI/ML

http://mattturck.com/wp-content/uploads/2019/07/2019_Matt_Turck_Big_Data_Landscape_Final_Fullsize.png

https://mattturck.com/data2019/

Cloud providers are also making significant attempts on capturing this space. - https://aws.amazon.com/machine-learning/ - https://cloud.google.com/products/ai/ - https://azure.microsoft.com/en-gb/overview/ai-platform/

There are tons of AI/ML open source solutions as well. Tensorflow, Keras, PyTorch, MXNet, Kubeflow and the list goes on... https://github.com/topics/machine-learning?o=desc&s=stars

There is also a huge list maintained by kdnuggets. https://www.kdnuggets.com/companies/products.html

The use cases for ML are vast and every solution in the market is trying to find a unique corner for itself.

What do you think is the best way & tech stack to go from Idea to Analytics using AI/ML?

Do you have any recommendations for a decent commercial solution even if it is closed source.