>> Announcer: SiliconANGLE Media presents theCUBE! covering Alibaba Cloud's annual conference, brought to you by Intel. Now here's John Furrier.... >> Hi I'm John Furrier, with SiliconANGLE, Wikibon and theCUBE. I'm the co-founder based in Silicon Valley, California, Palo Alto, California, and I am here in Hangzhou, China for the Alibaba Cloud conference in Cloud City, it's the biggest cloud computing conference here in China. I'm excited to be here with Dr. Min Wanli, who's the Chief Data Scientist and General Manager of Big Data division at Alibaba Cloud. Dr. Wanli, thank you for spending time. >> Thank you for having me. >> We have seen a lot of data in the conversation here at the show, data technology's a big part of this new revolution, it's an industrial revolution that we've never seen before, a whole 'nother generation of technology. What does data technology mean to Alibaba? >> Okay, it means everything. So first off, our internal technical speaking, it's technology handling massive real-time data and streaming data, and that's of different variety. For instance the app for the mobile app, for system knock, the customer behavior, they click, and they click browsing of the digital image of each merchant and asking for the price and compare against another similar product. All these behaviors are translated as data, and this data will be further merged with the archived data and try to update the profile of this customer's interests, and then try to detect whether there's a good match of they current merchant with the customer intent. If the match is good, then will flash this to the top priority, the top spot. So that try to increase the conversion rate. So if the conversion rate is high, then our sales is high. So DT, data technology means everything to Alibaba. >> It's interesting, I find my observation here, it's so fascinating because in the old days, applications produced data, that was stored on drives. They'd go to data warehouses, and they'd analyze them. You guys, in Alibaba Cloud are doing something fundamentally different, that's exciting in the sense that you have data, people call it data exhaust or data in general, but you're reusing the data in the development in real-time. So it's not just data exhaust, or data from an application. You're using the data to make a better user experience and make the systems smarter and more intelligent. Did I get that right? >> Exactly, exactly. This is a positive feedback loop, in a way, so in the old-fashioned way, you archived the data for offline analysis and for post-event analysis, and trying to identify whether there's any room for improvement. But that's fine. But now people cannot wait, and we cannot wait. Offline is not enough. So we have to do this in real time, online, in a feedback version, in search of a way that we could capture exactly at the right moment, understand the intent of the customer, and then try to deliver the right content to the customer on the fly. >> Jackie Ma, or Jack Ma, your boss, and also Dr. Wong who I spoke with yesterday, talk about two things. Jack Ma talks about a new revolution, a new kind of industrial revolution, a smarter world, a better society. Dr. Wong talks about data flowing like a river, and you hear Hangzhou as an example, but it highlights something that's happening across the world. We're moving from a batch, slow world with data to one that's in motion and always real time. They're not necessarily mutually exclusive, but they're different. A data lake or a data river, whatever word you want, I don't really like the word data lake personally, I think it means, it's batch to me. But batch has been around for a while. Real time mixed streaming. This is something that's happening, and it's impacting the architecture and the value proposition of applications, and it's highlighted in Internet of Things, it's highlighted in examples that we're seeing that's exciting like the ET Brains. Can you share your view in your project around ET Brains, because that is not just one one vertical. It's healthcare, it's industrial, it's transportation, it's consumer, it's everything. >> Yeah, good question, so first of all I concur with you that data lake already exists, will continue to exist, because it's got its own value because our ET Brain for example, actually emerged from data lake, because it has to learn all the benchmark, the baseline model, the basic knowledge from the existing archive data, which is a data lake. However, that's not enough. Once you have the knowledge, you have the capability but you need to put that in action. So we are talking about data in motion, data in action. How do we do that? So once you have the training example, all the training data from data lake, and you train the brain, the brain is mature enough and in the next step you want to push the brain coupled with real-time streaming data, and then to generate real-time action in real-time manner in preemptive way, rather than posting in a reactive way. So for example, in transportation and travel, T and T, travel and transportation, and traffic management. So currently, all the authorities, they have access to real-time information, and then they do a post-event analysis if there's a traffic jam, and then they want to do some mitigation. However, the best scenario is, if you can prevent the traffic jam from happening in the first place, right, how can you foresee there will be, there would be, there could be traffic jam happen in 10 minutes from now, and then you take a preemptive strike, and then try to prevent that from happening. That's the goal ET Brain, in traffic management want to achieve. Like for example, you see the ambulance case, and once the ET Brain receives the message say the ambulance is going to go to Point A, pick up a patient, and carry that patient, rush