Today, I'm going to be talking about a feature platform that we built at Gojek. So this is a project that's been going on for quite a while. We're actually planning to open-source it soon. At the moment, I can't give the finer details. But in this talk, I'm going to give you the higher-level considerations of what went into designing the system and the major components inside of it. So let's start with who or what is Gojek. So Gojek is an Indonesian technology startup. I'm not sure if many of you know about Gojek. But there's an example of our application on the screen. So on the screen, you can see some of our services and products. One of the most famous four is actually GoRide. So that's a ride-hailing service. And the reason it's a motorcycle and not a car like Ola is because of the unique challenges in Indonesia. In Indonesia, there's been a massive population growth. They're a very big country. They're the fifth largest in the world by population. So I'm sure you know, as in India, traffic can be a big problem and people need to find ways around that. Otherwise, you're always stuck in traffic. So the way in which they solve this is with Ojek's. They're just people in this corner of the road that will take you to your destination on a motorcycle. And this is an example of that. So when we started in 2011, we were just a call center, no application.  And that went down really well in Indonesia because now people had a way to quickly move around without having to wait for an Ojek. And our founders were very pragmatic and they did a lot of data research with customers, analyzed user behavior, and they knew that there was a demand for more products and services. So in 2015, we launched a small basket of products in an application. So go ride, so a motorcycle ride-hailing food delivery with GoFood, GoMark, which is our grocery shopping, and a few other ones, e-commerce related and logistics. Since then, we've hit hyper growth. We've gone from a call center at that point in time to a unicorn, so a multi-billion dollar company. And we are one of the largest players in some of these verticals. We are in 15 verticals at the moment with 18 different products. And our approach has always been understand the customer, be very localized to the region and don't just randomly launch products, but be very targeted. So a bit about our home, Indonesia. In Indonesia, our application has been downloaded 80 million times. We're in 60 cities. We have more than a million drivers on our platform, hundreds of thousands of which are active at the same time. So just in one product, we are a very big pair. And for GoFood, our food delivery, we are the biggest food delivery in Southeast Asia. We have more than 200,000 merchants on our platform. And if you're talking about digital payments, we've got one of the largest e-money systems in Southeast Asia. So the scale at which we operate is massive. And if you're talking about data and data science and machine learning, there's a very richness of the data and the engineering systems are also, I have to be very robust to handle that. I hope this animation works, but on the right you can see an animation of just Jakarta through one day. So each pixel is a person being picked up or dropped off on a ride. So this gives you a little bit of an idea of just the scale at which things operate. And this is a pretty old animation. This is at least a year old, but it gives you a good idea of the pulse of the city, of magnitude of orders. Most, excuse me. This is only Jakarta, yeah, just Jakarta. So currently we're also expanding into new territories, but this is just Jakarta. So the scale is very big and most of these transactions are processed with ML models. It's engineering systems, but they're affected by ML. But data science in Gojik is not just about machine learning, it's also about understanding the customers. The business looks to us to understand our customers and then give them advice on what plugs to launch and what actions to take. So as a data science platform lead, it's my role to make sure that data scientists are very effective and efficient in what they're doing. So we looked at where the data scientists were spending their time and this is what we found. So this is just a bar that represents a typical project workflow for data scientists. So the colored bars represent tasks that they're doing that are data science related. It's in their domain of expertise and the gray bars kind of represent tasks that we think they should not be doing, engineering related tasks. So when we looked at what they were doing, we figured out they were doing a lot of engineering related work. So this was a concern for us. And this was actually the precursor to understanding or knowing that we needed a feature platform because even though making features was one section of the tasks that we're doing, we realized that many of the other tasks they were doing were in support of creating features. And especially when it came to building pipelines, that was one of the biggest time sinks for data scientists because the feedback loop is so long and failure in creating a pipeline can be very costly. So we wanted to decouple them from that and free them up to be focusing on modeling more. So why did we need a feature platform? But post that investigation, we realized there were two problems, the service creation and feature engineering. So the feature engineering was the one that we wanted to solve and this is what the talk is about. But there were some specific problems. The first pipeline jungles. So the data scientists were building all of these pipelines and they were using hacky code. I'm sure you guys have seen it. There's very many levels of competency in a data science team. And what ended up happening is you have all these pipelines that are interdependent on each other in ways that you don't understand. And it's very difficult to visualize and get a clear picture of that. So we built systems that were not going to scale to multiple countries to global scale. So we needed to solve this in an engineering way. Inconsistency between training and serving. So this is actually a big problem. So what happens is data scientists will build a pipeline to create features. Maybe he's using Python. And when he goes to production, he can't reuse those feature transformations because they're written in Python. So what ends up happening is they redevelop the features in serving or in production in Go or in Java. And there's an inconsistency between the feature creation and the transformations. And there's scope for error creep there. So we wanted to solve this with our feature platform. The third is the need for real-time features. So when you have production systems, typically you don't automatically get real-time features. You need to engineer a system that can stream the data in and store the features necessary for that model. And having to do this for every project was a big cost. We wanted a system that could just give us any feature that our models required. And the fourth is standardization. So just having a standard way to define a feature, this allows you to have logging and monitoring and lineage and documentation and all these things that are often ignored, but very important. So if a data scientist opens a code base, there's a standard way in which he understands that the source code should look. It shouldn't be a mystery that he has to decode every time. And then we wanted to be able to handle large volumes of feature data. So if you have 18 products and you have tens of millions of customers and more than a million drivers, you have a lot of rich data and you often have thousands of features that you want to pull into a single model and you want to look at months of data so that that can take days on the wrong platform. You want to be able to create that in hopefully hours or maybe even minutes. And then last is duplication between data scientists. So we wanted the platform to have an element of discovery. One person creates a feature, everybody can use it or improve on it. You don't want data scientists to work in silos where they create features that only live in their realm. And this is very important because some data science is a lot more productive in building features and some features are very powerful cross-project. It's not just useful for that one project. So to distill these requirements down, there's some core things that we wanted from our platform. Abstract to where the infra, we don't want data scientists working on engineering systems. Standardize the feature creation, like I said. We wanted the consistency between serving and training because it's a big room for bugs to inter-production systems. We wanted the system to be scalable both in serving and training. So a training store should have a historic time series feature data and the serving one should be able to scale to high QPS. Obviously real-time features in serving and discoverability. So to reason about this problem. Yeah, yeah, so you mean between serving and training. Well, typically what happens is that data scientists are kind of disconnected from the engineering and production systems. So they're fine building a pipeline that maybe take some like query database, get it to CSV, transform the CSV. I mean, this is the basic example and now you've got features created. But how do you go to production with that? You can't just put those Python transformations or code into production. Often it needs to be rewritten in a different language like Go or Java or something that's more performant. So what ended up happening is you have the same features in two different languages and often you're using different libraries and dependencies and there's inconsistency and data types and there's room for error to creep in there. And we want to avoid that and move feature creation upstream so that a feature that's created is in both training and serving and it's identical. So there's a consistency. So we're talking about a feature platform. We needed to break it down into higher level components instead of just having this homogenous platform thing that doesn't really, you can't really reason about. So we knew we were gonna have input data. In our case, we're on Google Cloud platform. So this will be BigQuery for raw relational data, object store, cold storage on Google Cloud storage and then finally an event stream. So we use Kafka for our event stream. We knew we needed, we have two consumers, model services and production for serving for the real-time features and we have users that'll be training their models. So data scientists training their models. So the components we broke it down into were creation. So these are essentially the transformations on the raw data. So this is bounded and unbounded data. It can be streams or flat files or tables. We needed a storage layer to store the data for both training and serving. We needed an access layer. So this is a very important part that often is missed. You don't want the client's accessing database directly you want an abstraction layer there to handle the load. We'll talk about that a bit later.  