>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation I am stupid a man and this is a special cube conversation we've been talking a lot of course for many years about the ascent of cloud and today in 2020 multi cloud is a big piece of the discussion and we're really happy to help unveil coming out of cell al kiram which is helping the networking challenges when it comes to multi cloud and I have the two co-founders they are brothers I have Amir who is the CEO and a DIF who is the CTO the Khan brothers thank you so much for joining us and congratulations on the launch of the company thank you sue for having us on the show it's a pleasure to see you again all right so Amir we've had you on the program your previous company that you've done was of course the fella you the two of you have worked together at I believe five companies successful companies acquired you know the most recent one into Cisco so a mirror obviously you know you know strong networking theme your brother the CTO I was going to talk to us about the engineering but give us you know just the the story of Al Kyra what you've been building and now ready to unveil to the world certainly needs to so in around 2018 timeframe we started looking into the next big problem to solve in the industry which was not only a substantial you know from the market size perspective but also from the customers perspective was solving a major pain point so when we started looking into the cloud customers and started talking to our customers they were struggling from the cloud networking perspective even in a single cloud and it was a new environment for them and they had to understand all the nitty-gritty details of each one of these clouds and when you go to multi cloud environment it becomes exponentially complicated to address not only connectivity but how to deploy services like firewall and other services including low balancers and IP address management etc and remote access so we started digging deeper into this problem and start working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as a service this time we are not shipping any hardware or software it is you know just like any other SAS application you just come to our portal I just drag and drop literally draw out your network and click on provision and you know come back after 40 minutes or so your full global cloud infrastructure is up and running so out if your brother laid out a pretty broad vision there any of us from the networking world we know there's a lot of complexity there and therefore it takes a lot of work when I want to do things simply as a service is you know a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution second let's do so we've been working both our men and myself in the networking industry for more than 25 years now and our the way we have worked and what we have believed in is that we need to solve customer problems we never believed in like doing a science project so here also we started working with customers as we have always done in the past we understood the customers pain points the challenges they were facing especially in this case and in cloud networking space multi-cloud networking space based on the user requirements users or the customers use cases we started the building a service and here what we have built is a complete network as a service it's a multi cloud met work as a service which not only provides connectivity to multiple routes but also addresses the needs for bringing in networking services as well as security services making sure that you have a full policy based infrastructure on top of it you have deep visibility into into the clouds as well as into on-premise into and visibility into and monitoring troubleshooting and all of it is delivered to you as a service so that's what we have been doing here at ELQ here excellent so when we look at multi-cloud of course you know every cloud they have some similar things they have some different things they all tend to do things a little bit differently you know one of the secret sauces that have been talked about for the last few years is ESP BAM space like you and built with Nutella to help really enable those environments so if we've got a diagram here which I think will help explain a little bit as you know we're out here it how it plugs into these different environments walk us through a little bit what we're seeing here and what you're actually doing a tell Kira so here we are building a global unifying the multi cloud Network it's consumed as a service think of it as consuming it just like you would consume any other SAS like our SAS issue so you come to lqs portal you register and then there you go and you start building your global multi-cloud unified network with integrated services so here what you see is is a Elka's cloud services exchange with comprises of cloud exchange points you can bring these up these cloud exchange points up anywhere on the globe you can decide like what networking services security services you need in these cloud exchange points you can connect the multiple clouds from there you can bring your existing on-premise connector matiee into the CX PS all these CX B's have a full mesh of overlay high speed low latency connectivity among each other so there is a full network which comes up between these CX B's and this the whole infrastructure scales with customers as as a customer scale so it's a horizontally scalable veil a very highly redundant and resilient infrastructure which we have both all right so armor now that we understand the basics of the technology you've got some strong investors including Sequoia kleiner perkins give us you know what is being announced day you're coming out of stealth where are you with the product you know how many employees you have and where are you with the discussion of customer adoption so stew we're obviously bringing this to the market and we will be announcing it on April 15th it's available for the customers to consume our solution as a service on that day so they are welcome to reach out to us and we'll be happy to help them and as a matter of fact just come to our website and register for the service and yeah we rightly said that we have a superstar team of not only the venture capital companies but also the board members representing those companies the bill Cochran and mamoon Hamid Wright who the leading VCS are on the board of our company including myself inactive all right I'm all right love to actually bring up the second slide that we have here walk us through you said you know the service you know how do people get started how do they understand you know what would walk us through what what they do so the biggest challenge when we started looking into these problems you know Stu was that it was very complicated you have to piecemeal bring up instances and the cloud and stitch them together and when you try to integrate the services that was a different challenge for the customers right so we wanted to make sure that it was so simple and clean that the customer didn't even have to think about any underlying construct on any of the clouds they should not have to worry about learning each individual power from the you know networking perspective so here's your portal you just come you know step one is come to a portal or register step two is you start drawing your network based on your intent what on-prem an activity you want to bring into this service what type of services you need like all all the firewalls and then you know what pilots you need to connect and everything happens seamlessly