them to Hospital B, and then it immediately calculates the right routing, the driving direction, and the calculate the ETA to every intermediate intersection and then try to coordinate with the traffic lights, traffic signal. All this systematic integration will create on demand a green wave for ambulance, but in the past ambulance is just by the siren, right. >> Yeah, this is fascinating, and also I'd like to get your thoughts, because you bring us something that's important, and that is, and I'd like to connect the dots for the audience, and that is, real time matters. If you're crossing the street, you can't be near real time, because you could get hit by a car. But also latency's important, also the quality of the data is good. I was talking to an executive who's laying out an architecture for a smart city, and he said, "I want the data in real time," and the IT department said, "Here it is, "it's in real time", and he says, "No, that's last year's data." And so the data has to be real time and the latency has to be low. >> Exactly. I completely agree. The latency has to be low. Unfortunately, in the current IT infrastructure, very often the latency exist. You cannot eliminate that, right? And then you have to live with that, so the ET Brain acknowledge the fact, in fact we have our own algorithm designed in a way that it can make a shortened prediction. So based on five minutes ago data, the data collected five minutes ago, and then it can project the next five minutes, next 10 minutes, what would be the data, and then use that to mitigate, or to conquer, to offset the latency. So we find that to be a good strategy, because it's relatively easy to implement, and it is fast and efficient. >> Dr. Wanli, fascinating conversation. I'd like to get your thoughts on connecting that big data conversation or data conversation to this event. This is a cloud computing event. We at theCUBE and SiliconANGLE and our Wikibon research team we go to all the events. But sometimes the big data events are about big data, Hadoop, whatever, and then you have cloud, talking about DevOPs, and virtual machines. This conference is not just a siloed topic. You have cloud computing, which is the compute, it's the energy, it's the unlimited compute potential, but it's also got a lot of data. You guys are blending it in. >> Exactly. >> Is that by design, and why is that important? >> It's by design. Actually, you cannot separate cloud from data, big data. Or you cannot talk big data without referring to cloud, because once the data is big, you need a huge computation power. Where does that come from? Cloud computing. So that means that data intelligence, all the value has to require a good technological tool to unleash the value. What's the tool? Cloud computing. For example, the first time IBM come up with a smart plan, a smart city, that's 2005 or 2006, around that time, there's no cloud computing yet, at the earliest emerging stage. And then we see what happens. And the smarter city and then gradually become IT infrastructure construction. But it's not DT, data technology. So they invested billions of dollars in the infrastructure level, and they collect so much data, but all the data become a burden to the government, to save, to archive the data or protect the data from hacking, right. Now, these days, if you have the cloud computing available, you can do real-time analytics to unleash the value in the first place, at the first moment you receive the data and then later on you know which data is more valuable, which data is of less value, and then you know how much you want to archive. >> Our Wikibon research team put out research this past year that said IT is no longer a department, it's everywhere, >> It's everywhere >> it supports your DT, data technology, it's a fabric. But one thing that's interesting going back to 2005 to now is not only the possibility for unlimited compute, is that now you're seeing wireless technologies significantly exploding in a good way, it's really happening. That's also going to be a catalyst for change. >> Definitely. >> What's your thoughts on how wireless connectivity, 'cause you have all these networks, you have to move data around, it has to be addressable, you have to manage security. That's a heavy load.\ what do you do, how are you guys doing that? >> Okay, very good question. We faced this challenge a couple of years ago, we realized that, because in Chinese domestic market, the users they are migrating from PC to mobile, and this create the mobile phone has wi-fi, right, so interacts with another AP, Access Point, right. So then how do we recognize our tracking, and recognizes ID identification, all this stuff, create huge headache to us, and this time, in this conference, we announce our solution for mobile, for mobile cloud. So what does that mean? So essentially, we have a cloud infrastructure product designed in order to do a real-time integration and do a data cleansing of the mobile data. I mean by mobile, and wireless as well. Wireless means even bluetooth, or even IoT, IoT solution also supported there. So this is a evolving process in the way. The first solution probably is less than perfect, but gradually, as we are expanding into more and more application scenario, and then we will amalgamate the solution and try to make it more robust. >> You guys have a good opportunity, and Alibaba Cloud certainly met with Karen Liu about the opportunity in North America and United States where I'm from. But Alibaba Cloud, and Alibaba Group, in the Alibaba Cloud has had a great opportunity, almost a green field, almost a clean sheet of paper, but you have a very demanding consumer base here in China. They're heavily on mobile as you pointed out, but they love applications. So the question I want to ask you is, and I'd love your