Google is boosting its AI-as-a-service offerings this week, most notably with the alpha release of a new Contact Center AI solution. Contact Center AI is built around its Dialogflow development suite for conversational agents, which was launched last fall and already in wide use. Dialogflow Enterprise Edition now has the ability to build AI-powered virtual agents for contact centers, a Phone Gateway for taking calls without infrastructure, Knowledge Connectors for understanding unstructured data like FAQs and Sentiment Analysis. In Contact Center AI, a Virtual Agent first answers the call and handles it if possible. If not, it passes the call to a human representative, who is helped by an Agent Assist system that continues to monitor the call and provide supporting info as needed.
Yesterday, tech giant Google announced its latest solution, the Cloud AutoML, that will enable developers, even those that lack machine learning expertise, to build image recognition models. It is said to be a part of the company's initiative to democratize AI learning and provide a simple approach that anyone can easily understand.
If last week's Google Next 2018 event is any indication, Google Cloud is growing quickly. Registrations for the July 23 to July 26 event topped 25,000, and actual attendance easily doubled the 10,000 at Google Next 2017. That's good, but if this public cloud is going to catch up with also-fast-growing rivals Amazon Web Services (AWS) and Microsoft Azure, Google is going to have to play to its strengths. Read also: Google's G Suite adds new AI and security tools - CNET From my perspective, Google's biggest appeals to big businesses are its deep learning (DL), machine learning (ML), and data platform capabilities (though I'm biased and my Constellation colleagues who follow G Suite and the rest of Google Cloud Platform (GCP) cloud infrastructure might see it otherwise). Among the many announcements at Google Next 18, the biggest steps forward -- and the ones I see as most likely to accelerate growth -- were those aimed at expanding the use of Google's DL, ML, and data platform capabilities.
Leveraging machine learning to process data and workloads has proved to be significantly beneficial for diverse enterprise industries in recent years. Whether it be healthcare, BFSI or retail, machine learning systems turned out to be extremely promising to process millions of data and build complex models. Having said that, the traditional machine learning process involves humans to look after the operations, to code, and to build the models. But, with the crisis in hand, businesses are looking to reduce their workforce, some are even not equipped with resources to spend on employing an experienced data science team. And that's when AutoML can come to rescue for many.