What's your on looking forward to this all-star minor final this coming weekend and clone us against area Suppose what's it like and in the camp this week? How's these young men preparing for it? Now we're looking forward to knowing as was everybody's kind of well This was a bit of excitement now and as was maybe nerves and the build up now And this was the last week now leading up to there Maybe runs a wee bit slow everybody's waiting for just to get the game on yeah And Antrim and Monaghan were the two sides that you had to defeat to progress and I suppose two types of Games and one of the matches you had to come from behind against Antrim and the other game You were you had the game on at halftime, but but let Monaghan slip back and it I suppose there were two different games, but there were two distinctive different teams I suppose a lot of people would have thought that we were going up to beat Antrim up in Corrigan Park, but we always knew that was going to be a huge task for us because Antrim had already played in the league or in the championship and they were playing at home and They knew that they were up for the game, but we had a big task that day and we went up and we put up a good performance Particularly as was in the latter half of the second half and things didn't go for well first area in the game They're particularly the south the second half where we lost two men two of your best players like in Jason McGee and On McGee again, and I suppose we're five eleven minutes to go We're five points down, but the short great character and short great heart and they came back Yeah, I had to show character as well and the Monaghan match because Monaghan had us on the record one states early in the second half Well, it's funny because for the few games we played both that we played your own in the league final And we went behind and then Diane Menace like and we played As I said, we played Antrim there and we're five point down 11 minutes to go again against Monaghan at no time to ever believe that they're bother We just have great faith in them. Now, you know, they're great glass They're great hunger in them, and they've great belief in them Yeah, so it's supposed to say now how strong their character there is within this this group of players that I Yeah, well, I suppose It's like anything they have a lot of work done like you know And they know what they're doing know what they're about and it was last day against Monaghan It's a bit difficult like you're eight points up particularly a young lad like they think the game's over at halftime It's not as place to be expected to be We knew Monaghan are very good side We also knew Monaghan were going to come at us throw everything at us at some stage in the game And then started the second half they did that and the bit right up to us But the short little lad showed great belief and showed great character and came back and finished out well in the end Has conceding goals or worry for you? Nobody likes to concede goals We certainly don't like to concede goals and something we try to guard against and we were disappointed with a couple of goals That we did particularly the goals we scored or we conceded the last day against Monaghan But we like to score goals and we don't like to concede them Let's look at the the the advantages that the Dunnegall have had through the this this championship and Some of the key areas and in the parkways have been performing very very well The last day midfield was very impressive Jason and Cure have really stepped up to the pit That's Jason McGean and Cure and Gallagher and they've turned out to be a formidable force in them in the middle of the field Jason and Cure and they're two fabulous players They're great athletes, but to them have both very good hands now And they're they're I think last day they're Cure and give an exhibition of how you're feeling Jason as well as the feeling scored one two from play like it's massive like for any team to have that and It was the two lads the last day It's other lads other days that stand up like their tickets that are the main players and like they said mention Naila Donald and the previous day against Antrim score 2-5, you know was a fabulous contribution Aiden McGean has done it in the league final against Tarone and JD got us the draw against Monon in the league So there's players every day and there's players all over the park like everybody contributes hugely like it's not one or two Players they all play their part and they do that particularly well the last day And they're very much into the team effort. There's no selfishness about these players And like you can be a star forward for a county as a young man Like what Naila Donald is and he scored 2-5 the first day and he's got you back in it But he played very much a team role against Monahan in the second game He scored four points, but how his contribution was was vital so it was well I suppose that's one of the things that we have laid down right from the start that there's Immensely important that it is a team that no one individual is going to do it no matter How good he is and no one player will carry the team as a team effort as a team every day They go out and not only the players that the play that get a chance to play the 15 and the subs that come on But all the whole panel everybody has to contribute every night a train in and that's the only way it can be and for us to be successful That's the only way it will be and they have done that and they bought into it every single one of them So I suppose that's where we are where we are and another thing that that stood out particularly from very early on Sean and the game against Monahan was the half-backline I think the entire half-backline scored before I don't go before anybody else in the pitch had managed to get one Yeah, well, that's I suppose that that's part of the character of the team as well We encourage all the players to go forward and driving forward particularly from half-backline and particularly Yes was JD and Aiden have been very instrumental in doing that They're two very strong players and Oren as well going forward I think Oren has scored consistently nearly in most games going forward But they're the three very good players and as opposed to players that we looked at that may come in their ways They must also be very good. Yeah, let's talk about your opposition Derry. What's what do you know of them? They are the defending champions and they're going into this game like yourself There's what four or five players there from from last year in the squad which went on which were involved in an Ulster campaign But what do you know about them? How have they evolved over over the years? That's going to be the toughest challenge you're you're going to make today I would expect so it's an Ulster final So we're going to expect that or that Derry are going to be at their best now They are a very good side. We played them now and we beat them actually in Celtic Park on the league But that will count not for nothing and I've seen them playing three or four times now already This season I've seen them playing twice in the championship against Tyrone and against Armagh and they've been very very impressive They've a lot of very very good players and particularly the two men inside are very good They've said only from last year playing centre-back or sweeper they played sweeper against Armagh the last day excellent player I think they've made it 11 players still left from last year, which is immense And as you said they're going on as defending champions champions, so they're not going to give it up easy They're certainly not and I suppose having the experience of last year of being an Ulster and occasionally I guess won't really phase them up because when you're dealing with young men When you're dealing with young men and situations arise it can affect them in different ways So it can but with Derry being there and having experienced it and having those handful of players It's a situation you feel will not be phasing them Absolutely and as well as that there Derry have already played in the Champions you've been clonus Which is in front of a big crowd, which is also probably a help to them, but listen we're Hoping that when we get our players out in the field If the players are there and they're fit and they're not carrying any knocks and they're ready to go That the lads will give it a give it their best shot How do you deal with what's the occasion with what's the young boys? How are you going to keep them grounded now for these days going on dead? We like to keep it kind of relaxed and have the crack with the lads like you know But keep them focused they're really focused and that's important for us that we keep them really focused now between now and the weekend They're focused all year and what they're doing and They know what the job they want to do and they know what we have to achieve and they want to be champions and It's already been in the final but no good being in the final this minute Just moving away from the final looking at the group you have It's been fantastic to see the development of football at underage level in recent years in Donegal And yes, there would be a lot stemming off what we've seen from 2011 and 2012 the success that the senior team had But one thing we do note is then and we've seen it over the last couple of years that Football does have a bright future in the county when we see all these young players coming through the minor ranks Well absolutely like there is a huge amount of work going on at every level all over the county Like there's 40 clubs in the county and there's an awful lot of work going on at underage the work that's going on in schools And then the development squads the development squads are excellent from under 14 up One of the things there's both that was causing a difficulty for Donegal for minor