When considering beginning your AI project, you're likely inclined to have a blend of excitement and concern. Stunning, this can be astonishing. All the examples of success stories, the number of sales grow, income development etc. In any case, on the other hand, imagine a scenario where it turns out badly. How might you alleviate the risk of wasting money and time on something that simply isn't practical in any way?

People often think the algorithms used for Machine Learning (ML) are the most important factors for developing a successful ML system. However, shrewd Artificial Intelligence (AI) and Machine Learning systems in production (managing the data at all stages, with multiple models) have much more impact on the success of the model than the specific learning algorithm. In their book AI and Analytics in Production, Ted Dunning and Ellen Friedman describe how organizations can get their AI systems into production and delivering value. "In addition to the platform and application orchestration technologies, you will need an architectural design that simplifies logistics, supports multiple models and multiple teams easily, and gives you agility to respond quickly as the world (and data) changes, as indeed it will." Database storage has been determined by specific processes that assured accessibility, security, and accuracy, however, increasing amounts of unstructured data and the increased use of data lakes has caused significant problems in Data Management.

The idea sounded good in theory: Rather than giving away full-boat scholarships, colleges could optimize their use of scholarship money to attract students willing to pay most of the tuition costs. So instead of offering a $20,000 scholarship to one needy student, they could divide the same amount into four scholarships of $5,000 each and dangle them in front to wealthier students who might otherwise choose a different school. Luring four paying students instead of one nonpayer would create $240,000 in additional tuition revenue over four years. The widely used practice, called "financial aid leveraging," is a perfect application of machine learning, the form of predictive analytics that has taken the business world by storm. But it turned out that the long-term unintended consequence of this leveraging is an imbalance in the student population between economic classes, with wealthier applicants gaining admission at the expense of poorer but equally qualified peers. Machine learning, a branch of artificial intelligence, applies specialized algorithms to large data sets to discover factors that influence outcomes that might be invisible to humans because of the sheer quantity of data involved. Researchers are using machine learning to tackle a wide variety of tasks of unimaginable complexity, such as determining harmful drug interactions by correlating millions of patient medication records or identifying new factors that contribute to equipment failure in factories. Web-scale giants such as Facebook Inc., Google LLC and Microsoft Corp. have stoked the frenzy by releasing robust machine learning frameworks under open-source licenses.

While there are only a few companies using artificial intelligence in production, it's certainly where the future lies. In this wide-ranging episode of The New Stack Makers podcast, we talk with Ted Dunning, chief application architect at MapR and author, along with Ellen Friedman, of the new O'Reilly book "AI and Analytics in Production." We all have an image in the back of our minds of computers taking over the world, but the truth for the short-term, said Dunning, is that some of the best value for artificial intelligence (AI) is going to be some of the most boring stuff. AI, at least in the beginning, will replace boring repetitive tasks and mine massive amounts of data in ways not previously imaginable. As you accumulate data, said Dunning, it begins to have interesting synergistic effects, so that the raw value can go up in unexpected ways.

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA. Not even close, says Ted Dunning, chief application architect at MapR, but "anybody who thinks that they can just buy magic bullets off the shelf has no business" buying machine learning technology in the first place. "Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your company's life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator," Dunning says.

Recently Anthony Evans, principal consultant with Computer Design & Integration, was recruited to come in halfway through what should have been a relatively straightforward project. The company wanted to deploy artificial intelligence at its customer service help desk to provide agents with a sort of "whisper agent" that would help the agents with questions about which they were unsure. Either the virtual agent would have the answer or it would escalate the question to a second tier of assistance. But something was off with the implementation pilot -- the whisper agent turned out to be only of marginal help to the desk agents. Eventually the team discovered where they went wrong, according to Evans.

The internet of things (IoT) and industrial internet of things (IIoT) will breakout in 2018, with organizations ramping up deployments and incorporating IoT technologies into their products, processes and workflows. Research firm Gartner predicts there will be nearly 20 billion devices connected to the IoT by 2020, and IoT product and service suppliers will generate more than $300 billion in revenue. We spoke with a number of IT leaders and industry experts about what to expect from IoT deployments in the coming year. Following are six IoT trends to watch in 2018. Scott Gnau, CTO of Hortonworks, predicts 2018 will be the year of consumer IoT.

Since big data roared onto the public stage in 2012, we've seen an accelerating pace of technological progress and prowess. Along with it, tech trends have come and gone. Once a rising star, Hadoop is now settling into a supporting data role while AI now soaks up all the attention. But this year, there's good reason to think that many organizations will dedicate a sizable chunk of their time and resources to just getting a handle on the data. After getting burned with advanced data projects that didn't pan out, it's becoming increasingly obvious that many organizations are not ready to partake of data's profitable bounty until and unless they master some of the more basic requirements.

At the end of every year, tech PR firms circulate and hawk the prognostications of their client companies' executives on what the next year will bring in the world of data and analytics. There are almost always contradictions to be found on certain points and suspicious unanimity on others. And because the predictions tend to function as self-serving marketing messages, sometimes they can sound more like taglines than substantive forecasts. It's always fun to read and sort out these predictions. That may sound a bit snarky but -- I gotta say -- even if it's a lot of work, it's always fun to read and sort out these predictions.

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA. Not even close, says Ted Dunning, chief application architect at MapR, but "anybody who thinks that they can just buy magic bullets off the shelf has no business" buying machine learning technology in the first place. "Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your company's life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator," Dunning says.