At this week’s Apache Spark Maker Community 2016 event, the list of star guests and key speakers is a lengthy and impressive one, as sponsor IBM puts firm support behind drawing in the attention of existing and potential developers, along with spreading awareness of Spark’s utility for various industries.

Jim Kobielus, data science evangelist at IBM, joined John Walls and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, to give a run-down of some of these speakers, but also to explain the goals and perspectives of the event’s organizers and what they hope to see in the future.

Frameworks and open source

Asked to give an overview of the event, Kobielus said, “[Today, at] the Apache Spark Maker Community event … what we’re doing is we’re bringing together the open analytics community around the evolving open source stack … to enable data scientists to be more productive. The evolving stack includes Spark, but also R: the R language is so important in terms of programming data science applications.”

Kobielus also highlighted the widening market for Spark: “The adoption of Spark is going through the roof, we’re finding with our customers all over the world and in all industries, and we’ve invested deeply in R&D on all things Spark,” he said.

The power of open data science

With this growing market, IBM is making firm efforts to keep it growing by supporting training centers for “hundreds of developers.” Kobielus described how they were “investing heavily in training … to train the next generation of application developers, who at their very heart need to master the techniques and the tools for data science, building and tuning machine learning models and deploying them, deep learning into applications of all sorts.”

A major drive for this directed learning is that, as Kobielus explained, at IBM “we see the future as all focused on statistical analysis, machine learning and so forth.”

Looking back at IBM’s hardwired history, Kobielus also noted that there’s a big move toward data extraction and extracting from the data through machine learning instead of coding algorithms directly into programs. This “dynamic and adaptive” growth has the company excited about the possibilities, though it may take some time for the full potential to be realized.

Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Apache Spark Maker Community 2016.

Photo by SiliconANGLE