So this can be a user interface or some way that data scientists can see which features are in the platform. What can I use? What can I use to train my models? So let's talk about those components. And the first one is creation and this is actually one of the hardest ones. I hope you guys can see this but this is just a typical scenario of one of the problems that we solved with machine learning and this is the dispatch problem or allocation problem.  You're at one point you press book to go to your destination. We need to decide which car, which driver do we give you. So this is a problem that is very important because of the amount of money that throws through. If you have millions of bookings every day a small tweak in the ML model can make a big difference. So features are critical for this model. So you can determine if a car is going to be or a driver will take a long time to get to the customer or if he's already on a trip and these things change in real time. If he's got a bad rating or a good rating all these factors come into play. So for the dispatch model we needed to have both training and serving features and a lot of them. So some examples of the features that we needed. So if you're talking about the driver the driver has location, data, speed, direction, ETA and a whole range of other features. You have temporal features like time of day, day of week, is it public holiday? If you train a model and it's suddenly Ramadan your model becomes inconsistent. So these factors you need to take into account. There's regional information that you have. Regional features like is there a traffic spike? Are there many flights landing in the city at the moment so that you can anticipate what's going to happen in the future? And then the customer features as well like what is their behavior in the pause? Clicks and actions and are they canceling a lot of rides at the moment? Are they price sensitive or are they very tolerant for a driver being late for example? So these features are critical in this model's decision making. So like I said, I don't know if you guys can read this but basically we had three stores that we needed to support with the system. BigQuery, this is our data warehouse. This is where all our transactional data is. We have BigQuery as our relation store. We have events on Kafka and we have our data lake on cloud storage. So the data lake is mainly used for extremely large volumes of data. So if you take GPS pings, if you have pings coming in every second for every driver, that's an immense amount of data. So that we store on Google cloud storage. So this is where our input data is and we looked at a lot of ways in which we could create features. But based on our requirements, the one we went for was cloud data flow. And I'll talk a little bit about why we went for that. So our stream main options were basically Spark, Dataflow and I'm gonna say Dataflow slash Apache Beam and Flink. We considered Flink and Beam as the main options because of the first class support of streams and batch. So that's one of the first things. It's both of those concepts are first class citizens. So if you define a feature in Beam or in Flink, you can support any kind of bounded and unbounded data source. So that was very powerful because it means that scientists only need to define it once. So we scrapped Spark for that. Although Spark has made a lot of progress in the last couple of months and years. And the reason we chose Beam was because of Dataflow. So Dataflow is a managed service on Google cloud where it allows you to run Apache Beam code. So one of the things you wanted from this platform was to abstract away in the engineering and the infra. And Dataflow has incredible amount of power in scaling and abstracting away a lot of the pens. So it's kind of like a serverless platform in a way because you're only defining your code. So that was the main reason for choosing Dataflow. So once you had feature definitions defined only as Apache Beam code in Dataflow, you have a consistency automatically between serving and training because now if you have your data stores downstream from that, they always get identical data. It's up to you how you store that data but the transformations happen before it gets to the stores, not after. So this is a very important point. And if you architect this correctly, you can replay the whole feature creation process from a captured events that you've captured on cold storage. So it's deterministic and idempotent. Just an example, hope you guys can see this. This is what a feature definition looks like in Beam. So in Apache Beam, just to reiterate Apache is the API, Apache Beam, and you run it on Dataflow. So Dataflow is the runner. This is a Python example. Typically we write Java code for the feature transformations because the feature set is larger. You can't do windowing in Python on Beam but this is a good example of what a feature transformation looks like. You just have trip events coming in. You do subsequent basically lambdas. In this case you filter out the successful trips. You build a data structure there which is basically a key value of the driver key and then the value one and you do a group buy by all the events for that driver over the day and then you're left with the trip count for that driver. So this is just a simple example of a feature that is essentially taking a collection of events doing a transformation and produces another collection. And these collections can be transformed over and over. So at a high level, this is what the creation process looked like for us. But what we did is we separated the creation and our core platform. So we don't wanna limit ourselves to only having one way to create. Sometimes people wanna create features offline on a CSV and just import it into our system. So we also added ingestion capabilities. We also have our own internal lambdas that we're building as well. So we support multiple ways in which people can create features but this is the primary way. So this is the first building block and that's creation. The second one I'd like to talk about is storage. When we got to storage, we realized something else. We're gonna have all these components in our system and they need to be able to talk one language between them. So if you're talking about a feature, what is a feature? Obviously we know that that's an input to a model but the system is a way to understand what the bytes that are being streamed into it is and how to validate whether it's correct. So what we did is we talked about how do we define what these features are and we broke it down into at least three specifications. So our system has these YAML specifications which I can't show the exact YAMLs now but in a month or two we're planning to open source these and basically you have base entities, both entities as in drivers, merchants, customers, in area and time for example and composite ones. So you can have one say driver area. So these are the entities and the features on that entity for a driver in a specific area. So those are specifications that we create and our system, once it's defined as a YAML spec it's in our system and then we create feature specifications and this is the interesting part. So that's where you define what data structure the feature has. What is the distribution of the data in that feature? What granularity is the feature? Where is the source code located? Who is the owner of the feature? So these YAMLs are very important for us because it gives you that source of truth, the master, it's like a master manifest on what is this feature. And one thing that's very important to know is that it's very difficult to trace the creation of a feature all the way from the source data. So your system needs to be built in a defensive way and this is the kind of way that we define it but creation can happen from any source. So we can't necessarily trace it back to its origin but we can at least say your distribution of data is correct, your data structure is correct and you have an ownership, et cetera. We also use protocol buffers or protobufs all throughout our system. So we want two reasons. This is a lot more, less data usage. One, and we have many different languages in the system. We use Go, Java, and Python depending on which layer you're talking about. So this allows some interoperability between these components in our system. We have a command line tool, we have a UI, we have ingestion, we have access layer. All these layers are not necessarily built on the same programming language. So we store the data of the features also inside of our stores using protobufs or we encode them. So from a storage standpoint what do we need from our training store? The first thing we need is it has to be easy to use. The reason for this is because a feature training store will not necessarily have all the data that data scientist needs. They will maybe have labeled data that is external to the store. So they'll need access to the store either through a UI that we've built or the databases on UI or command line tool and they'll need to join the data onto labeled data unless you have a way of importing the labeled data into the store. But in our case, that's not the case. So we knew it needed to be easy to use. Secondly, it needed to scale to a very large amount of feature data something I already touched on earlier. Scalability is a big concern for us. And then we needed some types of queries and transformations. We needed to be able to store historic feature data for months and we needed to be able to join that feature data and sometimes with fuzzy joins onto labeled data. So imagine if you have a customer buying a dish. He buys it at one time but you don't have a feature on that exact same time. Then typically a data scientist will say well if it's one minute later the feature data at that point in time is maybe good enough to train the model. So you want sometimes some fuzzy joins on that. So the training store needs to support that. So the one we actually