teams pushing on was no under 17 competition But since the team Jim McWigan Competition was introduced. Donegal have bridged that gap because Donegal teams or schools Predominantly don't be playing in ulcer colleges football, but that with the Jim McWigan now in the under 17 competition now There's no break between under 16 and minor and that's why I think mainly the Development in the last number of years Donegal have been successful at minor level And I know there's a huge amount of work goes on with yourself and the back room team and the way Underage football training regimes and what has now become part of the standard that you have to go through It takes up a lot of time. Wasn't it sure? Yeah, absolutely But that's the commitment you give you know what it takes and you know what it's going to take And you have to be prepared to give that like you have to buy ended right away And you're when you're looking for everybody else to buy into what you're doing You have to be prepared to get the commitment and it is like it's immense like a we've been out three times a week Since the start of the year and as well as the lads would be doing There's dent the condition programs like three or four times a week as well like you know one That takes a huge amount of time particularly the lads than the minor lads that are doing that even certain That was a huge task for them, but thankfully they've come through it now It's through the exams they've been focusing on on football for the last month or so Do these young men understand how important it is to one and an Ulster title at minor level? Oh, they understand You know, it's it's been they understand for what about it's one of the things I suppose we look to reiterate with them right away like what it is to be a county minor footballer What it is to represent your your county and the pride and honor it is to do that and I think they will they know What this is what it is and finally Sean How beneficial will to be to begin under the all Ireland series as Ulster champions and a couple of weeks time. Well, that's the only way we want to be going today We're looking forward to that and we want to be going in as champions Obviously, if you're gonna challenge you playing cork if you want to run it up you meet and carry both are formal teams But you want to be meeting cork? Yeah, and Outside of the Solstice competition. Who do you see as the counties that would really really built out the moment? It's hard to know Call me have a very impressive team The one last week a bit almost scores against Mayo and we play Mayo a couple times in the chat and challenge matches this year They were a very good side and Galway beat them well at the weekend And I also know that carry we've done watch carry and cork both are very very good sides particularly carry our excellent side and An old leasing clear now Will be two formal routines in the denser as well. Yeah, listen Sean money Thanks for coming into the studio to talk to us today And all we can do is wish you the best look on the final this weekend to you and the lads and hopefully when we're talking next to Be Ulster champions. Thanks very much. I appreciate it. Thanks


I am Chris Hogan and you're watching time to football. What's up everyone in this video? We're gonna look at five teams that Levy on Bell could play for in 2019 but before we get into that makes you guys hit that subscribe button down below to stay up to date Whenever we come out with future videos, but these five teams have a realistic shot of landing Levy on Bell and free agency I wanted to put specific teams down like the Chiefs or the Buccaneers teams that need running back help But the reality is they just don't have enough cap space to make it a possibility to sign Levy on Bell instead these five teams have a good shot of landing him starting off with team number one the Indianapolis Colts This shouldn't shock you if you follow the NFL's Instagram page Bell has been leaving cryptic comments underneath NFL posts that are related to the Indianapolis Colts all of last season The signing in itself makes a lot of sense though The Colts already have some pretty good running backs like Marlon Mack or Naheem Hines Getting someone of Bell's caliber will be beneficial and making a deep postseason run Another team Levy on Bell could play for the New York Jets The Jets have the second most cap space available right behind the Colts New York had his fair share of struggles last year on the ground There is some concern from the Jets front office about his weight gain from not playing last season But give him a full off-season program from the Jets and that should get him up and running Literally our third team on the list the Houston Texans It's safe to say that the Texans aren't sold as Lamar Miller being the featured back in Houston Even though Miller technically made the pro bowl He was like the sixth alternate and he's said to become a free agent in 2020 this is the prime time for the Texans to make a push for Levy on Bell as they're featured back Next up we have the Oakland Raiders Martian Lynch is set to become an unrestricted free agent Though it's understood that he would like to play the Raiders last year in Oakland before he thinks about retiring for good If Oakland can use their 69 million dollars to pursue Bell and Resign Lynch at a hometown discount to back him up Then Oakland would resolve a major flaw in their offense and our last team the Baltimore Ravens This would be a nightmare for the Steelers But the reality is that Baltimore has a good shot at landing Bell in free agency It would take a lot of sacrificing on Baltimore's end since the right around the league average in cap space But Bell would be tempted to sign with a team that runs the ball 70 percent of the time And That'll bring us to the end of our video But you can stay connected with us if you subscribe to this channel just hit the time of football logo That's gonna pop up right over here.  I don't know what's Well, I just just just click on my face and subscribe to this channel Duff man can't breathe. Oh, no


Hello, everyone, and thank you for being here with me. My name is John, and I am a Principal Engineer at GNI. And I'm here to present the presentation called ML Ops for Python and ML Engineers. Please let me share some words about our company, GNI.  We are around a little bit more than 50 members in the team. And although we are distributed around the globe, our main offices are in Germany and China. So just check our website, and you will see exactly what we do. So let's go through the outline of this presentation. First, I want to get an overview of the ML in ML Ops. So kind of discuss what are the use cases, challenges that all these deep learning powered applications have, what motivates us to build what we are building. Then we are going to have an overview about this ML Ops platform that Gina provides. Some of these names will make more sense as we go through the presentation. So we are going to present Dockerade and Gina. We are going to see how we provide observability, how we go in our journey through to the cloud with Kubernetes, and then our last part of the stack that is JCloud. And at the end, we are going to have the typical conclusions that we have in our presentation. So let's see, what are the typical use cases that right now with these deep learning models we can see? So we can have generating embeddings, neural search, all the generative AI, LLMs that are kind of a subpart of these generative AI, and all these umbrella term of multimodal AI that has appeared now. So embeddings are the foot of many of the main foot and source of force for many downstream tasks in AI. So what is an embedding is a vector representation of any object. It can be text, it can be images, audio, whatever. And we just try to represent in a vector. And the job of the model is to make sure that this model, that this vector has some meaning, so semantic meaning. When it comes about representing neural search, it means that similar objects are close to each other. When it comes to generative AI, it means that at the coding step, they represent nice pictures or nice generated content, whatever, then we see, for instance, generative AI, this is something that everybody has been astonished in the last years of how amazing pictures have been drawn by just giving prompts like this.  But all of these kind of, it comes to a new trend of AI, that is this multimodal AI we like to name, where kind of text, images, videos, audios are inter, they interchange the roles they play together. I'm sure that at some point, or already there is a model that given an image will create a song for you that is inspired by this image. So one thing that is worth mentioning is that for building these applications and working with these models, Python is by any, is good or bad, but it's the lingua franca for these applications. Most of the deep learning frameworks are written in Python. Python is by far the most popular language for these ML practitioners. And although Python has some lack of performance, it's worth mentioning that a lot of the problems are being solved, for instance, Python is going to a more strongly typed frameworks, and most of the work is actually done in other low-level languages, and Python is only the binding part. I think there was a talk about this before, where a Rust library was bound to Python via some bindings. So most of the problems are actually kind of hidden and messed away. So what is a typical workflow of when we try to deploy or serve or build these projects? So first we need to train the model or create a POC. Sometimes we don't even need to train the model because all these already provided models out of the shelf can serve our application properly. We just have to create the POC to make sure that we have the results that we expect, right? Then we need to serve this model because