Recap of the 2017 Interop ITX Conference

I spoke at 2017 Interop ITX Conference which ran from May 15 – 19 in Las Vegas. See I estimate there were about 3,000 attendees. Most of the attendees I spoke to were fairly senior managers at all kinds of companies, small to large, in all kinds of businesses.

My talk was titled “Understanding Deep Neural Networks”. I explained in some detail what regular NNs are, then described some variations of DNNs including convolutional NNs, recurrent NNs, LSTM NNs, and generative adversarial networks. I also mentioned a bit about some of the work Microsoft Research is doing in the area of deep learning. My talk was hosted by Sam Charrington, who is very knowledgeable, and who gave a great intro to the current state of ML. See

I’ve spoken at many Interop events over the years and each one was different from all the others. My overall impression is that most of the nuts-and-bolts challenges of enterprise networks seem to have been solved and that the two dominant issues are security and evolving infrastructure to give a competitive business advantage (such as predictive systems that use deep neural networks).

Other talks at the event that I particularly liked were “Machine, Platform, Crowd” (Andrew McAfee, MIT), “Machine Learning” (Josh Bloom, General Electric), and “AI for Wireless Networking” (Ajay Malik, Google).

Anyway, I met a lot of interesting people and picked up a lot of useful information, mostly about the current state of machine learning in enterprises. Every company I talked to is trying to find some way to get expertise in how to create advanced predictive systems.

The bottom line is that the 2017 Interop ITX conference was a good experience. I intend to speak there next year and if you work in IT management I recommend you consider attending too.

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