went for to no big surprise was BigQuery. So BigQuery was where we were already storing our labeled raw data. So in our data where as now if we have our feature data as well as our raw data it's a lot easier for them to generate training sets. So some of the benefits of BigQuery is there's no infrastructure to manage. It's completely hosted and managed. Data scientists understand it because it's SQL. It's easy to use. It scales, it's integrated with GCPs, integrated with TensorFlow and Cloud ML engine and all the tools that they want to use. It's what's cool about BigQuery and why it makes it so useful in those cases that features are typically columns and databases and BigQuery is a columnar store. So queries are very efficient when you are selecting specific columns in BigQuery and that's one of the key things that we also found that makes it very compelling. We looked at Presto and Hive and some of the other solutions and a lot of them were compelling and they actually had features that BigQuery did not have. Two reasons we didn't go for them was in our tests for our use cases they were not as fast as BigQuery and two, they required infrastructure management. If we had the liberty of using other cloud providers then we might include different training stores but the moment we're mostly GCP and so this was the best tool for the job. So from our serving store we had more requirements. Low latency reason, right, so this is very, very important. Typically when you have, for example, the homepage or application, if you have food recommendations you want to immediately send a response to the user. Otherwise it ruins the experience and you might only have about 40 or 50 milliseconds to produce a response because it's not just your ML model that's just generating the responses. All kinds of other systems that'll be building that UI. So if you're on that critical path the more time the feature lookup takes the less time your model has to produce a prediction. So having a below 10 milliseconds was our target. Very high throughput, depending on the features this could go up to hundreds of thousands of kilo cups per second in peak times for a single feature. Scalable storage for the persistence layer we needed to be able to store terabytes of data and ideally managed as well. So the two options we, or the two choices we made were using Redis and Bigtable. So I'll touch on Bigtable first. Bigtable is interesting because of its scalability and its performance, it allows you to read and write into keys at a very low latency, 10 milliseconds guaranteed as long as you space your data out correctly. Each node can handle 10,000 combined reads and writes per second, which is very high. And it's very hard to find any open source tool or a system that does this. And we looked at quite a few. So we looked at Cassandra and we looked at HBase and many of these and Elasticsearch and the performance didn't compare to it.  So for us this was a no-brainer in terms of our primary store for our real-time store. But in some cases, let's say you've got driver location data or speed, velocities, altitudes and things like that, that change at such a high rate. You don't wanna have to create 20 Bigtable nodes that'll cost you like 30,000 USD a month where you can have like one or two Redis instances. So this is for data that changes very fast and you only care about the latest data point. So we also added Redis as an alternative. So we used Cloud Memory Store, which is the hosted Google Cloud version, but you can just use the normal Redis. So Redis is just our non-persistent, higher throughput store that we use and this essentially allows us lower latency access on our data. So the third component is access. So before we talk about what we did with access, we can first talk about the problem. So what happens when a sports stadium empties or a music concert empties is that many of our customers will open their apps and they don't make a booking and the same drivers will be sitting there. So this will make a request to our systems and the system will do future lookups for all those drivers. But there's a big overlap, right? Because of all the customers in the same area and all the drivers in the same area, the same lookups will happen. So we knew the access layer needed to handle this kind of load balancing and caching and have all these features. So just a quick example, if you're at our load, we ran the math and at peak times we could have up to 150,000 key lookups per second for a single feature if we didn't have an access layer. So that's completely impossible to, not impossible to handle, but it's a bad idea from an engineering standpoint to not have an access layer that can fix that problem for you. So we built a serving API at the front. It does intelligent load balancing. So we have our own query structure. So when an external system calls our feature serving API, the specific query is sent, the feature serving API deconstructs that query and then it first looks up, it does things like user authentication and rate limiting, but then it looks up into a cache whether the answers already exist. And then to talk about our feature specification earlier that specification has a time to live. So it will tell the serving API, hey, this feature cannot, can only be cached for 10 seconds. Don't cache it for five minutes. So that's how these systems interact with each other. So the feature serving API does things like load balancing. It knows where all the data is stored if you're sharding it, if there's replication factors, all these things. And it obviously only interacts with our serving stores like Radis and Bigtable. So that's what we did for the serving API. And then onto discovery. So the interesting thing is now if you have these three systems like creation, storage and access, you actually have a lot of data about what features exist in the system, the source code of those features, who the owners are, the statistics about the feature, like how much events are being ingested for that feature, how many files have been ingested, how many rows and CSV records, how fresh or stale are the features. If a model's being trained, you know the performance of that model even before it goes into production, you know which features were the best in training that model, which you can really in a relative way rank them. So you can store all this metadata about features and you can just log it to the console and they know that it just dump it in a database. And then so what we did is we lump all this data and we bought a database around this and then we expose the data. Unfortunately, it doesn't render as well, but essentially what we did is we have a way in which just with the simple BI tool, data scientists can query all existing features that we've created. They can drill into the features and they can see feature information like the types, changes to the feature, the versioning, what validation has existed, what errors are there, how much data exists, so at which time ranges, which gaps exist. Then also interestingly, you can say, on average, how good is this feature in its use for training a model? How many hits is this feature getting on a serving API? So this kind of information to data scientists is very valuable because they can just rank by the best, select like 50 features, train the model and that's a good place to start instead of having to go and work from scratch in every project. So this is one of our main focuses is building a richer interface to this, but once you have a unified platform then this becomes one of the big selling factors and it allows for a lot of discovery insights and it allows senior data scientists to disseminate their insights basically to the juniors. So we don't have the model in our UI, they could if they looked at the logs. So we do have an event log in which they can drill into and go and trace it back. So this is one of the things we're developing still and hopefully to have it more enhanced. So impact. So when we started, we said that the data scientists were spending a lot of time on engineering related tasks. So building pipelines and setting up the infra, they spent a little bit of time on making features but we wanted to compress the time here a lot. So the two problems we wanted to solve was service generation which we didn't cover in the stock and then the pipelines and the ETLs for feature creation. So ultimately what we had built is a system that allows you to take features, sorry, raw data in our stores. We standardized the way in which they create features with data flow as feature transformations. This allows bounded and unbounded data. So you only have to define it once. We have serving and training stores with Redis and Bigtable and then we have a training store with BigQuery. So BigQuery has all our historical feature data for four months. It also and then Redis we have latest feature data that's not persistent in Bigtable. We have also time series data that's available in serving. We put a serving API there. So feature lookups come from our production systems. It's handled. So as a data scientist, all you need to do now is one, go to this feature explorer, find your features, export it through BigQuery into a file, train a model and then you can put in production. You don't have to build any new systems in production. You don't need to build any features actually at the start but if you do, you can go to data flow and write code there to build those features. So then another thing we're looking at doing now is to simplify the process of the feature creation. Temporatize it. So another idea we had was if you create a feature, you can actually generate more features from that. So let's say you create a booking count for a driver or a trip count. You can, the system can automatically do things like averages, minutes, maxes. It can aggregate over time periods. The person doesn't have to define that manually. I'm not sure what you mean by feature selection. So we have the creation part here where it's that really raw data that you transform and you make more raw data and then you comply with the specific specification of the feature and the data structure in the protobuf and that gets stored. Then the selection part is basically data scientist going into the explorer, the feature explorer, finding the names of the features and then going to BigQuery where the columns exist for those features and then he joins that onto his labeled data, exports it to CSV and then he can train on Cloud ML Engine. He can train on Spark or wherever he wants to