very rarely you will only use it yourself. You want to serve it to your clients or to your colleagues or whatever. You would use a serving framework, ASGA, then we might want to have observability to make sure what goes well, what goes wrong, and then at some point you might want to deploy to the cloud whenever you need scaling. You just don't want to run it yourself. You have more needs, right? As you can see, well, I think that there is a trend in these MLOps, like as long as you go to this ladder, you need less ML skills and more ops skills and the other way around. So I want to present this MLOps framework that in Gina we have that tries to help the ML engineer to go through these steps in the most smooth way we can. Okay, so I have presented the MLOps platform like this. So we have two main components that are Docker and Gina that you see at the center and I want to emphasize that Gina and Dockerade don't do it everything on their self. We don't reinvent the wheel. So we somehow integrate, use, make it easy to work with a lot of these technologies that are already awesome to work with. We make it easier to work with PyTorch or TensorFlow. We rely heavily on PyDantic, GRPC or Fast API. We make sure to use OpenTelemetry, Kubernetes, to ease the journey to the cloud, for instance. Okay, so first I want to see, to present Dockerade that is the component that we expect to make your life easier when you build your POCs locally, okay? So it's worth to mention that it is a project that we developed and we donated to LFHI, I think, back in November last year, but time flies. So basically it's a Python library for representing, sending and indexing multimodal data. So it's focused to represent this multimodal data and to put the embedding at the center. As I said before, embeddings are like the food that every downstream application eats for breakfast. So we want to make it easier for you to work with these embeddings. And there's this new component of the MLS stack that appeared in the last years that are the vector databases. So we make it easy for you to index this data and to retrieve this data from these databases in a seamless way so that you can change your backend very easily, so on and so forth. So it might make sense better later when I show some code snippets, but Docker is modularized in kind of four models. There is the data representation where we have the base document. We have some containers where kind of we group efficiently and it lets you work with groups of these documents. Then we have a typing module where we kind of have helper methods for you to work with multimodal data, video things, audio things so that you work more efficiently. And then we have document index where we allow you to get these docs and index them in vector libraries or vector databases where we have HNSW, just in memory. We support VV8, Quadrant, Elastic Search, so on and so forth. So the data representation module, it has the base doc. It's the main cops inside of the array and one can think of it kind of is that it depends, the array depends a lot on pedantic. So you can see it as a pedantic base model on steroids, so to speak. It helps to represent multimodal data and it's ready to be serialized into JSON, into protobuf, into bytes, and so on and so forth. And there are some kind of predefined documents that we have built for you to work easily with multimodal data. Then for typing, it's just a set of classes and helpers that you can enjoy for better, we will see later. And then for containers, we have two set of containers to work kind of in two modes. We have the dog list that is kind of, it behaves like a Python list and it is intended to be served, to be used kind of in inference workloads, kind of it just, it's kind of a role-like structure and we have the dog back, I'm not a fan of this name, but it's kind of a columnar oriented structure where tensors are grouped better together so that it's more thought for training applications. Okay and document index as I said is where we get the integrations of all these vector databases. But this makes little sense out of the box unless you see the code. So let's see it more or less in action.  We are gonna have an encoder class that has an encode method. Let's see what are we need here. So we do the imports from the career. We import from the main modules the base doc and doc list and we import from this typing a torque embedding class that is gonna help us work with embeddings that come with Torch. Okay then we define our types, kind of similar to Pydantic, we define our text and we say that our output is a Torch embedding of the dimension 384. This is gonna help us validate the dimension of the embeddings which is something that is very often quite a struggle to work with. Then we just wrap our model and we expose the model in a method, I mean just regular programming and we just use it and we get an embedding out of it. Then we can go and expand it to build a new research application. For this we need an indexer in this case. So in this case we import from the index part we import a method that is gonna help us index and retrieve documents from memory, kind of without the scaling or anything without any extra work. Then we get our results. As you see the results here is very nested because kind of the result you want the object that was the query and the results as matches. So it allows you to get this complex representation. You build indexer, you wrap the index and you build an index and search method combining with the encoder before we get an application that given an input matches whatever was indexed, it's quite a toy example. Or something that is more fancy, the generative AI let's build something that given a text will give us an image from that prompt. Kind of the same story. Here we use an image doc because it's gonna help us work with images. We wrap the method, we generate images, we call it and voila. Okay, so far doc array has not made a lot of work so it has helped us reason about our data, model our data in a very nice way and so on and so forth but what we mentioned before that is ready to be sent with Protobab, with JSON and so on is gonna shine now that we are gonna go to the next step. We're gonna use, try to use Gina to serve these applications, okay? So first I want to say what is Gina? Gina is a framework, it's an open source framework that is used to serve models and applications to the network via gRPC, HTTP or WebSocket. Basically it's consuming doc arrays in and doc array outs as structures, okay? It allows to do batching, it scales them, it has a Pytonic and Jamel interface and it makes everything ready to go to Kubernetes and to scale. It is also exposing telemetry data, tracing and so on for you to make it, for making your life easier. Okay, Gina is a structure in two basic layers. So we have the serving layer where we, the object executor leaves. This is the main layer, the main object that will always be there. This is the one that consumes doc arrays and it leaves in your Docker container in your process wherever and it processes doc arrays and it does changes and it serves via the network. Then we have another layer that is orchestration layer where we have deployments and flows. This is a layer where we configure the executors and we decide how these executors are gonna be served. But it's important to mention that these orchestration layer, the objective is that eventually it goes away and we let Kubernetes take over. So we have built this kind of simple orchestration layer to serve some use cases. But at some point if you want to go to the cloud we will keep the executor, we will keep all these serving part that consumes doc arrays in and out. But we are gonna let Kubernetes orchestrate it for us because we don't want to reinvent the wheel. Okay, so the executor, we will see it in action later but it's just a class, kind of you just inherit from a class and it allows you to expose these applications to the network. It allows you to configure for batching, dynamic batching. It allows to expose metrics and spans and whatever. And in combination with the orchestration layer it is served and scaled efficiently. In the orchestration layer we have deployment that is an object that configures how the executor serves data. And it also makes sure that it is scaled as it's desired. So it's kind of a simple orchestration layer in terms that it makes sure that the executor is alive at the beginning. It makes sure that the resources are cleaned at the end. But it cannot dynamically scale. It cannot dynamically under scale. So that's why I say that in the future at the next stage we will want Kubernetes to take over this job and it's important that it has a Kubernetes translation to YAML that we will see later. Flow has the same API as a deployment but it's intended to work with for microservice architectures where you need more than one model or more than application and work together, scale differently and so on. Okay, let's see it in action. So let's see how we can serve a single model. For instance, the generative AI application that we had before. Okay, this is the code that we said that just works with the query. And by doing this couple of changes we are gonna be able to expose it and serve it. Okay, let's take a look at the changes. So we import from Gina this executor and requests. We make sure that our class inherits executor so that it becomes an executor and is ready to be served. We have these decorator requests and we say that it's gonna be serving in this generate endpoint. Then we are gonna use a deployment to make sure that we serve this application. We are gonna use it to protocols, GRPC and HTTP and we're gonna serve in these two ports. It's important to note that these two protocols are not too rapid because they are two servers sharing the even loop and serving both in GRPC and HTTP. You even have swagger UI and so