 from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. Welcome back to theCUBE, our continuing coverage of our event, Big Data SV. I'm Lisa Martin with my co-host, George Gilbert. We're down the street from the Startup Data Conference, hearing a lot of interesting insights on Big Data, peeling back the layers, looking at opportunities, but the challenges, barriers to overcome, but also the plethora of opportunities that enterprises alike have that they can take advantage of. Our next guest is no stranger to theCUBE. She was just on with me a couple of days ago at the Women in Data Science Conference. Please welcome back to theCUBE, Ziya Ma, Vice President of Software and Services Group and the Director of Big Data Technologies from Intel. Hi, Ziya. Hi, Lisa. Long time no see. I know, it was just two to three days ago. It was. Now I can say Happy International Women's Day. It's the same to you, Lisa. Thank you, it's great to have you here. So I mentioned we are down the street from the Startup Data Conference. You've been up there over the last couple of days. What are some of the things that you're hearing with respect to Big Data? Trends, barriers, opportunities? Yeah, so first it's very exciting to be back at the conference again. The one biggest trend or the one topic that's hit really hard by many presenters is the power of bringing the big data ecosystem and the data science solutions together. You know, we're definitely seeing in the last few years the advancement of the big data and the advancement of data science or machine learning, deep learning, really pushing forward business differentiation and improve our life quality. So that's definitely one of the biggest trend. Another thing I noticed is that there are a lot of discussion on a big data and a data science getting deployed into the cloud. What are the learnings? What are the use cases? So I think that's another noticeable trend. And also there were some presentations on doing the data science or having the business intelligence on the edge devices. That's another noticeable trend. And of course there are discussions on security, privacy for data science and big data. So that continued to be one of the topics. So we were talking earlier because there's so many concepts and products to get your arms around. Like if someone is looking at AI and machine learning on the back end, we'll worry about edge intelligence some other time. But we know that Intel has the CPU with the Xeon and then there's the low power one with Adam. And there's the GPU, there's A6, FPGAs. And then there are the software layers with higher abstraction level. Help us put some of those pieces together for people who are like saying, OK, I know I've got a lot of data. I've got to train these sophisticated models. Explain this to me. Right, so Intel is a real solution provider for data science and big data. So at the hardware level, George, as you mentioned, we offer a wide range of products from a general purpose like Xeon to targeted silicon such as FPGA, Nirvana, and other A6 chips like Nirvana. And also we provide adjacencies like networking hardware, non-volatile memory, and mobile. Those are the other adjacent products that we offer. Now on top of the hardware layer, we deliver fully optimized software solution stack from libraries, frameworks, to tools, and solutions so that we can help engineers or developers to create AI solutions with great ease and productivity. For instance, we deliver Intel optimized math kernel library that leverage the latest instructions that give us significant performance boost when you are running your software on Intel hardware. We also deliver a framework like a big deal and for Spark and a big data type of customers if they are looking for deep learning capabilities. We also optimize some popular open source deep learning frameworks like CAFE, like TensorFlow, MXNet, and a few others. So our goal is to provide all the necessary solutions so that at the end our customers can create the application, the solution that they really need to address their biggest pinpoints. Help us think about the maturity level now. Like we know that the very most sophisticated, you know, internet service providers were sort of all over this machine learning now for quite a few years, banks, insurance companies, people who've had the statisticians and actuaries and who have that sort of skill set are beginning to deploy some of these early production apps. Where are we in terms of getting this out to the mainstream? What are some of the things that has to happen? To get it to mainstream, there are so many things we could do. First, I think we will continue to see the wide range of silicon products, but then there are a few things Intel is pushing. For example, we're developing this the Navana Graph Compiler that will encapsulate the hardware integration details and present a consistent API for developers to work with. And, you know, this is one thing that we can help, we hope that we can eventually help the developer community with. Also, we are collaborating with the end user, like from the enterprise segment. For example, we're working with the financial services industry, we're working with the manufacturing sector and also customers from the medical field. And your online retailer trying to help them to deliver or create the data science and analytics solutions on Intel based hardware or Intel optimized software. So that's another thing that we do and we're seeing actually very good progress in this area. Now, we're also collaborating with many cloud service providers. For instance, we work with, you know, some of the top seven cloud service providers, both in US and also in China, to democratize not only our hardware, but also, you know, our libraries and tools, BigDL, MKL and other frameworks and libraries so that our customers, including individuals and businesses, can easily access to those building blocks from the cloud. So definitely we're working from different vectors. So last question in the last couple of minutes, let's kind of vibe on this collaboration theme. Tell us a little bit about the collaboration that you're having with, you mentioned customers and some highly regulated industries for as an example, but I'd love to understand what's that symbiosis? What is Intel learning from your customers that's driving Intel's innovation of your technologies and big data? That's an excellent question. So Lisa, maybe I can start by sharing a couple of customer use cases. What kind of solution that we help our customer to address? I think it's always wise not to start a conversation with the customer on technology that you deliver. You want to understand the customer's needs first, and then so that you can provide a solution that really address their biggest pinpoint, rather than simply selling a technology. So for example, we have worked with an online retailer to better understand their customer's shopping behavior and to assess their customer's preferences and interests. And based upon that analysis, the online retailer make different product recommendations and maximize its customer's purchase potential and it drive up the retailer's sales. That's one type of use case that we have worked. We also have a partner with the customers from the medical field. Actually, today at the Strata conference, we actually had somebody highlighting, we had a joint presentation with UCSF where we help the medical center to automate the diagnosis and grading of meniscus lesion. And so today, actually that's all done manually by the radiologist. But now that entire process is automated, the result is much more accurate, much more consistent and much more timely because you don't have to wait for the availability of a radiologist to read all the 3D MRI images and that can all be done by machines. So those are the areas that we work with our customers, understand their business needs and give them the solution they are looking for. Wow, the impact there. I wish we had more time to dive into some of those examples, but we thank you so much, Zia, for stopping by twice in one week to theCUBE and sharing your insights. And we look forward to having you back on the show in the near future. Thanks, Lisa. Thanks, Georgia, for having me. And for my co-host, George Gilbert, I'm Lisa Martin. We are live at Big Data SV in San Jose. Come down, join us for the rest of the afternoon. We're at this cool place called Forager Tasting in Eatery. We will be right back with our next guest after a short break.