train. Eventually, yes. Like in the ideal world, everything is in this platform but at the moment we also support legacy systems and that's why it was very important for us to support BigQuery because now that the scientists can have their own airflow ETLs, for example, or whatever they're using for automation or orchestration and build their own features and then as long as it becomes a table in BigQuery or if a table can be federated onto CSV, then they can use that as well but that's not a long-term solution because you lose the discoverability and the definition of what that feature is. Yeah, so at the moment, it's an exporting process. Once you've exported, then you lose all the metadata about those features and where they've come from. So there's no way that we have the moment that you can differentiate between side input features that you've created and features from the feature store except if you're just exporting everything from those that dataset in BigQuery where these features are stored. I'm not sure if I understand your question maybe we should pick this one up afterwards. Okay, okay. So finally, we did compress the time to market a lot. Data scientists after deploying the feature store, feature platform was a lot more focused on modeling. We also spent time creating features but these features actually end up impacting the business a lot more and data exploration was another thing that they could focus on a lot more. So the impact on Gojek to wrap it up, foster time to market, obviously we're spending this time on projects, improve customer experience because our models are more accurate, the feature data is more, there's more features available. Because of our international expansion, this project helped a lot with data scientists supporting more customers. You can't just double the data science team every time you expand into a new market and because we're using BigQuery, Bigtable and Dataflow, we have less infrastructure to manage. And as an engineer, this is very compelling to me because I don't wanna have more terraform files and more infrastructure that can go down in the middle of the night. Right, thank you. We have time so I think we can find any questions.  Hi, the feature selection platform is like a great thing but how do you handle the entity feature relationship if a data scientist has a data scientist? I'm trying to do a trip analysis and trying to select a feature which is the driver level. It may be an inaccurate feature or I might need to do some aggregation or some modification to that feature. So how that's controlled? Is it handled by the feature platform or data scientist has to do it? Do you mean entity creation? Let's say there is a feature which is at the driver level. Yeah, yeah. So it could be like a... Yeah, yeah. Okay, so you have an entity for the driver, obviously, and then a feature as well. You'd need to create two specifications. It depends on what data type it is. If it's something simple like an integer or an N64 or a float, then you just create these two yamls. It's a merger quest in Git. You can do it and then an engineer reviews that. Once, is that your question? Yeah, it's handled at the feature platform or it's handled... Platform. Okay, it's... Well, there's a check because the merger quest in CI needs to make sure that that specs are integrated into the Integit, to get a source of truth. Then it gets deployed into the feature platform. Once that's done, it actually builds tables. So in Bigtable, it'll add columns. In BigQuery, it'll create data sets and tables there. If you've previously had that feature and deleted it and created it again, it'll tell you this column already existed. There is data from maybe a month ago, so there's a conflicting name. So there's all these little things that also need to be taken into consideration. Do you also record the access time? For example, one particular feature might be easy to fetch and retrieve in a real-life solution, but some of the feature might take more time because they have a computation behind it. It may take more time. You mean the creation or the access? Access part. And when I'm trying to access it in a particular model, the feature platform, does it also tell that there is a likelihood that these features can be accessed very fast and these would be slow? We don't publish how fast a feature is available. We do publish the information about granularity and the freshness of the feature. So we have a limit, let's say, of the age of a feature. So let's say an event is created. There's an event time to that, right? So if it takes too long to propagate through the system and to get into our store, then we calculate that you're not gonna be happy with that feature based on your requirements. That's available, but there's no UI for that. And your other question was on the lookups. Yeah, so the same thing, that is this information available as part of the lookup? We do track feature access and statistics about the performance. So we do observe that. And that's very important for our debugging of the serving API. That's the first thing we bought is when the query comes in and it's deconstructed and there's lookups happening to the stores, where are the bottlenecks? Where are the hotspots? Because often it happens, especially with big table, is that certain hotspots are very slow, but other ones are just perfect. And fixing that problem is actually quite a lot of work. But yeah. I think my last question is around the data privacy. So you are dealing with lots of customer data and the driver data, which is very personal data.  And do you face any kind of challenges in terms of a legal agreement or a kind of enterprise handshake you require to ensure that your data is protected enough and is used in the right manner? Yeah, so there's an internal team that handles this for us. But they follow all regulation for the different regions we're in and they layer access within the systems. So we have to stay within those streams. So we can't have cross-layer joins, for example. I hope that's enough detail. Yeah. OK, so the specification is more like a high-level manifest. I can show you an example, maybe, off through my laptop. Technically, I'm actually supposed to. We're going to open source in the future, so this won't be a final version. But it's more like a YAML that's basically a manifest that says who's the owner, what data type is it, what's the class path of the protobuf that defines that data structure, what validation hooks need to exist, at which points in the system to check the data. Like they're just... OK, so the feature specification, one thing to be clear about this, this doesn't tell the system how to create the feature. It only tells it how to validate the data that it comes through. So all the different components of the system, like serving, will take that feature spec in and then alter itself. It'll build types and checks based on the specification. And then when lookup is done, let's say from Redis, it'll validate. If the specifications say validate lookup, there's a time to live, there's a cache, a write or read time for a feature. All of this is defined on a specific for a feature basis. So in ingestion, it uses those specs. In lookup, from serving, it uses those specs. In discovery, it uses those specs. Even the UI uses the specifications to generate the interface. This is the one we're building now. Yeah. So at the moment, that's quite rudimentary, but it's basically validation on the data. So we allow engineers, platform engineers, to define functions. And then within the YAML spec, that function can be called based on the content of the data. And then it's basically a predicate that says, this is good or bad. It passes the validation. So data scientists or engineers can define those functions. Typically, at the moment, it's just the minimum and the maximum of a value. Like an integer cannot be higher than 100 and not lower than zero for a specific feature. And then the serving and ingestion pipelines will make sure that those validation hooks are called whenever the processing happens. Is this a distribution of values? Yeah. More than value? Yes, it's values. Yes. Yeah. Excuse me. People ask a question. So you spoke about the area or the location specific features, right? So a lot of times the granularity at which you define it, let's say you're defining a origin destination level feature, the number of combinations can just explode. Yes. You might have to put in some logic to prune it in some manner. Yes. So is that part owned by the feature platform or the data scientist? And how is that part managed? Yeah, so that's a good example of one of the most painful features is a composite entity between an origin and a destination. And if you have locations in space, there's just too many location permutations. The key space explodes. So you need to bucket them. And that's what we did. We bucket by time and we bucket by in areas. So in Jakarta, for example, we'll have these football field sized areas. And if you have composite entities of origin destination, you have to group the areas, the GPS locations in these football field sized areas.  And then the key space fits into Bigtable. It doesn't fit into Reds. It's still terabytes and terabytes of data. But that's why Bigtable was very compelling for us because of the amount of data we could store. But you're right, that is a big pain. And it's everybody's job to work that out because creators can create features that fill up the system. And our system needs to defend itself against that because creation is relatively open at the moment. So you were mentioning something about Dataflow helping you manage the differences between the experimentation state and production state. Can you elaborate on that? The experimentation? And production state, you were mentioning that Dataflow is helping you solve that problem somehow. Can you elaborate on that point? So experimentation, like it does help us in consistency. That's the world that's managed and it allows us by moving. This is the answer to your question. By moving the transformation from separate training and serving systems to having to transform, like if you have a model serving application and you have your transformation inside there, that's disconnected from your batch pipeline that has a transformation. Dataflow helped us by moving an upstream. You only transform once and you store the values, the refined feature values in all those stores. I don't know if that answers your question. Can you take it as a thank you? Okay, yeah. All right, thank you. Thank you.