thoughts on this. How do you bring that consumerization, its velocity, the acceleration of the changing landscape of the consumer expectation and their experience to small businesses and to enterprises? >> Ok, very good question. So user not just customer base, and the demanding customers in China trying to help us to harden our product, harden our solution, and to reduce the cost, the overall cost, and the economy of mass scale, economy of scale, and then once we reach that critical point, and then our service is inexpensive enough, and then the small and medium, SMB, small and medium business they could afford that. And in old days, SMB, they want to have access to high performance computing, but they do not have enough budget to afford the supercomputer. But these days now, because our product, our computation product, cloud product, big data product is efficient enough, so the total cost is affordable. And then you see that 80% of our customers of Alibaba, at least 80%, are actually SMB. So we believe the same practice can be applied to overseas market as well. >> You bring the best practices of the consumer and the scale of Alibaba Cloud to the small and medium-sized enterprises, and they buy as they grow. >> Exactly. >> They don't buy a lot upfront. >> Yeah, yeah, they buy on demand, as they need. >> That's the cloud, the benefit of the cloud. >> Exactly. >> Okay, the compute is great, you've got greatness with the compute power, it's going to create a renaissance of big data applications where you see that. What is your relationship with Intel and the ecosystem, because we see, you guys have the same playbook as a lot of successful companies in this open source era, you need horsepower and you need open source, what is Alibaba's strategy around the ecosystem, relationship with Intel, and how are you guys going to deal with partners? >> Yeah, first of all, so we really happy that we have Intel as our partner. In our most recent big data hackathon for the medical AI competition, and we just closed that competition, that data hackathon. Okay, very fascinating event, okay. Intel provided a lot of support. All the participants of this data hackathon, they do their computing leveraging on the Intel's products, because they do their image process. And then we provided the overall computing platform. Okay, this is a perfect example of how we collaborated with our technology partners. Beyond Intel, in terms of the ecosystem, first of all, we are open. We are building our ecosystem. We need partners. We need partners from pure technology perspective, and we also need partners from the traditional vertical sectors as well, because they provide us domain knowhow. Once we couple our cloud computing and big data technology with the domain knowhow, the subject matter expertise, well together the marriage will generate a huge value. >> That's fantastic, and believe me, open source is going to grow exponentially, and by 2025 we predict that it's going to look like a hockey stick. From the Linux foundation that's doing amazing work, you're seeing the Cloud Native Foundation. I want to get your thoughts on the future generation. >> Yeah, you mean open source? >> The future generation that's using open source, they're younger, you guys have tracked, you know the demographics in your employee base, you have a cloud native developer now emerging. They want to program the infrastructure as they go. They don't want to provision servers, they want the street lights to just work, whatever the project, the brains have to be in the infrastructure, but they want to be creative. You're bringing two cultures together. And you've got AI, it's a wonderful trend, machine learning is doing very well. How do you guys train the younger generation, what's your advice to people looking at Alibaba Cloud, that want to play with all the good toys? You got machine learning, you got AI, they don't want to necessarily baby, they don't want to program either. They don't want to configure switches. >> Yeah, very good question. Actually this is related to our product strategy. So in a way, like today we announce our ET Brain, so we are going to release this and share this as a platform to nurture all the creative mind, creative brains, okay, people, trying to leverage on this brain and then do the creative job, rather than worry about the underlying infrastructure, the basic stuff. So this is that part which we want to share with the young generation, tell them that unleash your creativity, unleash your imagination, don't worry about the hard coding part, and we already build the infrastructure, the backbone for you. And then image anything you think possible and then try to use ET Brain, try to explore that. And we provide the necessary tool and building blocks. >> And the APIs. >> And the APIs as well, yes. >> Okay, so I want to get your thoughts on something important to our audience, and that is machine learning, the gateway to AI. AI, what is AI? AI software, using cloud. Some will argue that AI hasn't really yet come on the scene but it's coming. We love AI, but machine learning is really where the action is right now, and they want to learn about how to get involved in machine learning. So what's your view on the role of machine learning, because now you have the opportunity for a new kind of software development, a lot of math involved, that's something that you know a lot about. So is there going to be more libraries? What's your vision on how machine learning moves from a bounded use case to more unbounded opportunities, because, I'm a developer, I want the horizontally scalable resource of the cloud, but I'm going to have domain expertise in a vertical application. So I need to have a little bit of specialism, and I want the scalability. So data's got to move this way and it's got