on to make sure that you have a rich documentation of your API. And then also it's important to say that this deployment as well as you can play in Python you can play with it in German. And then you just call it, we have a client that you can call it with GRPC or HTTP. And the same way you have worked with your class before, now you can work with it remotely and get the same results. Then when the flow comes in is when you want to orchestrate two or more executors together. Like in the case where we had neural search, I don't know if you remember, we had an encoder and we had an indexer. So in the encoder we are gonna do the same. We're gonna import executor and request and expose it the same for the indexer. In every endpoint we are making sure that every function is exposed to the network as an endpoint. And here we are telling a flow that it will listen on these ports. Also will expose the same protocols. And we are telling them that they will use an encoder that at index it will use the encode method. At search it will use the encode method that it will use two replicas and we are gonna then use the indexer. What is the point here is that you can scale differently your first executor, the encoder and the indexer. And as the same way as deployment, it's kind of a compositional pattern where a flow is just a composition of deployments. It also has a YAML interface. And the same way we can call it and have the same result. So many of you, I guess, might wonder what is the difference? So this, everything that I talked about might remind you about FastAPI and Palantik. And you might wonder what is the difference? I here put a versus but it's not a versus because we are very inspired and we are glad to say that we are kind of friends with FastAPI and Palantik. So they are awesome projects. The main difference I would say, and we are very inspired, the main difference I would say is that we are kind of more restricted in terms that we are more designed for machine learning use cases. Well, wherever these frameworks are really more general and are thought for any software application. Then also we put this embedding at the center and we use protocol, like we are able to use GRPC for communication which is important to serialize centers and so on. And also what is different is what it will come later is this orchestration layer and how we can help you move these applications that look like a FastAPI and Palantik application to the cloud and to telemetry and so on. Okay, so let's see how we use, how we expose in Gina traces and metrics to add observability to your services. Okay, so basically Gina emits automatically if you configure it to do so, traces and metrics and the open telemetry SDK, we expose it so that you can consume later by different services like Prometheus or Grafana or any other service of your choice that can work like that. So basically it's as easy as telling in your YAML applications that you want to expose metrics and tracing and where do you want them to be exposed for you to consume later. And it exposes the typical metrics that you would expect from these applications like what is the request time, how many requests have succeeded, how many requests have failed, so on and so forth. And for traces, it's especially important for flow when you have the typical spans for each microservice. But the thing is also that we expose a easy Python IPI to expose and to enrich this data with your own monitoring logic. So for instance, you could use custom metrics via the corrector or via context manager to make sure that you can find the access to these applications. For instance, what am I spending more time in the preprocessing step of my executor in the actual inference or whatever. This is for you to build this logic. And for tracing, it's the same. It's a little bit more complex because spans are a little bit more complex, but you can add spans of your request as you wish. And then you can consume in your favorite backend like Prometheus or Gafana. And then, as I mentioned before, one of the objectives is that once you have built your POC, you are able to serve your model via an API. You have make sure that you have observability in your service. At some point, this is not enough and you might need auto scaling, you might have a lot of needs or you might not want to run it locally and you want to deploy to the cloud. So how we deploy services to the cloud? I want to make a first recap about what is the cloud more or less. So the cloud is someone else's computer as we often hear. It just happens to be that someone else is normally a cloud provider, right? These cloud components normally have Docker containers as their first class citizen. So more or less what is a process in your host can be more or less logically mapped to a Docker container in the cloud. Most of, at least, Gina in the platform, but a lot of components are relying on Kubernetes to make this journey to the cloud in an easy way. This cloud has challenges and benefits that comes from scaling, auto scaling, exposing the service, isolation, resource management and so on. And something that is especially important for AI workloads that is the serverless capacity because resources are expensive, especially GPUs and so on. So what is Kubernetes? I don't know what is the knowledge of Kubernetes here. So Kubernetes is basically a container orchestration platform. It is important to say that it's open source and has a large community which makes it cloud agnostic. The community has made sure that every cloud provider can support this so you can run it in Google Cloud, in AWS and whatever. What is an orchestration platform? Something that I like to think about of Kubernetes is kind of like if you had the perfect DevOps engineer that could just go in every note of your cluster and check if the service is up every 10 seconds or whatever, so this is super powerful. So Kubernetes is this super awesome engineer that every 10 seconds or whenever you configure, it does this task. So if you know how to configure, it's super powerful. Okay, what is, and how does Kubernetes work? So Kubernetes has a set of objects that they call custom resources and they work in a declarative way. So what you are saying to Kubernetes via YAML, you're gonna say, I want this object to be in this state. And Kubernetes is gonna try at its best to make sure that that object ends up at this stage. It does it with their main objects, the objects that are native to Kubernetes. These are pods, deployments and services and with custom resources that anyone can manage. So what in the architecture point of view, it has nodes where you want to scale your application and distribute your applications. Every node has this kubelet, the kind of a demon, the same way that you have a Docker demon in your local when you talk to the, when you have a Docker command that you talk to your Docker demon. Kubernetes nodes have this small demon kubelet that makes it available for this control plane that is kind of the main node and the manager of everything to communicate to it. So to make sure that it schedules the actual workloads, containers and pods in the right places. And in this control plane is where your CLI or your SDK, everyone talks to this control plane and there is the scheduler that makes sure that everything is scheduled to work whatever it is intended to do and the controller manager where you can write your own operator to manage your own objects. So how Gina employs Kubernetes to do the, to do this, to take over the orchestration part. So basically what we do is translate these deployments and flows in a quite naive way. It seems from the outside, but quite easy. We translate these objects and we know how to translate it into Kubernetes deployments. What, there is a magic behind that we kind of know how to override the entry point of the Docker containers, how we set up the startup props, what is our endpoint for health checks, so on and so forth. But this expects you still to know quite some about Kubernetes and you would still have to handle auto scalers, original port auto scalers and so on. To help in this last mile, we have this last part of the stack that is jcloud. So think about deployment made easy. Up until now, if you follow these steps, you are good as long as you have provisioned your resources, you know how to manage Kubernetes, you enable monitoring, gateway, certificate, security, whatever. Compare this to doing Gina cloud deploy and your flow YAML or your deployment YAML without caring much about this. So what is jcloud? Jcloud is beyond some CLI and SDK and an API, is basically a Kubernetes operator and a set of custom resource definitions based on Gina flows and deployments. And it knows how to manage these objects more efficiently. It comes with auto scaling, GPU resource allocation, it exposes your services, DNS, blah, blah, blah. And it can not only be served by us as a management, but also since it's an operator, you can take it with you and it can be self-hosted. So it's kind of is in this bring your own cloud paradigm. So the conclusions right now. So I have tried to make clear how Gina provides a platform to help ML engineers build and deploy AI services. We have seen how DocArray help you from having a POC to serve with Gina AI. How Gina can remove your decision layer to move it to Kubernetes. And how at the end, jcloud can make this even more seamless and more easy by providing this operator and this API. So thank you and I'm welcome to take any questions. I have a very probably simple question. If I have this land chain, how this general platform can be integrated with this kind of library. So there are two ways. So you could just have long chain in one of your executors as deploy the service as long chain or the other way around. And we have already been successful as adding ourselves as a long chain dependency because long chain is kind of