Thanks for everyone for attending. Karm will see you today to make a public plea in relation to any information in relation to the death of her brother, Tony. Tony McGraw was, we believed, killed at his residence at 93 Maynard Street at Woolloongabba on Tuesday the 21st of May. Tony was found, his body was found under his house at that location two weeks ago today. He was found by a friend at about 6pm on the 23rd of May. We're investigating that murder and we've asked Karmel and she's, thank goodness, or luckily for us, she's agreed to attend. So I'll just ask for any questions of Karmel, thank you. Yes, well my brother and I were the only two living children of my mother and father so we were very, very close. My mother and father had lived in 93 Lower Maynard Street and after they passed away, Tony lived in that house to the day of his passing. We went to local schools, we went to St Luke's at Buranda, we walked to school every day and then Tony did his high school at Villanova. Tony left halfway through year 11 to take up a position with Australian Taxation Office and he worked there for 39 years until he retired two years ago. But while he was at the Taxation Office he completed his year 12 certificate, the senior certificate it was back then, and then went on to university to study a Bachelor of Business. Also around the time, it would have been about 18, he joined the Referees Association and he had a long-term association with the referees. He was very much a family man. He was my greatest confidant and friend. He was a loving brother, a loving friend, very loyal friend. We were brought up with very old-fashioned values, we were brought up to respect other people's space and possessions, but if anyone ever needed anything, he was always the first serious to joke to me that anyone on the Referees Association that had ever laid a slab of concrete or did any renovations or ever moved, he'd moved all of them. And the same with us and our family, if anyone had something to be moved or we had anything to do with the family, like any weddings or anything like that or even with my husband's funeral, he was always the first one there to assist us. So very loving family man. Yes, certainly. Do you have any enemies? No. Do you see any sort of person who might have any enemies? No, he was a peacemaker. He was so non-confrontational, so I can't get the words out. He was a very non-confrontational person. And I think that's the one thing that anyone who's known Tony, we can't understand why. He was, as I say, the best adjective I can think of is peacemaker. He seemed to have came to himself a bit calm, was there any particular reason why? Well, I don't think he did keep to himself, really. I think I did read a report, one of the very few reports I've read that he was alone, but I don't think he was. As I said, we had an old-fashioned background bringing that we respected other people. But he went to referees three or four times a week. You know, his funeral were 300 people there at the funeral. He had many friends through taxation department. I think what he had, he had long-term good friends. Well, he'd refereed since he was 18, so he was 57 when he passed away. He was a life member at the referees association. So we started off as a referee, and as the years went on, when he was made a life member in 1992, he then wanted to mentor young referees and got on to the admin side of the referees association. So he'd been on committees, and he was in development groups with the referees. And when he retired, he retired in 2010, I think it was. And just after that, in January 2011, the floods happened in Brisbane, and their referees association ground was impacted. So Tony then did volunteer work. Like, he was there every day to help build up the grounds. He then coordinated in getting bookings for the referees to, you know, to raise revenue. He was instrumental in the canteen. He stalked the canteen. And he was one of those old-fashioned people that you hear about that volunteer with no monetary gain. So when we say that he was a loner, I don't think he was a loner. He was just a quiet man, a very private man, but he had a lot of acquaintances. What was the last thing you said to him? Do you remember your last conversation? I remember the last thing he said to me.  I lived near the bay side, and we had a favourite fish and chip shop. And he said, I'm going to specifically ask to watch this young referee so I can come and have fish and chips with you. And as he drove me back home, the last thing he said to me is, thank you for taking care of me. Because he had stayed with me for the five and a half months of his recovery after the fire. And how did you hear about him? I found out ten days ago the police came to my house and advised me that my brother had, did I know, Anthony John McGraw, and that he was deceased. But it's devastating. This was my last relative. There was mum and dad and Tony, and I, this is the last person. And that was our family home. And all my brother was doing was driving into his driveway. He was shot in his driveway, underneath the house. It's devastating, and it's, I'm so unnerved by it. And I just want to know why. And that's why I've come here today. I'm appealing to anyone in the general public who Tony might have spoken to who has confided in to help us find out what's happened. Because we all should be able to feel safe in our own home. There was a fire in the house in October. Again, I didn't have my radio or television on that morning. And one of my sons called me to say, mum, there's been a house fire in Wollongabur and it's uncle Tony. We need to get to the hospital straight away. And so I did, I went to the hospital and I had to identify, identify he had come in as unknown. When I first got there I thought the worst. But when we went into intensive care they had the chip down his throat working on him. And they told me that they had to have to go in and try to save his toes. So they did that. They had to amputate three toes on one foot and one on the other. And that night when he came out of surgery he had no memory of the fire for none. He doesn't remember, he remembers waking up in the hospital room with the missing toes. But he was in hospital in Royal Brisbane for about five weeks. He had to have a skin graft. And then when he came out of hospital he came to live with me because of my floor plan. Like he couldn't walk stairs or anything like that. So he came to me for the five and a half months to recuperate. Did he ever think that someone had done that to him or that he thought someone was out to get him? No, he felt that he had angels that night because basically from what we gather I'm only telling you what Tony told me. My first impression when I got to the hospital that night is that I, my beloved brother was alive. But he thinks what happened is he's got up during the night and walked down the side of the bed. There was an old power board or frayed power board. He stepped on that and he was thrown back on the bed unconscious. It happened about two o'clock in the morning and please I don't know if I'm spot on but the neighbours next door had saw a flicker of light and they'd come in to see if he was home. And they didn't think he was, but he coughed and they got into him. But he was very lucky to survive this scene. But he just said, I've had a second chance of life. Yes, he was still undergoing recovery. When I say he would have had ongoing recovery. He was still going back to the Burns unit for check-ups and he was under his own doctor at Cooparoo. So to you this is just a complete... Absolute nightmare. Can't, cannot believe it. Are you scared? I'm more unnerved and scared because then this is... It's unnerving to think that you're not safe in your own home. I think we all should be unnerved. I really do, you know. It's more unnerving when anyone passes away.  It was my birthday yesterday, Tony, and now we're going to meet up last night for a steak. We had planned that. That was unnerving too. That's the last time of my... I have children, but that was the last time we made arrangements to meet. And that's why it's so important. I'm appealing to anyone to assist the police. None of us should feel scared. None of us should feel unnerved, you know. Tony was a good and decent man. He worked hard all his life. Colonel, how do you feel knowing that somebody hunted your brother? It wasn't a year to be around your Kenny, but this was a targeted, strategic attack in the place he was most vulnerable. Well, I'm not sure if he was hunted. I don't know that, but what it does... I think I just want to know why. And how dare they?   He'd gone through the skin grafts and the pain of going through that. He'd learned to walk again. He was driving again. He was back in the referees. He was so proud when he was voted in as president. How dare someone take that away from him? That's how I feel. I'm not that hunted. I don't know about that. Again, I'm just appealing for anyone who saw anything or Tony might have said the least little thing. Please get in touch with the police so we can get closure. Yes. I've spoken to a lot of his friends and also I caught up with some of them at the funeral. Yeah. Everyone says the same thing. Why Tony? Why Tony? Well, all through his life, he was a teacher and a mentor.  