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I love these initiatives and would love to learn about any other end-to-end deep learning architectures. It took us a long time to build up our in-house tagging

I love these initiatives and would love to learn about any other end-to-end deep learning architectures. It took us a long time to build up our in-house tagging infrastructure and whole model warehouse + inference servers that are connected to Kafka. I'd love something more polished though. It's a killer when you want to do ML in the real world, but have to spend forever rolling out infrastructure.

skewart 9y ago
I'd be really curious to hear more about how you set up your tagging infrastructure and model warehouse.
- wooter 9y ago
  you need applications and queryable storage for maintaining models both for production use, internal testing/validation/research, and everything in between. Maintaining a model - or collection of models - implies maintaining a ton of data that can ideally be parameterized (learning data, build info, tuning parameters, purpose, priority, etc) and there are a lot of sophisticated ways to handle this all efficiently mostly dictated by your problem space (algorithm, data volume, response time, research methodology, etc). This is all outside (or inside) of whatever application runs your actual business model and the infrastructure necessary to query your "live" model(s) in a distributed/replicated environment if necessary (as is for most big data projects).
gaius 9y ago
Have you considered AzureML? http://studio.azureml.net http://studio.azureml.net It's really slick, and you can program it in Python and/or R, you aren't tied to MS stack.
- ganeshkrishnan 9y ago
  They don't have recurrent neural networks or most Deep Learning stuff. The closest to all these, IMO, is H2o Flow
aub3bhat 9y ago
I am building Deep Video Analytics which aims to become Database+Processing framework for visual data. With DVA you can load, store, decode, annotate, apply inbuilt and custom Deep learning algorithms. Further you can examine the data through full text and visual search. It's comes with a Django+Postgres+Celery/RabbitMQ setup. And scales across multiple machines on AWS using EFS + S3. https://www.deepvideoanalytics.com https://www.deepvideoanalytics.com
- deleted 9y ago
  [deleted]

skewart 9y ago

I'd be really curious to hear more about how you set up your tagging infrastructure and model warehouse.

wooter 9y ago
you need applications and queryable storage for maintaining models both for production use, internal testing/validation/research, and everything in between. Maintaining a model - or collection of models - implies maintaining a ton of data that can ideally be parameterized (learning data, build info, tuning parameters, purpose, priority, etc) and there are a lot of sophisticated ways to handle this all efficiently mostly dictated by your problem space (algorithm, data volume, response time, research methodology, etc). This is all outside (or inside) of whatever application runs your actual business model and the infrastructure necessary to query your "live" model(s) in a distributed/replicated environment if necessary (as is for most big data projects).

gaius 9y ago

Have you considered AzureML? http://studio.azureml.net http://studio.azureml.net It's really slick, and you can program it in Python and/or R, you aren't tied to MS stack.

ganeshkrishnan 9y ago
They don't have recurrent neural networks or most Deep Learning stuff. The closest to all these, IMO, is H2o Flow

aub3bhat 9y ago

I am building Deep Video Analytics which aims to become Database+Processing framework for visual data. With DVA you can load, store, decode, annotate, apply inbuilt and custom Deep learning algorithms. Further you can examine the data through full text and visual search. It's comes with a Django+Postgres+Celery/RabbitMQ setup. And scales across multiple machines on AWS using EFS + S3. https://www.deepvideoanalytics.com https://www.deepvideoanalytics.com

deleted 9y ago
[deleted]