Thanks for the details comparison.. i'll have to look more into these tools to come to any conclusion based on my needs.

Here's what I'm looking at:
An automated ML Pipeline

thanks for the reference Martin.. I'd soon by starting with the TRAINS.. and would be in touch on the progress.

I work on VisionAI so would need integration to my existing data pipeline (including the annotation tools - LabelMe, VGG etc) and also add features like Email Alert for finished Job(I'm not sure if it's already there).

Others doubts that I have is:
How does it compare to Apach AirFlow or DVC for Data Management(if I'm not going for the Paid version)?

Glad to know it.
As I'm a Full-stack developer at Core. I'd be looking to extend the TRAINS Frontend and Backend APIs to suit my need of On-Prem data storage integration and lots of other customization for Job Scheduler(CRON)/Dataset Augmentation/Custom Annot. tool etc.

Can you guide me to one such tutorial that's teaching how to customize the backend/front end with an example?

Automated Data Source Integration Data Pooling and Web Interface for Manual Annotation of Images(Seg. / Classif) Storage of Annotation output files(versioned JSON) Online-Training Support(for Dataset Shifts) Data Pre-processessing (filter/augment) Data-set visualization(stats of Dataset) Experiment Management(which is why I liked TRAINS), Jupyter Integration(for Test Management) Training Progress Visualization(TensorBoard like) Inferencing and Visualization of Results Reproducibility of Trai...

online-training:
Re-training the models to update it's weights for any new dataset introduced after the previous deployment. Based on certain threshold, we can decide when to re-train the model.

It's mainly application for scenarios that involve streaming/sequential data sets that are made available over time. E.g. Facial Recognition or Retails usecases for a new Fashion segments.