And that came through with anyone in the referees or with the in taxation department or even in the family. He was just, you know, a peaceful man, a peacemaker. He was my confidant. So if I called him and said, oh, look, this is happening here. You know, think of it this way and, yeah, that's what I mean with the peacemaker. He wasn't aggressive. I've never known him to be aggressive. Tethys, I don't know what you make of this. How does this murder stand out in contrast to some others? It's certainly baffling at this stage. As Carmel said, Tony certainly didn't have any enemies and we've been investigating this intensely for two weeks and certainly we haven't uncovered any enemies as such. He was a clean living man, honest, hard-working. Not a recluse as was first reported, as Carmel's alluded to. So it's very frustrating from us. Is there a possibility here that this might have been a mistaken attack? Anything's possible at this stage, yes. One detective was reported as saying that he didn't think it was a random attack. Is there anything to suggest that, you know, this was someone, is there something specific that suggests that it was someone that he knows? From witnesses it could well be that because of a brief argument that neighbours or witnesses have heard that we're hopeful it's not mistaken identity and we're certainly hopeful that it's not a random thing. So if there was an argument and a confrontation then you would assume, I would think, that they were known to each other. So that's right, yes. There was no words heard, it was just witnesses that had heard raised voices. Again, that may or might not have occurred on that night. People perceived different things while looking at all that. No, they weren't. It seemed to be male voices, but again that's not certain from the witnesses. Were these only reported after his body was found? Because? Were these the after his body was found? Yes, yes. Have you looked into that? Yes, we have. We're looking at all angles of Tony's life. So was there any cases that he was put on? Was he assigned to you know, audit particular people and stay on that case? Probably calm, I was only talking about this earlier so you probably best answer that. Tony did a lot of work in project work. So Tony retired, it was a voluntary retirement. He decided to retire it in 1955, so but previous to that he'd been working on project work with the GST in superannuation and he was also doing training. So, and that was two and a half years ago. Extensive have the search been for a weapon and have you got any closer to defining on that local area? It has been extensive the search. We call on the SES who are wonderful. They searched that area of Norman Creek, if you know it it's heavily mangroved at the bottom of Tony's street and he lives a short distance from there so clearly if anyone had gone in there on foot which is a possibility too from Dession Street or nearby streets we had divers also search the creek there thoroughly and interesting enough since the floods the creek was quite clean so they're 95% sure that that's been searched thoroughly and at this stage the firearm hasn't been located. Yeah, we do. We have a major room set up at Dutton Park Police Station we've got the assistance of the Homicide Squad the numbers of investigators and police involved vary from day to day with resourcing but we're putting as many resources to it as we need can't rule out anything at this stage, unfortunately. Would you describe it? Is it literally stage and history? Yes, yes. It's not a particularly nice question to ask but what style of killing was it? Was it cold and calculated? Was it a brutal slaying? One shot, what sort of kill was it? It was one gunshot wound to the head all we know is it was brisk execution style whether it was out or not or just a passionate thing we don't know. Do you know what some gun was? We believe it was a low-calibre low-calibre weapon, possibly a pistol or a saw-in-off weapon but again that's open to debate. That's right, yes. We believe it occurred at 9 o'clock on the Tuesday night and on the Thursday night a friend of Tony's from the Referees Association went to look for Tony and he found him there about 6 o'clock on the Thursday night. We've had some calls to crime stoppers but yes it is frustrating that Tony would like more information. Is it a case that you're out of leads now or are you still pursuing some lines of inquiry? Are you literally desperate at this stage? Look I wouldn't say there's lots of lines of inquiry we're still running but we would like further information with new leads clearly. Sorry other than the argument on the night was there anything else unusual reported by neighbours or the other sides of any people or cars that have been reported? No, no. Carmel said it's a bit unnerving that you know someone is out there from a police perspective are you concerned for the public living around that area or are there any fortunes that should be turned from a message to them to a lot of things? Personally I don't think it's a random attack that's my feeling but certainly it's concerning the public that someone can do this and they're walking around I mean it's clearly worrying to everyone including ourselves So we know that Yes, that could be right and we haven't exhausted all the inquiries there's still lots and lots of work to be done with our methodologies and whatnot there's a hell of a lot of work to be done but at this stage we just don't know what direction to look in. I think that's why I'm here today because they know anything sometimes it might be so insignificant but it might help the police so we're asking anyone who's had any contact with Tony within the last four weeks of his life to come forward and help us just help us find out what happened who did it please, please, please help us Basically on the family liaison officer and also just the system with the investigation itself I'm sure there will be We're quite fortunate where it's happened is there's extensive CCTV in that Deshyn and semi-industrial area and the residential area around there we're very fortunate in that regard we just really need that civil bullet of someone with a bit more CCTV that's clearer a lot of it is a lot of it is that's clearer a lot of it isn't clear some is clear so that's going to be a great source for us but obviously that takes a lot of time to review and analyse look we're still examining that we're still results of pending on a lot of that stuff it varies it varies from times and when we had searches we'd have up to 50 police normally in the major incident room the investigation centre we'd have 20 to 30 20 to 30 years I think everyone was shocked that was the overriding comments at the funeral and everyone was shocked and couldn't believe it it was quite a large funeral it was a requiem mass because we wanted he'd been when we found him he'd been dead for two days it was a Catholic ceremony but everyone everyone came up and said to me we can't believe this has happened this was your last connection to your original family you must be feeling lonely at this stage well I am I am my mum and dad and Tony and I were a unit my mother used to buy casket tickets in the olden days when we had caskets in the syndicate was Cage, Carmel, Anthony, George and Eunice and he left yeah it would have been I can't think it was it would have been probably 10 days before Tony passed away but we were having a one of my granddaughters was having a little birthday party and I texted him we would have seen him then would have been about 10 days before well he refereed quite a few years ago he was in the admin side and he was the president of the referees association he was working towards building up the ranks and mentoring young referees and bringing people through the ranks that's what he was doing and also in the admin work building up the fields and getting the canteen to be viable and also looking out the fields to get more revenue for the referees so he was an all in admin I can't remember I'm so sorry


