As an ML practitioner in a large ecommerce company, I totally agree. When I evaluate third party tools, 99% have no use at all for me. Things like Algolia or Rekognition or Clarifai.
Even Sagemaker and Fargate are mostly not useful to me as my company can easily operate its own k8s platform.
Open source tools for model building are great and a small team of engineers can write whatever odds & ends that aren’t covered, or make scalable implementations.
Notebook platforms like Databricks are similarly useless. A notebook is not a frontend to a cluster and notebooks are universally poor development environments even for the very tasks they are advertised.
The main thing that matters for ML development is how quickly & easily you can put an arbitrary container or VM into a deployment environment. You’re always going to need to rapidly change your container or VM, have common ancestors that get specialized to support GPU training or some web server application layer. Making the shortest possible distance between the definition of this environment and deployment of it is the whole game.
The type of tools I’m willing to pay for are specialized database engines, rapid data annotation tools, and specialized hardware environments.
I have no time for some “platform” tool for data provenance, notebook environments, model tracking, model-as-a-service APIs like Rekognition, or anything where I upload data and get back a model.