the from on pram pram through services into the cloud and across multiple clouds it's a seamless service that we have created and with full analytics capabilities and full governance built in alright so I'll to bring us into what this means for customers you know how do they manage it you know is this the networking team is it the cloud architects you know what api's are there how does this fit into kind of what customers are doing today and you know solve some of those challenges that we laid out earlier in the discussion yes trauma from the customers perspective it's as I said it's it's completely delivered as a service customers come to our portal they draw out the network they select the services they click on provision and the whole network comes up within minutes so the main thing here is that from a customer's point of view if they are connecting to different clouds they don't need to understand any of the underlying specifics or underlying constructs of any of the of the cloud in order to bring can I bring up connectivity so we what we are doing here is we are abstracting the clouds here so we are building a virtual cloud network so if you if you think of if you compare it with what we did in the in the previous life be virtualized the when so here would be a doing is we are virtualizing the cloud network so underlying doesn't matter which cloud you sit on which cloud you need to connect to which networking services whether cloud native services or whether you you want to consume our care services or we also support like customer bringing in third-party services as well so it's all all offered from our platform all offered is a service for to the customer again no expertise required in any of the underlying networking constructs of any of these cards give us what we should be looking at from a technology roadmap from Akira through the rest of 2020 good question as to so as I mentioned earlier our roadmap is dictated by customer requirements so we prioritize what customers need from us so we have come out with a scalable platform we have come out with a marketplace for networking services in there in the near term we'll be expanding our market place with more services we will be addressing more use cases and when I talk about use cases I can give you some examples like there's a view you not just only need connectivity into cloud you might have different requirements from from throughput perspective or bandwidth perspective or different services that you need to front-end your cloud but you may have certain applications such as internet basing application where you eat like traffic coming in from the internet inbound to those applications you might need services like a load balancer like an external load balancer in our services exchange you might also need like a firewall you might need traffic engineering or sorry service eaning capability is where you would chain service through multiple or traffic through multiple of these services like a firewall in the load balancer so we have built a platform which gives you all those capabilities going forward we will be adding more services more use cases to it we have a long ways ahead of us and we will be putting all our effort in delivering a roadmap as we go all right so Amma your technical team definitely has their hands full and uh you know robust after work on uh give us the the high-level what we should be looking for out Kira for people that are out there you know multi-cloud and networking you know tend to get talked a lot there's many big companies and some small ones what will separate al Kira from the rest of the market today and what should we be looking to see the company's progression through 2020 yeah thanks for asking that yeah certainly I mean you know from the solution perspective out it's said that you know it's so fundamentally important to have a very strong basis right and that's what we have done we are bringing out a certain number of services and now we will continue to grow on that will create a big marketplace we will continue to improve on which clouds we connect to and how and we will be building our own services in certain cases as well now building a technology is just one piece of it we have to go out to market with a company that the customers can trust every single you know the department in that company whether it's sales or how they do business with us all the business back-end pieces have to be sorted out and that's what we've been working with and you know then go to market partners that is very very important right support is very important so let me spend a little bit of time on go to market strategy we have been working with the service riders so that we can extend our reach not only to the large customers but also to midsize customers across the globe so you will see us in the future announcing major service water partnerships as well as we've been working with large sis bars and system integration in a partners and also we have taking a slightly different approach this time because it's a service so we are going with telecom master agents which have been you know working with the service providers the cloud providers the cable providers as a channel and they have a huge reach into the customer base so we we have a very comprehensive strategy not only from the go to market in the technology perspective but also how we are going to support our customers and continue to build a relationship to build a lasting company yeah I'm a super important point there absolutely we've seen the maturation and change in the service providers as today they are working with many of the public cloud providers and they're as you said a close touch point and a trusted partner of our customers all right so before I let you go you know YouTuber brothers everybody in today's day and age is spending even more time with family but you know your your situation you've worked together for a long time what keeps bringing the two of you together working together and then talk about that ball so I mean we're very close-knit family we have four brothers and one sister and obviously active and I have been the closest because we have been working together for the longest we have at least work in five different companies together our families travel together we have three daughters each we live about five minutes you know walk from each other and we you know just have this bond where we not only have you know the family close but also very close-knit friends a circle which we both hang out with and we you know obviously have common interest in the sports as well we play squash and tennis and work out so after four if they want to take a stab at it but also yeah so we've always been very close in fact we've been together for the last like ever since I can remember like even even college days he was we were roommates for for some time also he ever say we have like our circle of friends is the same also so again we're very close and we work well together so we complement each other's skills and and it's it's worked out in the past hopefully it will work out again and I look forward to working with them for many many more years to come yeah well I'm or not - thank you so much for sharing the the coming out of stealth for Al Kyra we definitely look forward to watching your progress and you know seeing how you're helping customers in this multi-cloud world thank you for joining us - thank you so much thank you for having us all right I'm Stu minimun and thank you so much for watching this special cube conversation on the cube [Music]