Our customers - mostly Fortune 1000 organizations - are in some stage of a machine learning project. And it's early days, even in these very large and well-funded organizations. There aren't a lot of ML models in actual production today.

We participated in an AI event during last week’s South by Southwest Conference (SxSW) that perfectly captured the Austin experience. Capital Factory, a local incubator, hosted a Saturday night AI Mixer. The event gave local AI companies the opportunity to showcase and demo their solutions to SxSW attendees. We were invited to co-sponsor the mixer by our friends at Kung Fu AI, an Austin-based AI strategy and development services firm.

This morning we put out a press release announcing that we have integrated a suite of computer vision data labeling and annotation capabilities into the Alegion Training Data Platform.

We know how hard it is to prepare training data for machine learning projects. We’ve been labeling and annotating training datasets for enterprise data science teams for years.

The promise of AI for a business is irresistible. Increased efficiencies, cost savings, improved customer satisfaction are too attractive to ignore. So of course your board has just set a mandate of “integrate AI into the business”.

The local Austin machine learning community will be busy this week.

We produce quality training data for computer vision and natural language processing, which means we have a lot of experience with machine learning projects throughout their lifecycle. Our experience with firsttime ML project teams has given us valuable insights into the kinds of approaches that foster project success and the obstacles that can cause costly delays and even project failure. Data scientists are well aware of the best approaches, the ugly obstacles, and what it takes to be AI-ready. Does the rest of the organization?

Human to AI interactions have become pretty commonplace with the ubiquity of natural language processing (NLP) devices and systems. I can ask my home assistant to set a timer, purchase things, get me a cab, or tell me a joke. I can ask my phone to get me directions or send a text for me. Being able to do these things hands-free demonstrates the power of Natural Language Processing to understand, analyze, and generate human speech.

By Sudipto Ghosh, published at AiThority on Dec 24, 2018.