Machine Learning

The Fast Track to AI with Javascript and Serverless

What is the work that you are doing today?

A lot of our work involves three threads. We're very active in serverless. And a lot of that is actually looking at legacy world challenged platforms and helping transform them into systems that can run in a serverless way. There's a lot of organizations that have realized that the economic operating model for cloud and particularly around serverless starts to make a lot more sense. So we're helping with a lot of that movement to serverless and also greenfield as well. Increasingly, a lot of that work involves the upgrading of those platforms to have additional machine learning capabilities. That's about augmenting the business process with things like natural language processing and understanding how to extract meaning from text, some image recognition and so on.

What are your goals for this talk?

The goals are similar to the book. The talk is based on our book, AI as a Service. And we wrote that to help people get on board with AI services. I think there's a lot of myth and uncertainty around A.I. and Machine Learning. You don't necessarily need to have a team of data scientists and a PhD in Machine Learning because of the commoditization that we're seeing around this technology, engineers can be very effective for the right use cases. And so we're trying to educate and inform developers about how they can get on board with this technology. I suppose the other part of it is that data science is one thing and building machine learning models is another. But that doesn't equate necessarily to a useful product or even a useful internal platform. There's an enormous amount of work around the edges that has to be done in order to operationalize these machine learning solutions. So there's a much wider context around that that we're trying to help educate people on as well.