to be up this way. >> Yes, yeah, okay, let me put it this way. So first off, for people who are really interested in AI, or they want to work on AI, my recommendation first of all, you got to learn some mathematics. Why, because all the AIs and machine learnings is talking about algorithms, and those algorithms are actually all about math, mathematics, the formula, and also the optimization, how to speed up the convergence of the algorithms, right. So all this maths is important, okay. And then if you have that math background, and then you have the capability to judge or to see next, which algorithm, or which machine software is suitable to solve the vertical problems. Very often the most popular algorithm may not be the right one to solve the specific vertical problems. So you're going to the way, capability to differentiate and to see that and make the right choice. That's the first recommendation. The second recommendation, try to do as many type of examples as possible, try to get your hands on, don't stop at looking at the function specification and oh, this is a function and input, output, da da da, but you need to get your hands dirty, get your hands on the real problem, the real data. So that you can have a feeling about how powerful it is or how bad or how good it is. Once you have this kind of experience, and then you do have capability, you gradually build up a cumulative capability to make a right choice. >> This is fascinating, Dr. Wanli, this is fantastic. I want to follow up on that because you're bringing up, in my mind I can almost see all these tools. There's an artisan culture coming on. You're seeing that. Dr. Wong discussed that with me yesterday. Artisans meeting technologists, scientists and creatives. UI, we're seeing evolutions in user experience that's more art. And so culture's important. But the machine learners of the algorithms, sometimes you have to have a lot of tools. If you have one tool, you shouldn't try to use tools for other jobs. So bring this up. How should a company who's architecting their business or their application look at tooling, because on one hand, there's the right tool for the right job, but you don't want to use a tool for a job that it's not designed for. To your point. Tools, what's your advice and philosophy on the kinds of toolings and when to engage platforms, relationship between platforms and tools. >> Okay, then put it this way. So, this is a decision based on a mixture of different criteria together. So first of all, from technology perspective, and secondly from the business perspective. From technology perspective I would say if your company's critical competence is technical stuff, and then you've got to have your own tool, your own version. If you only rely on some existing tool from other companies, your whole business actually is dependent on that, and this is the weakest link, the most dangerous link. But however, very often to develop your own version of the tool takes forever, and market wouldn't give you so much time. And then you need too strike a balance, how much I want to get involved for self development and how much for in-house development, and it's how much I want to buy in. >> And time. >> And time as well, yes. And another one is that you've got to look at the competitive landscape. If this tool actually has already existed for many years and many similar product in the market, and the problem is not a good idea to reproduce or reinvent, and then you're going to why not buy it, you take that for granted. And it think that's a fact, and then you build a new fact, right. So this is another in terms of the maturity of the tool, and then you need to strike a balance. And in the end, in the extreme case, if your business, your company is doing a extremely new, innovative, first of a kind study or service, you probably need some differentiate, and that differentiator probably is a new tool. >> Final question for you. For the audience in America, in Silicon Valley, what would you like to share from your personal perspective about Alibaba Cloud that they should know about? Or they might not know about and should know about. >> Okay, 'cause I worked in the US for 16 years. To be frank, I knew nothing about Alibaba until I came back. So as a Chinese overseas, I'm so ignorance about Alibaba until I came back. So I can predict, I can guess, more or less, in the overseas market, in US customers, they probably know not that much about Alibaba or Alibaba Cloud. So my advice and from my personal experience, I say, first of all, Alibaba is a global company, and Alibaba Cloud is a global company. We are going to go global. It's not only a Chinese company, for example. We are going to serve customers overseas market in Europe and North America and Southeast Asia. So we want to go global first. And second, we are not only doing the cloud. We are doing blending of cloud and big data and vertical solutions. I call this VIP. V for vertical, I for innovation. P for product. So VIP is our strategy. And the innovation is based upon our cloud product and big data product. >> And data's at the center of it. >> Data is the center of this, and we already got our data technique, our data practice from our own business, which is e-commerce. >> And you're solving some hard problems, the ET Brain's a great playground of AI opportunity. You must be super-excited. >> Yeah, yeah, right, right, okay. >> Are you having fun? >> Yes, a lot of fun. Very rewarding experience. A lot of dreams really come true. >> Well, certainly when you come to Silicon Valley, I know you have a San Mateo office, we're in Palo Alto, and this is theCUBE coverage of Alibaba Cloud. I'm John Furrier, co-founder of SiliconANGLE, Wikibon research and theCUBE, here in China covering the Alibaba Cloud, with Dr. Wanli, thanks for watching.