So I got a overwhelming amount of people who a little fit to be tired girl. Do y'all know what that means? I know I'm so country who are a little bit not so happy at the thought of me discontinuing my cooking videos So I won't slow down. They will continue, but they would not be at the It won't be as much as I was doing before I think I was doing release in the video every week Sometimes twice a week. It will probably be once every two weeks Now what I'm going to be making for y'all today is something I thought off the top of my head I saw a couple of people make this on YouTube. Sorry. I keep looking at the actual camera I'm using my backup camera right now. I saw a couple of people make this, but I'm gonna put my own spin on it So as far as spices goes we have red pepper flakes black pepper Coriander garlic powder onion powder and turmeric. We have half a head of cabbage here We have some green onions kind of getting that order as some celery some carrots over here We have ginger. We have garlic. We have half an onion. We have soy sauce chili paste And some stir-fry sauce. I'm wiping down my counters real quick But let me tell you what I have going on in that skillet I have the chicken that has been seasoned with the onion powder garlic powder and turmeric I have some veggies going in next the onions the garlic the ginger and two sticks of celery, okay? All right, so my chicken has been going away for about 15 minutes So now I'm gonna add my veggies as you can see there. I have a pot of carrots in the back That's about three carrots that have been sliced up some sesame oil I know this is different change in the recipe as I'm going sesame oil for my Diced up cabbage here. I should say chopped up cabbage putting in some garlic powder some onion powder I'm gonna add some soy sauce you guys I rarely use salt if I do use salt is gonna be on pink salt putting the lid on gonna lower this down So that it can go away for about 15 minutes now I'm gonna add some stir-fry sauce about one fourth of a cup of stir-fry sauce You can add any type of sauce that you want it doesn't have to be this particular sauce I typically do like to make my own sauces when I'm cooking just because of the amount of sodium There's in a lot of these bottled Sources, but that's okay as you can see here my chicken is kind of sticking to the pan All right So what I want to do is go ahead and try to thicken this up The best way to thicken up any type of sauce gravy is to add warm up water You don't want to add cold water because it's gonna take a long time to warm back up I should say so adding some warm water right from the This pot of carrots here. I'm gonna add Eyeballing it about half a cup. You may not need to do this again You may not need to do this about a half a cup Add some water on this side of the kerosene skillet moving the chicken around and I can see it's kind of sticking on the other side Go ahead and add some more water you guys now at this point if you want it to you could serve these all Separate because the cabbage cabbage doesn't take long at all to cook But what I'm going to do because I'm grating and lazy I'm gonna go ahead and spoon this chicken mixture on top of this cabbage at the carrots do a little taste season accordingly so Got a towel here So I don't burn my hand So I placed the lid on and 15 minutes later this was absolutely Let me tell you something I wanted to slap everybody's mama within a hundred mile radius the carrots were sweet the cabbage because cabbage is mostly water We got a lot of liquid. This would be great over some rice or even by itself. So this is what it looks like plated up I hope you guys enjoyed this video. Take care. Bye


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Hey guys, Realistic Gaming here. Today we're going to be doing some Roblox Thai scary stories. These are way different than like the Roblox scary stories because these are from Thailand. And these are pretty creepy man. So let's begin. Oh yeah. And I'm here with Twitter. So hi Twitter. There you go buddy. I'm here with Paul Hedgie, Loops, Kidkiller, Jelly, and Daniel Macal. He's rocking the shades. Okay so let's begin with Ogre. Oh my god that looks creepy already. Let's begin.  Here is a mother's home. Here this is my mother's home. Okay that house looks creepy. Let's go in there. Oh my god what? Okay. Hello? Hello mom. Boy? Mom? Uh oh. Something is definitely wrong with this mom. Okay mom what are you doing? Oh my god what is going on with this mom? Is that blood or she didn't catch up? It could be either. Uh scream. What? She stabbed him with. She stuck her hand right through his chest. What in the world is going on here guys? Okay she's eating his organs now. Oh my god so much for her mother right? And what is a girl doing? Why is still here girl? She should have ran away.  She's eating her own son's guts. Oh no. She's running after her.  Oh no. How did she become that? I don't know man. It says a few minutes later. Oh no what happened here? Wait what? So that's the girl isn't it? That's the girlfriend. So she turned into one of those things? Oh my gosh she got affected or something. Alright that was a crazy story man. So much for the girl meeting the mother right? Okay let's go with the next one. It's called kakai. I don't know if I pronounced that right but that guy looks freaking. He only has one leg. What is that all about? Okay. I am tired. I want to sleep. Me too. Okay so these guys are all camping man. In the middle of the woods. That's the kakai guy. Oh my god he's creeping up on them. Okay so these dudes are like straight up knocked out right now. Okay uh I feel sick. You should sleep a lot. Okay uh oh. So they woke up from their nap. And now they went back to sleep as you can see here. Okay let's go let's go let's go. Okay I want to pee. Oh no this is where the bad stuff happens guys. Once you guys separate that's when all the bad stuff goes down. Oh no baby. You should have just stood there with your friend. What is that? Oh dude the kakai is biting his leg. And he only has one leg too. What in the.  Let's go. Let's go let's go guys. Hey come back here. He ran away. He's like yeet I'm out of here boy. I really got a piece of his leg in my tummy.  And his belly and he does only have one leg. That's kind of strange man. If you guys know anything about these Thai stories let me know in the comments because they're pretty interesting. Okay now he is dead. Oh man come and rest in peace soldier boy. With soldier guy because we don't know his actual name. Oh no. No it's crazy. So the witch is out camping and then this kakai just ran out of his friend's leg and he ran away. Wow. Okay it feels eating spirit. Okay that looks freaky man. Let's go in here guys. Okay I'm lost in the forest. Oh no. I feel sad for this guy already. I would be creeped out of my mind being lost in the forest. Light? Oh there is a light. Uh oh. What is this? Don't tell me that's a girl. It's just a floating light. Why would there be a floating light? Let's go check it out. What? Oh my god. It's exactly like Krasu right? Oh my god bro. Look at that. Look at that. That is cray cray. Okay oh my what? She bit him. What is up with these stories and like them getting eaten by their intestines. Oh no. Oh my god come and rest in peace Dave because we don't know his name. Oh no man. Poor guy. He was just lost. He was trying to find his way home. He did not deserve that. These are traditional time myths. Yeah they're pretty awesome man. Golden baby. Oh this one sounds freaky man. Okay uh what is going on here? Oh look the golden baby is up there. Seems like this guy is breaking in to steal whatever this place is. Well he's gonna find no good in here. Lock? It's locked? Okay well you better watch out with that golden baby up there man. Oh lordy. Okay what? What happened? Oh the stuff got pushed around. Oh oh I think the golden baby is mad guys because he's trying to steal. What in the world? What in the world is going on here? The golden baby is right there.  Oh man. Little chuggy doll. Hi what's up man? Oh he stabbed him right in the eye. Well that's what you get mister for trying to come in here and steal stuff that does not belong to you. Oh my god his eyeball is on the floor. Somebody pick it up for this dude. Where is he? He's right behind you bro. He's up there.  Ah he can't move. Oh no he's dropped it on top of him. He dropped a shelf on him. Well that's what he gets for trying to steal man. And his eyeball is still over here. Look at this dude. It's so funny. Look at the eyeball guys. Okay well that's the end of that guy man. Rest in peace to that dude because well he deserved it because he was trying to steal. Breathe, breathe, breathe.  That golden baby is more evil than we thought man. That's crazy. Who would imagine such a little guy like him would do that man. Okay let's begin with these. Jen. This one looks scary man. It looks like it doesn't damage to cosita alien. Okay I stole from mom. What? Why would you steal from your mom? Where did you?  