a monolithic platform that works locally here. So for instance, we have a plugin to help with you with DocArray. I don't know, one of the vector indices that are most popular on on long chain is coming a wrapper around DocArray or we have our own embedding models served with this Kubernetes approach and that's exposed as an API wrapped from long chain. So this is the way we integrate with them. Yeah, so I'm also one of the co-maintenors of this GINA project. So I faced a question of, so what's the difference between GINA and long chain especially this year, right? It's because long chain now becomes kind of very popular in the generative AI and people start to build prototype and thinking about using long chain production. So I think the philosophy behind GINA and long chain are quite different. So I would compare GINA to more like a Kuber flow or air flow, which is based on the all the philosophy of cloud nativeness, right? So you need to have microservice, you need to wrap the model into a Docker container and then you need to connect all this Docker containers to do some high level task, right? So if you look at GINA source code it's all about this containerization, setting up the protocols, using GRVC, compressing the data into binary format and then communicating all this microservice in order to kind of orchestrate some high level task. But if you look at long chain is very different, right? So long chain is a very monolith program where inside long chain is basically a string manipulation happens everywhere. So one can say that it's a prompt orchestration framework. So it's not really like a microservice orchestration framework, but it's prompt based. So it's mostly like a prompt serving framework. So in that sense I wouldn't compare long chain versus GINA in that perspective even though from both architecture there is a concept of a flow or chain stuff but you know everybody hate flow, right? So that's why, for more explain our perspective this is the difference between. Yes, so what I believe is the future generative AI applications or multimodal AI applications is a combination of model based technology and prompt based technology, right? So long chain and some prompt serving framework can be used as what John said as one of the executor used for pre-processing, for example, on constructing the prompt, right? Building instruction prompt from the template from the prompt database, all these kind of things, right? And then you feed the prompt with to a llama, right? To a kind of self hosted service. And the self hosted service is running as an executor inside GINA, right? Yeah. Long chain is one of the executors, yes. The whole orchestration is still organized by GINA or Kubernetes, right? In this case, right? Yeah, so we had, I think we have GINA embeddings as a plugin, for instance, in long chain. Long chain at the end is really a Python program that calls different services and kind of wraps things. We have our own wrapper for GINA embeddings that instead of computing the embedding locally kind of uses our client to send two computing embeddings that are working as scaled in Kubernetes. Yeah, but the general idea I will say is more intuitive to kind of feed a monolith program into a microservice architecture than the other way around, right? So feeding a microservice architecture such as GINA into a monolith program such as long chain is less intuitive, yeah.


What's going on guys John Hammond here coming back at you with some more Natus videos from the over-the-wire war game So we just got up to Level six here. We just got the pass before it in the previous videos And again, we're still working with Python scripts that will go ahead and return the Actual password for us or kind of a flag to get us to the next level. So that's been working pretty well for us We're working in Python scripts and now we're on to level six. So let's go ahead and check out what we're working with here We're still using a request object session and When we run this script control B We're not getting an output. Let's find out why Okay, looks like we're good now sweet Let's let's see what this this page is here in the content of the Natus 6 level. There's a post method form. Okay, it says input secret and And checking The secret that that's that's always submit. That's all we're submitting.  Let's try and make a post to it. Let's Change our command here to post to URL And we'll pass in some data. We need right we need the secret To I guess equal anything We'll keep our off like credentials moving in so name equals secret and make sure submit is it should be going through on its own It's submit So now if we run this what was the response? Seemingly nothing. Okay What is going on? Oh Oh, there is a view source code. Okay, totally missed that out. There is a view source dot HTML script here That's linked. So let's see what we've got Maybe it's doing something peculiar with this With this input secret that we're doing. Let's change the URL that we want to Just add the What was it? It was index hyphen source dot HTML after we get that page. Oh goodness gracious. This looks pretty bad let's Encode special characters will that work for us? No Well, what about Tidy HTML HTML tidy will that come to save the day kind of Maybe we can install a package to HTML Escape or Decode and code Dang, how do we HTML escape this? Because we've got all these encoded ones See if there's an easy way to do that Head on over to our friend Google Sublime text HTML Decode De-entitized looks like there's a Package here has commands to convert HTML entities to a character. Let's try that Install package. It was called string and code installed cool HTML De-entitize, okay, that did some stuff will De-entitize again sweet cool. We just got the source code just like that. All right If a reiki exists submit posts if the secret so so this is PHP code, right? You can tell here Denoting Well, at least the the question marks and braces make it say that this is PHP code So if you haven't heard of PHP It is the back-end language on most servers that will do processing of Code and server side code before a page is returned to you. So it looks like it is including Something from a separate file. Maybe we can access that and It looks like it gets a variable secret out of that We can only assume that's how that dollar sign is referred to PHP variables are denoted by a preceding dollar sign in PHP code and it's testing if the Submit Okay, so if it is submitted if the post request has been made it tests if the secret that we posted to it Well, we actually submit through the form is equal to that secret and if it is it will give us the password Okay, so let's check out see if we can access that included file Let's just tack that on To our request change that instead of the instance it is okay cool, and we get the secret here This guy. I don't think that our browser. I don't know if Because normally this clearly this is PHP code. You can tell by the beginning and ending question mark and Greater than less than symbol walka walka whatever you want to call them PHP code here would normally not be rendered out To the web browser you'd not be able to receive that. I think there's an issue in that the designer didn't name that file With a dot PHP extension you can tell Apache or a web server to evaluate things bet You can tell a browser to do some of those things With like evaluate PHP code on things that don't have a PHP extension but it's probably a good idea to use the same extension if you Like if you want to and you can So all that that didn't go through but we got the secret for it. Did I put it in my clipboard? I did okay cool so let's go ahead and paste that or post that in this case with using that as the data that we give that that we submit and Now let's see what the response is Nothing. Why did that happen? I don't think maybe we're not giving the submit key And that's necessary because we aren't giving it. We let's say submit can equal Submit now that should evaluate. Okay, great That PHP code ran and it said great the password for that a seven is here Let's steal this Glad that was able to be echoed out for us Paste that in here. If we run it will we get it? Great. We've got it Let's go ahead and save that So if we had submitted that just how we were in a web browser, let's get let's get to that page and show you how it looks Just even in your web browser All that's happening is Let's go to your native six That that form that input secret form here We're submitting this like all we're doing is Literally typing that in and pressing the click here button. You can see through like your developer tools if you're checking that out in Firefox or your web browser, you can see that you're making a post request when you do that So if I loaded this page here all the get requests that are happening to that URL and if we input a secret here we submit the query you can see that it was posted here and You can check out the headers stuff that we sent it like you've seen before in the previous video Here's the server the content that we're working with and refer we had in here just like a previous video and We actually send it the parameters the data that we're working with Secret that we encoded or secret that we sent and sent as a form data Put into Dictionary data type the pipeline could work with and then we got the response Here's your web page back. Here's everything to give you and since the PHP back-end server code was able to process it We are granted with the natus 7 password neat All right, cool Really, that's all for this video. I think we're dragging on for a little bit of time here So thank you guys for watching. Hope you enjoyed this and we'll move into natus 7 in the next video See you soon


We're at PDAC. Who would have thought a media company here with a lot of miners? Well, guess what? They need to tell their story too. That's why they need Moveli.


