AI is all the buzz today, yet many find the final step – operationalizing AI at scale – too big a leap. So, what's the secret to successfully get AI out of the lab and into production? To deploy, monitor, and govern machine learning and other analytical models, you need the confidence and control of a rapid, repeatable process. Model operations (ModelOps) is the answer you need now to make AI business value happen.

Data science platforms are gunning toward automation. With the widespread release and popularity of Google Cloud AutoML, DataRobot Inc. tools and other automated machine learning platforms, analysts, businesses and users are beginning to tap into the technology and the rapidity of automation. In this Q&A, Mike Gualtieri, vice president and principal analyst at Forrester Research, outlines the state of automated machine learning platforms and their use cases. Editor's note: The following has been edited for clarity and brevity. What are some key capabilities of automated machine learning platforms?

More than half (54%) of global information workers are interrupted from their work a few times or more per month to spend time looking for or trying to get access to information, insights, and answers. The problem: Old keyword-based enterprise search engines of the past are obsolete. Cognitive search is the new generation of enterprise search that uses artificial intelligence (AI) to return results that are more relevant to the user or embedded in an application issuing the search query. A new generation of enterprise search solutions that employ AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Our new Forrester Wave report evaluates 9 of the leading search vendors against 23 important criteria.

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules).

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect.

Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on to Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war."

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules).

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?