Developers should be prepared to integrate with and use their deep learning portfolio.  Before we get some scope on their API's and endpoints some background in deep learning (a specialized subset of machine learning concentrated on the "Greedy Layer-Wise Unsupervised Pretraining procedure" - Hinton, 2006 - University of Toronto) and the "Long Short-Term Memory model - Hochreiter and Schmidhuber 1997" will be required.

Design/Theory Preparation

Get the following Nov 2016 MIT book from Amazon by Ian Goodfellow, Yoshua Benglo, Aaron Courville - (one for work, and one for home - as it is usually out of stock).  Review your linear transformation and matrix math to prep.

https://www.amazon.ca/Deep-Learning-Ian-Goodfellow/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1486501444&sr=8-1&keywords=deep+learning

AIaaS

Open Source

OpenAI

https://github.com/openai/kubernetes-ec2-autoscaler

https://openai.com/systems/

http://www.cs.toronto.edu/~fritz/absps/ncfast.pdf

http://people.idsia.ch/~juergen/lstm2003tutorial.pdf

http://dl.acm.org/citation.cfm?id=1246450&CFID=898083621&CFTOKEN=58541063&qualifier=LU1039002

https://deeplearning4j.org/devguide

Commercial

https://aws.amazon.com/machine-learning/amis/

https://aws.amazon.com/mxnet/?&trk=ha_a131L000005CvfAQAS&sc_ichannel=ha&sc_icampaign=pac_q4_site_merch_sign-in_deep_learning&sc_ioutcome=Product_Adoption_Campaigns&sc_iplace=SignInPage

https://www.microsoft.com/cognitive-services/en-us/sign-up

https://deepfield.com/cloud-genome/