An IT industry analyst article published by SearchITOperations.
Big data and artificial intelligence will affect the world — and already are — in mind-boggling ways. That includes, of course, our data centers.
The term artificial intelligence (AI) is making a comeback. I interpret AI as a larger, encompassing umbrella that includes machine learning — which in turn includes deep learning methods — but also implies thought. Meanwhile, machine learning is somehow safe to talk about. It’s just some applied math — e.g., built-over probabilities, linear algebra, differential equations — under the hood. But use the term AI and, suddenly, you get wildly different emotional reactions —for example, the Terminator is coming. However, today’s broader field of AI is working toward providing humanity with enhanced and automated vision, speech and reasoning.
If you’d like to stay on top of what’s happening practically in these areas, here are some emerging big data and AI trends to watch that might affect you and your data center sooner rather than later:
Where there is a Spark… Apache Spark is replacing basic Hadoop MapReduce for latency-sensitive big data jobs with its in-memory, real-time queries and fast machine learning at scale. And with familiar, analyst-friendly data constructs and languages, Spark brings it all within reach of us middling hacker types.
As far as production bulletproofing, it’s not quite fully baked. But version two of Spark was just released in mid-2016, and it’s solidifying fast. Even so, this fast-moving ecosystem and potential “Next Big Things” such as Apache Flink are already turning heads.
Even I can do it. A few years ago, all this big data and AI stuff required doctorate-level data scientists. In response, a few creative startups attempted to short-circuit those rare and expensive math geeks out of the standard corporate analytics loop and provide the spreadsheet-oriented business intelligence analyst some direct big data access.
Today, as with Spark, I get a real sense that big data analytics is finally within reach of the average engineer or programming techie. The average IT geek may still need to apply him or herself to some serious study but can achieve great success creating massive organizational value. In other words, there is now a large and growing middle ground where smart non-data scientists can be very productive with applied machine learning even on big and real-time data streams…(read the complete as-published article there)