SNESTER! Allow me to speak in the second person for a second. Now, you're Nintendo. You've got the biggest money-making franchise in gaming, it's a sure thing. Super Mario World sold approximately 80 gazillion copies, and to this day it remains one of the best games ever made on any system. So if you're Nintendo, you gotta make a sequel. Easy enough, right? Just more Mario World. More levels, more power-ups, more Yoshi stuff, more Mario stuff. You can practically print your own money with this formula. But, no. Scratch all that.  Let's all go into a room for a week and do nothing but smoke pod and trip on acid, and let's make Super Mario World 2 Yoshi's Island. Nintendo really outdid themselves with this game. The graphics and the gameplay elements are so incredibly imaginative.  Dude, let's have Yoshi be the hero, but like, have Mario have a baby? But then let's have like, Yoshi poop out his enemies into eggs? But then, use them to flaying at other enemies? Dude, then let's have Yoshi be able to turn into a bulldozer. No way, a race car.   And the craziest thing of all is that it just works. Yoshi's Island is top quality. It even adds a couple new dimensions to the regular platformer that you'd be used to seeing on the Super Nintendo. You rely on Yoshi's unique abilities to make sure Baby Mario is protected. So there's no life bar, really. You just have to make sure you hold on to the baby, or else. Yes, that's right. The game's biggest flaw is no secret. It's that annoying, ear-splitting sound of a crying baby. I decided long ago I'm never having kids, and I'd like to think a tiny part of that decision came from getting fed up from the crying baby in this game. I'll give it this though, it's effective. You want to get him back if only for the sake of shutting him up. It's not that big of a deal though. The game maintains a good balance between being unique and keeping that Mario vibe overall. Despite how weird it looks and plays, it does still come across quite well as a sequel to Super Mario World. One tweak in particular that I like is the adjustment that they made to the difficulty. Extra lives are so easy to come by in Mario World, you can practically play that game blindfolded and somehow end up with like 20 extra lives somehow. The scoring system that Yoshi's Island implements makes it a little tougher to accumulate extra lives, and I appreciate that. However, one other minor flaw I want to point out is that the game relies a little too much on exploration. I like that the levels are a little longer than the first Mario World, but some maps are so big and so long that they slant away from intuitive and more towards deliberately confusing. In some levels there are literally arrows telling you where to go, but you know, only when the game feels like it should tell you. This game more than makes up for its flaws however, I'm going to sound like a total cliche written douche canoe reviewer for a second, but I have to say it. This game is an experience. There, I said it. I hate phrasing stuff like that, but in the case of this game, it's true. If I had to pick one game that had the best graphics on the Super Nintendo and the best overall visual presentation, it would be Yoshi's Island, and I wouldn't even think twice about it. It's not even close. If you've never played this game before, just wait until Yoshi accidentally eats one of those poppy cloud-looking things. It's just the best.  It's like you're suddenly in the middle of a Looney Tunes cartoon. Look at this. Anyway, I passed on playing this game for a long time, because I wanted a quote unquote real sequel to Mario World, and I regret doing that because this game is such a trip. The creativity and sheer fearlessness in which this developers tackled this game has to be commended. So if you were like me, and you took forever to get around to playing Super Mario World 2, Yoshi's Island, or if you wrote it off because of the quote unquote kiddie style graphics, you're missing out big time. You won't find a game like Yoshi's Island anywhere else.


When they call me Prospect, I'm from the Bronx, born and raised in South Bronx. BX is the brother, you know what it is. I've been rapping since I was like 12 years old, you know what I'm saying? And big puns to do that I grew up with. He was like my cousin's best friend and shit from back in the days. And out of nowhere, I heard it, I heard it, he did it on the radio. And I was bumping like, that's my man, come right there. After I heard that, I got this little tapy hat, you know what I'm saying? And then right here on this block, he's hanging on right now. That's where he was, he come through at. So he came through the block. I was like, yo, what pun I got the new tape you had, you know what I'm saying? I heard your drone right here. So he was like, well, yeah, but the whip, he's like, let me see that. So I'll take the tape out, he was like doing the killer tape. He was like so happy that I had it, and he was like, yo, stay right here. I'm gonna come back through and we're gonna jump some 40s. I want us to chill. I was like, all right, cool. So when he came back through, me, him, Cuban.  I never told him I was rapping that, but he knew.  So I just started rapping, whatever.  So I never really asked him to come in on him.   I was like, you know what I mean? I was like skeptical about Joe, because I heard Joe was like a funny dude. So I was like, I don't even want to meet him, you know what I mean?  Then they took me through to meet Joe. I'm off with Joe, you know what I'm saying? Joe was like, whatever, he good. We're working, but he wasn't really convincing. He sounded convincing, you know what I mean? But anyway, point of, you know, kept me with him and we started doing drinks like that. You know what I'm saying? He was doing records, and he heard me on punches.  And then we just went and kept going. So I got down with the spa and all that memory. So I got down with the terror squad. I was like 17 going on 18. When I was 17, I was like, right, I was getting hard into it. But over here, they had like a, on my man named TUD. He was like an older dude. He was like the head rapper out here, you know what I mean? So I used to do little cybers with dudes and all that. But it was like, he didn't really pay attention to nobody. So it was like, all right, he ever seen y'all whatever TUD was to do, you know what I'm saying? So he's my motivation, you know what I mean? So it was kind of funny, because everywhere else I was rapping besides my hood, he was saying that was nice. I never was getting that props over here like that, you know what I mean? So, you know, I was like, yeah, I think dudes just hate it. So I just went in the lab.  I never rhymed over here in the world.  You know what I mean?  That's when I thought that we taking the serious ones. I want you to be down just that.  It was funny, because when I heard he was down, the squad, I felt like they needed a nigga like me. I was like, I'm the perfect nigga for them, you know what I'm saying? So it was just weird, because I thought about it before it happened, and it really happened. That's crazy, when you know a dude, just being a regular dude from the block.   So it was like counting the way, because you always looked at him as like a regular dude, like.   They was praising my man, like he was the greatest.  So when we started doing the tours, and from the clubs to big stadiums and shit, like 15, 20,000 people, that's when I realized it was real, you know what I'm saying?  Like that's when I was like, yeah, my dude was just big, you know what I'm saying?  Regular homies that everybody looking up to, like they're in there with the world, going to heavy state, can't install on the radio, like, yeah, man, he was really big, you know what I'm saying?  But I didn't see like a wild man over there just standing like a man with a super star, you know what I'm saying? Because he a humble dude, so he doesn't really, his approach was never on those super stars, he's the humble dude or something. Took a while to kick in, but yeah, my dude was big, you know what I'm saying? And then it's like an eight year run, you know what I'm saying? A few type of sport albums, a couple of the big pun albums, a couple of the Fat Joe albums, a couple of the soundtracks and everything, you know what I'm saying? So now I'm here, you know what I'm trying to do?  You see, the game is different, the game changed now, you know what I'm saying? At that time it was more like nice people giving you opportunity, people that try to set you up and shape you into a rapper. But right now the game at a point where the label should spoil you, you gotta already be a made person, you know what I'm saying? Like you gotta let it even know somebody that's famous in the business, or you gotta already have your knowledge of the game where you already got your whole look, your whole movement, the way you're trying to go, where they don't wanna shape you in. They used to shape you into a trainee where you said, now there's no training, you gotta come as a ready project, which the inmates know what you wanna do, and have you in your style, anything down pack, you know what I mean? So, and now you're out and plus a lot of mixed safety, you know what I'm saying? That helps you out a lot too, putting out all the mixed safes, but it's really going to where their place is at, where their fence is at, you know what I'm saying? In your face out there, the more people see you in different states, the more they respect you, cause this game is like, people look down on you, you know what I'm saying? They might look at you like, oh, he's no bummy dude, but when they see you in a few different states, like, this dude's anywhere, you know, he over here in this state, they start respecting you more, you know what I'm saying? So it's like, it's fake, you know what I'm saying? They respect money, man. So, you know what I mean? But anything that's got to be around, man, then you just go hard, man. Whatever you want to do, just go hard, man. What I learned from this game, man, is that this shit is strictly business, man.  This is all business, man.   This is the whole game. People you do business with, and people that's in the business that show you love when you come through. Yo, what up, momma, yo, yo, yo, yo, love, love.  Well, put their head down, they go the other way.  Same dudes right now, you got an arm and a leg and you get one beat from them, they probably try to charge you 15,000.  So, what I'll tell you is the main thing is yo, it's always business, it's never personal, man. So, when niggas make some niggas personal, you're my brother, I love you, don't believe it for one bit. Don't care how good it sounds, you know what I'm saying? Just keep your, the main thing about this business is you gotta be smart with the business, you know what I mean? As far as like the paperwork, the contracts, everything is business, as far as videos, or putting it on the radio, or a feature, or doing it with your writing, your publishing, like everything is business, so really get smart behind the business, man, and you'll really last longer in this game, you know what I mean? But always remember, business never personal, you know what I'm saying? What's pop, man? It's your boy Prospect, man. Prospect, though. Peach of the T, BX is the barrel, man, from BX to Boston, man, you know how I do it. Slab on both sides, man, dude, show me love. I'm holding it down with PSP, the best.com, you know what I'm saying? Dude, get it in, man. Watch out for that, you heard me? Real talk.


