There’s a ton of momentum around machine learning and AI today, but there are important logistics to be worked out. Despite fears and bold proclamations that AI will replace humans, its best application today is in serving people, to make them more productive. Today’s AI platforms need to support that use case, but how well do they do so?
Next is the overwhelming fragmentation of AI tools and technologies. There are a range of machine learning and deep learning frameworks and libraries with which to build models. The result is that companies are getting distracted by these disparate technologies, diluting their focus on pragmatic adoption of AI. There is also the a decision point around using trained models from the public cloud providers: which platforms should you be on, and is there any way to mix, match and compare them?
Abstraction layers help here, not just across libraries or cloud-based cognitive services, but for using them in combination, and testing which is most effective. Plus, once that’s done, and the models are built and/or selected, there’s the issue of deploying them to, and using them in, production. What’s the best way to achieve that operationalization?
There are a lot of questions here. Join us for this free 1-hour webinar from GigaOm Research to get to some of the answers. The Webinar features GigaOm analyst Andrew Brust and our special guest, Jon Richter from CognitiveScale, a company specializing in augmented intelligence.
In this 1-hour webinar, you will discover: What’s involved in building AI that makes all your people more productive; How to experiment with models from different libraries and cloud platforms, efficiently and efficaciously; Why production deployment and use of machine learning models is no mere detail – it’s the critical link in making AI work at scale, beyond the scope of mere proof-of-concept projects; How to maximize sharing and unification, across programming languages, tools, frameworks.