01. Dinesh Nirmal, IBM Analytics, Visits #theCUBE!. (00:21)
02. What Do You Make Of The Show. (00:44)
03. What Is Machine Learning And Why Are You Sharing The Secret Sauce. (02:20)
04. Where Does System ML Come In To Machine Learning. (03:58)
05. How Does The Sequal Contributions Fold Into The Maturation Of Spark Sequal. (08:20)
06. Are You Modeling, Training And Deploying Things In Real Time. (12:24)
Track List created with http://www.vinjavideo.com.
--- ---
‘House calls for data’ in IBM’s future | #SparkSummit
by Gabriel Pesek | Jun 7, 2016
As attendees gather for this year’s Spark Summit 2016, many are asking how Spark has been leveraged over the past year to address emerging trends, such as machine learning, expanded data harvesting, improved analytics and more.
Dinesh Nirmal, VP of development for next-gen analytics, Spark, Hadoop and site exec at IBM, met with John Walls and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, to discuss these things along with his take on the IBM-customer relationship and the importance of flexibility.
Spark is growing
Nirmal was very happy to see the number of attendees and speakers at this year’s event, mentioning that he felt as though “there’s double the number of people in the keynote.” A major reason for the growth of interest in the event, he felt, was that development and adoption are “taking off” for Spark, along with related technologies.
“Every single customer shop that I’ve gone to has an R-shop,” he said, referring to the increasingly popular programming language that is commonly used for statistical analysis and similar functions. “[R] is getting adopted in a very accelerated space,” he said.
Making it easy for users
Nirmal also noted the flexibility that IBM was striving to enact, saying, “We see that we can give a rich set of libraries that focused developers can use to build new models and have machine learning.”
That open draw for programmers and developers will lead to big things, IBM hopes. “What we envision … let’s make it flexible for the developer … let’s give the developer the choice on what language they want to use,” he said.
Addressing the various goals of statisticians, Nirmal said, IBM is asking: “How can we give the end user, the data scientist, a multitude of choices that they can pick and choose from, rather than just saying [for instance]: ‘You want to develop, use Python.’”
“When we started talking about how [IBM would] build a platform, there’s three things that we looked at,” Nirmal continued. “One, how do we build a scalable open-source-based architecture? … The second piece is, how do we differentiate? … And the last piece is … what we want to do is bring analytics to where your data is.”
This last point, termed as “house calls” for data, along the lines of a doctor making house calls, was one of the ways in which IBM will be more engaged with users looking to fully capitalize on their Spark experience.
“Those are the kinds of things that will get the buzz on top of Spark,” Nirmal said, giving an example of the system applied to machine learning while asking the question of “’how do we real-time score in a feedback loop, so that the model is learning?’” The answers, he felt, are waiting to be uncovered by the Spark users and developers.
Looking at the future of Spark and these resultant technological abilities, Nirmal was highly positive, saying: “I think there’s tremendous potential.”
#SparkSummit
#theCUBE
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Dinesh Nirmal, IBM Analytics | Spark Summit 2016
01. Dinesh Nirmal, IBM Analytics, Visits #theCUBE!. (00:21)
02. What Do You Make Of The Show. (00:44)
03. What Is Machine Learning And Why Are You Sharing The Secret Sauce. (02:20)
04. Where Does System ML Come In To Machine Learning. (03:58)
05. How Does The Sequal Contributions Fold Into The Maturation Of Spark Sequal. (08:20)
06. Are You Modeling, Training And Deploying Things In Real Time. (12:24)
Track List created with http://www.vinjavideo.com.
--- ---
‘House calls for data’ in IBM’s future | #SparkSummit
by Gabriel Pesek | Jun 7, 2016
As attendees gather for this year’s Spark Summit 2016, many are asking how Spark has been leveraged over the past year to address emerging trends, such as machine learning, expanded data harvesting, improved analytics and more.
Dinesh Nirmal, VP of development for next-gen analytics, Spark, Hadoop and site exec at IBM, met with John Walls and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, to discuss these things along with his take on the IBM-customer relationship and the importance of flexibility.
Spark is growing
Nirmal was very happy to see the number of attendees and speakers at this year’s event, mentioning that he felt as though “there’s double the number of people in the keynote.” A major reason for the growth of interest in the event, he felt, was that development and adoption are “taking off” for Spark, along with related technologies.
“Every single customer shop that I’ve gone to has an R-shop,” he said, referring to the increasingly popular programming language that is commonly used for statistical analysis and similar functions. “[R] is getting adopted in a very accelerated space,” he said.
Making it easy for users
Nirmal also noted the flexibility that IBM was striving to enact, saying, “We see that we can give a rich set of libraries that focused developers can use to build new models and have machine learning.”
That open draw for programmers and developers will lead to big things, IBM hopes. “What we envision … let’s make it flexible for the developer … let’s give the developer the choice on what language they want to use,” he said.
Addressing the various goals of statisticians, Nirmal said, IBM is asking: “How can we give the end user, the data scientist, a multitude of choices that they can pick and choose from, rather than just saying [for instance]: ‘You want to develop, use Python.’”
“When we started talking about how [IBM would] build a platform, there’s three things that we looked at,” Nirmal continued. “One, how do we build a scalable open-source-based architecture? … The second piece is, how do we differentiate? … And the last piece is … what we want to do is bring analytics to where your data is.”
This last point, termed as “house calls” for data, along the lines of a doctor making house calls, was one of the ways in which IBM will be more engaged with users looking to fully capitalize on their Spark experience.
“Those are the kinds of things that will get the buzz on top of Spark,” Nirmal said, giving an example of the system applied to machine learning while asking the question of “’how do we real-time score in a feedback loop, so that the model is learning?’” The answers, he felt, are waiting to be uncovered by the Spark users and developers.
Looking at the future of Spark and these resultant technological abilities, Nirmal was highly positive, saying: “I think there’s tremendous potential.”
#SparkSummit
#theCUBE