You guys, Kijun and Simo from SAP, they're going to talk about, sorry, HANA applications. So why don't we start off with, tell us a little bit about your role inside SAP and then we can kind of go into the news this morning and roadmaps and talk a little bit more about the applications you guys are building on top of HANA. Sure. Hi, Jeff. So my name is Simo Said and I drive HANA for lines of business and industries. Kijun Lee, based out of Palo Alto, the party marketing for some of the new business and consumer apps that are powered by HANA. Great. So yeah, so we saw a handful of new applications come out this morning. So maybe if we could walk us through a little bit, you know, what we heard from this morning, what the news was all about and what the overall theme is of the applications that came out today. Yeah, maybe to set up the context of this new solution. So I mean, we live in an area where big data is getting more and more important. So we tend to characterize big data by the volume of data, which is just getting more and more important. The velocity of data and the variety of data are more and more instructive coming from social media, et cetera. And there is a fourth element to characterizing big data. That's what I'm calling the fourth V, basically, which is the value. So how can you basically generate more value from your big data? And the whole purpose, basically, of HANA is to help customers transform their big data, explore the full potential of their big data, and transform this into business value. And with HANA, bringing innovations for having real-time information, working on large volume of data, being able to get access to any level of information, and accelerating the analysis and providing flexibility to any user, basically, to access the information they need, is actually just transforming the way we are developing, thinking our solutions. And the announcements that we made today is basically just a continuation of the execution of our strategy, which is to provide more value and bringing our HANA to the next level as a platform on top of which we're developing new solutions. Right. I mean, as we've been covering the big data market, we definitely feel that the application layer is really where you start to see the value. The plumbing underneath is certainly necessary and part of the overall equation. But really, the real value comes to end users in the form of applications. So why don't you guys tell me a little bit about what's different in terms of developing applications on top of HANA compared to the way SAP traditionally developed applications? What are some of the nuances and some of the changes you've had to adapt to? So the way you think application is different because you don't have the limitation that you're having in the past with limited technology, disk-based, database. So HANA is now leveraging, as you know, the power of in-memory so you can work on a larger volume of data and you can simply think about application that were not possible in the past. So if you think about it, we're helping our customers in several ways. When we announced HANA and general availability close to one year ago, we, a few weeks after, months after we started to launch the first solution, smart meter analytics was one of them, helping utility companies to basically collect real-time information from small meters and being able to analyze it for the finding patterns, trends, segmentation. For ERP customers, we were looking at areas where their performance was fairly slow because of limited technology. So we started to provide these accelerators that can help them take data from ERP, put it into HANA, and then simply do real-time reporting and analysis on top of it. So this is just two examples as to how we can rethink actually the way we design application. But the essence remains the same, that we start with the customer, we start with their business requirements, their business issues. And that's basically the essence of our development. And to build on the same point, because you can do so much data within HANA, it does change your mindset on how you develop the application designing it because given that you can run predictive analysis and simulations and other types of deep analysis, that really changes how people plan. So for example, companies are used to planning in a very linear fashion. You plan for a couple weeks, you execute, and you might look back and might not, but not you can be much more iterative. Three of us can come together in a meeting, actually run analysis right then and there on big data and simulate and say, well, here's the answer, we got back. What if we try planning this way? What would be the impact on supply chain finance and other aspects of the company? So it sees a lot of time. One of the customers for the new app that we launched called SAP Sales and Office Planning, they said this app will help us cut our planning time in half. Can you imagine how much more agile it can be given that this application will speed up the response of the man? Interesting. So what about the kind of the cultural shift that has to change at your customers to take advantage of these new applications? I was listening in on a side session earlier today, University of Kentucky, a customer of yours, and it's something very interesting that we're struggling getting the business to move at the speed of HANA, and he's actually developing some big data days to explain to his constituencies how we can use this application, get them thinking about new ways to use big data. How do you approach that? Is that something you have to think about when you're building applications, or is that a kind of a services problem after the fact you approach training and trying to teach average users to think in this new big data kind of mindset? It's a good question because they can figure out where they are, and they can take steps that make sense for them. They can start with speeding up their existing reporting analysis and making their existing business process efficient. Or if they want to go beyond that, then they can think about how do we treat our customers in a much more personalized way because we have access to big data coming from internal sources such as CRM, point of sale data and other sources, combine that with social media data, mobile data, and news feeds to really understand who are my customers and what are they looking for? Their preferences and what have they shopped in the past. Similar to the verb example that was on the keynote yesterday, today that really helps you do that as well. So marketing can think in a different way. Sales as well as the entire company. And they can even think about new business models. Interesting. And maybe just to add to this point, our strategy from an innovation perspective hasn't changed. We continue to want to provide innovation to our customers in a non-disruptive way. So today customers using ERP can leverage the power of HANA in a non-disruptive manner without changing the user interfaces they're using without disrupting the way they are working. And in parallel, as you said, there is probably other areas where transformation and change is needed because HANA can also help deliver a new data-driven business models that were simply not possible before. But that's a positive change because it can allow you to be more competitive in your market. What about the mobile paradigm? How does that impact building applications? Because I know one of the big themes here at Sapphire has been mobile. Cloud also like to get your take on that, how that impacts building applications for HANA. So what do you have to do in terms of mobile application development? Is that a separate process from the application development you might do on premise or I should say a desktop, laptop type system? How do you approach that? So the beauty of the approach and the portfolio that we have at SAP is that we can allow many combinations and give more choice to our customers. So whether they want to use their application on the desktop or their mobile. So if you think about it for an end user, you will not see HANA, right? You will see the power of HANA but you will not see HANA in action. They will leverage the real-time capabilities, the ability to work on large volume of data, etc. And I think the best way probably to summarize it is coming back to your earlier question as well is probably what Bill said in his keynote on Monday summarizing very well the approach. What we're trying to do is basically looking at accelerating things, simplifying and bringing more personalization and bringing HANA mobility as well as cloud is what is allowing us to accelerate, simplify and bring more personalization. And everything that we're bringing basically on the mobile technologies is helping us achieve this level of improvement in the performance that we deliver to our customers. So that mobility just adds a productivity enhancer almost exponentially in many ways. That goes together. I mean, what Hasso was saying this morning during his keynote that everything is mobile, yes, and you don't want to wait three seconds to get the information that you want. So that's where HANA comes into play, right?  