>> Narrator: Live from San Jose, California it's the Cube, covering Big Data Silicon Valley 2017. (upbeat music) >> Big Data SV 2016, day two of our wall to wall coverage of Strata Hadoob Conference, Big Data SV, really what we call Big Data Week because this is where all the action is going on down in San Jose. We're at the historic Pagoda Lounge in the back of the Faramount, come on by and say hello, we've got a really cool space and we're excited and never been in this space before, so we're excited to be here. So we got George Gilbert here from Wiki, we're really excited to have our next guest, he's Fred Rice, he's the chief architect at IBM Spark Technology Center in San Francisco. Fred, great to see you. >> Thank you, Jeff. >> So I remember when Rob Thomas, we went up and met with him in San Francisco when you guys first opened the Spark Technology Center a couple of years now. Give us an update on what's going on there, I know IBM's putting a lot of investment in this Spark Technology Center in the San Francisco office specifically. Give us kind of an update of what's going on. >> That's right, Jeff. Now we're in the new Watson West building in San Francisco on 505 Howard Street, colocated, we have about a 50 person development organization. Right next to us we have about 25 designers and on the same floor a lot of developers from Watson doing a lot of data science, from the weather underground, doing weather and data analysis, so it's a really exciting place to be, lots of interesting work in data science going on there. >> And it's really great to see how IBM is taking the core Watson, obviously enabled by Spark and other core open source technology and now applying it, we're seeing Watson for Health, Watson for Thomas Vehicles, Watson for Marketing, Watson for this, and really bringing that type of machine learning power to all the various verticals in which you guys play. >> Absolutely, that's been what Watson has been about from the very beginning, bringing the power of machine learning, the power of artificial intelligence to real world applications. >> Jeff: Excellent. >> So let's tie it back to the Spark community. Most folks understand how data bricks builds out the core or does most of the core work for, like, the sequel workload the streaming and machine learning and I guess graph is still immature. We were talking earlier about IBM's contributions in helping to build up the machine learning side. Help us understand what the data bricks core technology for machine learning is and how IBM is building beyond that. >> So the core technology for machine learning in Apache Spark comes out, actually, of the machine learning department at UC Berkeley as well as a lot of different memories from the community. Some of those community members also work for data bricks. We actually at the IBM Spark Technology Center have made a number of contributions to the core Apache Spark and the libraries, for example recent contributions in neural nets. In addition to that, we also work on a project called Apache System ML, which used to be proprietary IBM technology, but the IBM Spark Technology Center has turned System ML into Apache System ML, it's now an open Apache incubating project that's been moving forward out in the open. You can now download the latest release online and that provides a piece that we saw was missing from Spark and a lot of other similar environments and optimizer for machine learning algorithms. So in Spark, you have the catalyst optimizer for data analysis, data frames, sequel, you write your queries in terms of those high level APIs and catalyst figures out how to make them go fast. In System ML, we have an optimizer for high level languages like Spark and Python where you can write algorithms in terms of linear algebra, in terms of high level operations on matrices and vectors and have the optimizer take care of making those algorithms run in parallel, run in scale, taking account of the data characteristics. Does the data fit in memory, and if so, keep it in memory. Does the data not fit in memory? Stream it from desk. >> Okay, so there was a ton of stuff in there. >> Fred: Yep. >> And if I were to refer to that as so densely packed as to be a black hole, that might come across wrong, so I won't refer to that as a black hole. But let's unpack that, so the, and I meant that in a good way, like high bandwidth, you know. >> Fred: Thanks, George. >> Um, so the traditional Spark, the machine learning that comes with Spark's ML lib, one of it's distinguishing characteristics is that the models, the algorithms that are in there, have been built to run on a cluster. >> Fred: That's right. >> And very few have, very few others have built machine learning algorithms to run on a cluster, but as you were saying, you don't really have an optimizer for finding something where a couple of the algorithms would be fit optimally to solve a problem. Help us understand, then, how System ML solves a more general problem for, say, ensemble models and for scale out, I guess I'm, help us understand how System ML fits relative to Sparks ML lib and the more general problems it can solve. >> So, ML Live and a lot of other packages such as Sparking Water from H20, for example, provide you with a toolbox of algorithms and each of those algorithms has been hand tuned for a particular range of problem sizes and problem characteristics. This works great as long as the particular problem you're facing as a data scientist is a good match to that implementation that you have in your toolbox. What System ML provides is less like having a toolbox and more like having a machine shop. You can, you have a lot more flexibility, you have a lot more power, you can write down an algorithm as you would write it down if you were implementing it just to run on your laptop and then let the System ML optimizer take care of producing a parallel version of that algorithm that is customized to the characteristics of your cluster, customized to the characteristics of your data. >> So let me stop you right there, because I want to use an analogy that others might find easy to relate to for all the people who understand sequel and scale out sequel. So, the way you were describing it, it sounds like oh, if I were a sequel developer and I wanted to get at some data on my laptop, I would find it pretty easy to write the sequel to do that. Now, let's say I had a bunch of servers, each with it's own database, and I wanted to get data from each database. If I didn't have a scale out database, I would have to figure out physically how to go to each server in the cluster to get it. What I'm hearing for System ML is it will take that query that I might have written on my one server and it will transparently figure out how to scale that out, although in this case not queries, machine learning algorithms. >> The database analogy is very apt. Just like sequel and query optimization by allowing you to separate that logical description of what you're looking for from the physical description of how to get at it. Lets you have a parallel database with the exact same language as a single machine database. In System ML, because we have an optimizer that separates that logical description of the machine learning algorithm from the