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.

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?

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.

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)?

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...