And if you're my customer in sales, I'm meeting with you with an iPad and there's HANA on the back end. I can meet with you and run the analysis as you're asking me questions and tell you to answer right away instead of me saying, let me give out to you in a day, which we lose context and momentum as well. Right, absolutely. And that's what customers expect now in customer service. So both on the business and the consumer side. So we were talking a little bit earlier about kind of the consumer versus business application. So how is SAP approaching that? Sure. So I get this question a lot. Why is SAP building consumer apps? And the reason is simple. Many of our customers are in consumer-facing industries, whether it's CPG, utilities, Hellco, media and others. All their customers are consumers. So what we're doing is we're building out consumer-facing apps, whether it's an iOS app, Android, Facebook, Web app as well. Building business apps as well to help our business customers connect to consumers in innovative ways. There are many technologies that have come up, as we all know, social media, mobile and others, where companies have an ability and opportunity to engage them in really interesting ways. So some of the apps that I can talk to you about are for retailers. To enable consumers a personalized shopping experience, because we can gather information about their location, their preference, let's say they're really into organic or fair trade, types of products. And we can connect to the backend system of a retailer, CRM, point of sale data, even supply chain for available information. Bring the two together, big data, external and internal data. Have the right context about you. As you're in the store, send you the right promotion and recommendation in the moment as you're shopping, as opposed to when you walk out. And then we send you information, which is too late at that point. Okay, great. All right, guys, well, thanks so much for joining us today. Great conversation. We're talking about hand applications. I'm sure we're going to see a lot more from you guys in the coming weeks and months. So thanks for joining us. We'll be right back after this short break. Thank you. Thanks, guys.


This is stock number 8J424A, we are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and use Jeep headquarters today. We are checking out this super clean 2018 Jeep Cherokee Trailhawk. This vehicle has the 3.2 liter V6 Pentastar motor which pumps out through 271 horsepower. From this HD video you will be able to tell that this Jeep is extremely clean all the way around inside and out. Granite Crystal is the color. We shoot all of our videos in 1080p so if you have HD capabilities on your computer tablet or smartphone device turn them on right now because it's like you're right here looking at the vehicle with me. See just how nice and glass like that paint is. Comes with these 17 inch painted alloy rims no scuffs or scrapes on them and it has the firestone destination 245. 65 R17 tires with just about all the tread left. No dents or dings on that front fender. You get the LED running lamps, the projector lamp headlamps and the factory fog lamps. You get the dual red powder coated tow hooks, the granite crystal trimmed grille, the black decal which is removable. Just a sticker. Passengers side fender is absolutely perfect as well. No scuffs or scrapes on that and the passenger side rim is in excellent shape too. Down this side of the Jeep just as clean as the passenger as the front of the car. No dents, no dings. You can see just how nice and clean that paint is. We take these HD videos so if you are far away or even if you're close by just can't make the trip down you can still see the vehicle, hear the vehicle and have confidence in what you're looking at before you even get here. Once again very clean down this side of the vehicle no dents or dings and this back rim is absolutely perfect as well. Back tires have just as much tread as the front tires and as we come around to the back of the vehicle you can see that the rear bumper is absolutely perfect. Dual rear exhaust, you get another red powder coated tow hook, backup sensors, the rear gate is absolutely perfect. You get the granite crystal Jeep and four by four lettering and the LED tail lamps as well as the trailhawk logo on the back. Back storage area is very very clean, has a full size spare tire with all the jack tools and if you press this button that gate will power come down on like that. Down this side of the vehicle just as clean as the passenger side no dents or dings on that quarter and the rear tire is absolutely perfect as well. Very very clean down this side. It does have the power heated mirrors with the built-in directional signals and blind spot indicators. As we hop inside the vehicle you can see the trailhawk package gives you the black leather and cloth seats. You get the red stitching, the trailhawk stitched backrest, power driver seat with factory lumbar and it does have factory all-weather formats throughout auto headlamps, power windows, power locks and power mirrors. As we hop inside the vehicle you can see it does have the seven inch TFT display and this vehicle does have 2,125 miles. There you go, you can see it a little bit better right there. You got the digital spinometer, outside temperatures range to empty, you can modify those however you want. It has the leather wrapped steering wheel, cruise controls on the right, Bluetooth and information center controls on the left. On the back of the steering wheel are the controls for the radio. This one does come with the 8.4 3C radio, has the factory navigation installed on it. This is also where your backup camera shows up, Bluetooth capabilities.  SD card, USB and the Augsjack 9 speed automatic transmission factory all weather format on the passenger side. No rips or tears on the passenger seat. This vehicle has never been smoked in, smells very clean inside and is just about as nice as you can get for a used vehicle. Has the homelink system for your garage door security systems and lighting systems, your map lights and then you have 911 and assist controls right there. We will take a quick look at the back seats and we'll check out under the hood. This vehicle also has remote start and the enter-and-go system. Back seats are just as clean as the front seats. No rips, no tears back here. Has latch child safety system. Backrests are nice and clean. Knee bolster airbags. This vehicle has 10 airbags in it. Very safe vehicle. Factory all weather formats back here and a 115 volt 150 watt. Well again, these seats also fold down for extra storage and they go down pretty nice and flat like so. We'll take a quick look under the hood. 3.2 liter V6 Pentastar motor and Jabez very clean runs very smooth. This vehicle has 271 horsepower, has been fully safety inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. Vehicles been gone through mechanically 100% and is 100% ready to go to see more pictures of this Jeep or one of our other 450 new and used cars, trucks, SUVs, minivans, wranglers, you name it. We got to go to our website www.SummitAuto.com. Full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle that we have all at SummitAuto.com. If you want to make this one yours, give us a call right now<PHONE_NUMBER>, sales professionals are standing by to take your call. Once again that number is<PHONE_NUMBER>. And if you'd like to check out more HD videos like this one you can go to youtube.com slash Summit Auto. Remember to like, subscribe and share on this video and all the videos that you see there. In fact, in a second you'll see a link to subscribe to our YouTube channel on your left. A link to more used inventory videos like this one on your right. If you have not been to our website on the bottom, a link to this vehicle on our website, click those check us out. We really look forward to helping with this like new 2018 Jeep Cherokee Trailhawk. Thanks again.