physical implementation, we can target a lot of parallel systems, we can also target a large server and the code, the code that implements the algorithm stays the same. >> Okay, now let's take that a step further. You refer to matrix math and I think linear algebra and a whole lot of other things that I never quite made it to since I was a humanities major but when we're talking about those things, my understanding is that those are primitives that Spark doesn't really implement so that if you wanted to do neural nets, which relies on some of those constructs for high performance, >> Fred: Yes. >> Then, um, that's not built into Spark. Can you get to that capability using System ML? >> Yes. System ML edits core, provides you with a library, provides you as a user with a library of machine, rather, linear algebra primitives, just like a language like r or a library like Mumpai gives you matrices and vectors and all of the operations you can do on top of those primitives. And just to be clear, linear algebra really is the language of machine learning. If you pick up a paper about an advanced machine learning algorithm, chances are the specification for what that algorithm does and how that algorithm works is going to be written in the paper literally in linear algebra and the implementation that was used in that paper is probably written in the language where linear algebra is built in, like r, like Mumpai. >> So it sounds to me like Spark has done the work of sort of the blocking and tackling of machine learning to run in parallel. And that's I mean, to be clear, since we haven't really talked about it, that's important when you're handling data at scale and you want to train, you know, models on very, very large data sets. But it sounds like when we want to go to some of the more advanced machine learning capabilities, the ones that today are making all the noise with, you know, speech to text, text to speech, natural language, understanding those neural network based capabilities are not built into the core Spark ML lib, that, would it be fair to say you could start getting at them through System ML? >> Yes, System ML is a much better way to do scalable linear algebra on top of Spark than the very limited linear algebra that's built into Spark. >> So alright, let's take the next step. Can System ML be grafted onto Spark in some way or would it have to be in an entirely new API that doesn't take, integrate with all the other Spark APIs? In a way, that has differentiated Spark, where each API is sort of accessible from every other. Can you tie System ML in or do the Spark guys have to build more primitives into their own sort of engine first? >> A lot of the work that we've done with the Spark Technology Center as part of bringing System ML into the Apache ecosystem has been to build a nice, tight integration with Apache Spark so you can pass Spark data frames directly into System ML you can get data frames back. Your System ML algorithm, once you've written it, in terms of one of System ML's main systematic languages it just plugs into Spark like all the algorithms that are built into Spark. >> Okay, so that's, that would keep Spark competitive with more advanced machine learning frameworks for a longer period of time, in other words, it wouldn't hit the wall the way if would if it encountered tensor flow from Google for Google's way of doing deep learning, Spark wouldn't hit the wall once it needed, like, a tensor flow as long as it had System ML so deeply integrated the way you're doing it. >> Right, with a system like System ML, you can quickly move into new domains of machine learning. So for example, this afternoon I'm going to give a talk with one of our machine learning developers, Mike Dusenberry, about our recent efforts to implement deep learning in System ML, like full scale, convolutional neural nets running on a cluster in parallel processing many gigabytes of images, and we implemented that with very little effort because we have this optimizer underneath that takes care of a lot of the details of how you get that data into the processing, how you get the data spread across the cluster, how you get the processing moved to the data or vice versa. All those decisions are taken care of in the optimizer, you just write down the linear algebra parts and let the system take care of it. That let us implement deep learning much more quickly than we would have if we had done it from scratch. >> So it's just this ongoing cadence of basically removing the infrastructure gut management from the data scientists and enabling them to concentrate really where their value is is on the algorithms themselves, so they don't have to worry about how many clusters it's running on, and that configuration kind of typical dev ops that we see on the regular development side, but now you're really bringing that into the machine learning space. >> That's right, Jeff. Personally, I find all the minutia of making a parallel algorithm worked really fascinating but a lot of people working in data science really see parallelism as a tool. They want to solve the data science problem and System ML lets you focus on solving the data science problem because the system takes care of the parallelism. >> You guys could go on in the weeds for probably three hours but we don't have enough coffee and we're going to set up a follow up time because you're both in San Francisco. But before we let you go, Fred, as you look forward into 2017, kind of the advances that you guys have done there at the IBM Spark Center in the city, what's kind of the next couple great hurdles that you're looking to cross, new challenges that are getting you up every morning that you're excited to come back a year from now and be able to say wow, these are the one or two things that we were able to take down in 2017? >> We're moving forward on several different fronts this year. On one front, we're helping to get the notebook experience with Spark notebooks consistent across the entire IBM product portfolio. We helped a lot with the rollout of notebooks on data science experience on z, for example, and we're working actively with the data science experience and with the Watson data platform. On the other hand, we're contributing to Spark 2.2. There are some exciting features, particularly in sequel that we're hoping to get into that release as well as some new improvements to ML Live. We're moving forward with Apache System ML, we just cut Version 0.13 of that. We're talking right now on the mailing list about getting System ML out of incubation, making it a full, top level project. And we're also continuing to help with the adoption of Apache Spark technology in the enterprise. Our latest focus has been on deep learning on Spark. >> Well, I think we found him! Smartest guy in the room. (laughter) Thanks for stopping by and good luck on your talk this afternoon. >> Thank you, Jeff. >> Absolutely. Alright, he's Fred Rice, he's George Gilbert, and I'm Jeff Rick, you're watching the Cube from Big Data SV, part of Big Data Week in San Jose, California. (upbeat music) (mellow music) >> Hi, I'm John Furrier, the cofounder of SiliconANGLE Media cohost of the Cube. I've been in the tech business since I was 19, first programming on mini computers.