What is that with Froo Loops? What is that with this dude? Okay where did you get your money? He stole it from his mother. Why would you do that bro? Come on. Where? What is that? Where? What is he talking about? I don't see anything. I just see a full moon. I don't know what he's talking about guys. Let's go to the next one. Let's see what he's talking about. Whoa. What? What is that? Oh my lord bro. He stopped this dude. He just came in here and smashed him. Oh my god. Well that's what they get for being stealers. For being robbers. Why did I say stealers? Oh my god. I did not expect that. I was going to be like, I didn't know he was that time. I mean I should have known by the looks of the trees. Okay this is called taqian or taqian. Okay it seems like it's a tree. This should be interesting. Uh oh. There is taqian. Okay there's nobody here. Oh no he's peeing on the taqian. Oh my god. Ew man. This is so nasty. Uh oh. Let's go home. Yeah buddy you're not going to go home safe tonight because you totally messed up by peeing on taqian. Hello girl. Uh oh she's upset. She is upset guys. She is upset. Let me walk you home. Uh oh that dude has no idea what she's going to get himself into. Boy oh my god she picked him up in a choke hold position. Man this girl is strong. Or maybe she's the tree. Who knows man. Correct. She snapped his neck. Oh man. Rest in peace drunk dude. Because we never found this name on that. It's insane man. Oh baby. I'm going. What's up guys. Come on let's go. Let's go. Oh no.  Okay evil spirit. This one looks creepy. Like 100%. Let's go in here. I like these man. Whoever made these should make more. I really love these. I like walking at night. Well this night is going to be your last night buddy. Okay uh what is that? Where? I don't see anything. Uh oh. Michael Myers? Oh what?  Ah he's running for your life. Dude where did he come from? He just came in. Oh no he's going to slam his head. That's what I think. He's going to slam his head. Oh that was correct. Ah. Oh no. I am starting to forget about the energy. I did not forget about you bro. Oh no. Well most of these ghost stories have to do with them eating the person alive. That is pretty scary man. Yeet. Let's go. Oh what? Oh I got stuck up there. Okay this is going to be the last one. Tanny. Uh oh. Okay what's going on here? Time for bed. Hey wake up man. Let's go play some Roblox. Okay what? Yes?  Yes. Uh oh. She's winking. You know that's a sign for something fishy. I thought he was dead. What? Oh what a good dream. Okay so he was just dreaming? Okay. Hey Sarah can I sleep with you tonight?  Giggle giggle. Boy you are sleeping with her.  What? So she's mad because she slept with Sarah? She sliced his neck. Oh god. Oh my god. Okay guys say bye to YouTube. Say bye to YouTube guys. Say bye Twitter. There you go buddy. We'll let these guys say bye. And then we'll end the video. Let's press E wave because I like doing that little emoticon. Uh it doesn't work. Alright you guys well thank you guys for watching. Thank you guys for the amazing support as always. I really appreciate it. And I will see you guys next time. Bye.


Welcome to the University of Nevada. I don't know what a nobody has but today we're going to find out It's basically the guy that wrote the book on the Spanish language and the university is named after him because of its strong Focus on the humanities. If you're coming here to study anything related to humanities You're going to find yourself in a great place, but that's not the only things they have to offer Nevada also has really good engineering programs and it's also really well known for its fine arts programs In fact, just like watching around all day today I've seen a bunch of like dresses that have been made in old style And it's specifically because they're doing this collaboration to celebrate an anniversary It's like a really creative way to apply Your fashion design skills to you know the historical context of this university and speaking of that This building is awesome because it really combines old heritage with modern technology You see the old brick walls, but that at the same time in the classrooms You have these awesome touch screens and like, you know big projectors and so that you can review all your classes Like everything's recorded and put onto an app kind of gives me the same vibes as I got in Segovia the cost of the university is already pretty low for being a private university in spain So you're not going to expect huge scholarships But they do have some even they have one specifically for high performing Venezuelan students for example and we're working on a special scholarship with score Which i'm going to tell you about a little bit later now the de Brieja campus is an urban campus It has a few different buildings depending on your faculty this main one where i'm at today Has all the faculties of like communications and arts But they also have some really cool engineering facilities here And one of the things that impresses me is just how much they pack into this small space There's a ton of different laboratories here like renewable energy and chemistry and physics and a fab lab And while they're not huge spaces There's also not a ton of students here that are going to be competing for those spaces You get a lot of personal attention here at this university because it's a relatively small university Even though they have 12 000 students a lot of those are online and de Brieja offers a lot of their programs through an all online Environment or even you can take some of those classes online while you're studying here in person So if you need to travel for a bit and you want to keep your studies going you can do that Now most people when they come to spain they live in a residence for the first year It's kind of one of the easier options even though it's a little more expensive And de Brieja has a whole bunch of residences located throughout the city If you're looking for an easy way to integrate to spain That's the best way to start after your first year you make some friends Maybe you get an apartment together and that will bring your costs down too But the residence option is a great way to settle in for your first year when you don't really know anybody And you don't really know the city yet This is the part of the video where I would normally go ahead and talk to somebody from admissions But that person is back in Peru and de Brieja along with some of the other private universities that I visited on this tour Has satellite offices in other countries where you can register apply and actually like make all your payments and stuff Which is a pretty unique approach. I'm going to fly back to lima talk to somebody down there And we'll keep this video going before I go back to lima though I want to tell you something special a little while back I made a video about how these different college counseling companies work and how There are companies that take commissions from universities every time they send a student to that university But I thought how could I like turn this on its head and what I said instead was I want you to take that money and give it to the students Everybody has actually the first university to agree to be part of the score discount network What that means is that when you are with score and you apply it in the briehead you get admitted You're going to get an extra scholarship Simply because you were with score and that is really what my goal has been all this time is like I want the cost of my service to be less Than the money you save by using this service. And so that's what we're accomplishing right now Like nobody has the first one. I'm hoping to make a much bigger network The score discount network is officially on and I want to thank nobody for being the first to join and for having the faith in us to do this We're back in lima peru and I'm here because the university of nebideha actually has Offices in other countries to make the whole admissions process and even like making payments to the university way easier So they have an office in ecuador another one in colombia And one here in lima peru and there's even one in indonesia and another one in china like You can get in touch with nebideha in a bunch of different ways Now if you don't live in one of the countries with an office Of course, you can just go ahead and go to their website and send them your information and someone will get in touch with you But today I want to introduce you to your peruvian rep paula. So let's go inside the office During the whole process to its arrival in madrid Currently we have a meeting with the escort company and those interested who come through this company We can give you some discount bonuses during the first year in the monthly If you want to get in touch with us, you can look for us on the page www.nebideha.es or write us on the international email at robanebideha.es Our programs start on September 11th, however, we recommend that you do your admission process previously Until July 30th so that you don't have any inconveniences with your student visa. The university of nebideha is waiting for you. See you soon There's other companies out there that take commissions from universities And they tell you to apply to those universities because they're going to get paid and that's not how score works We want you to choose the right university for you So if you applied in nebideha with score, you're going to get a discount on your first year's tuition And that is something that nobody else is going to offer you And that's something that only score does because we care about you So I want to give a big thank you to everybody at nebideha paula here in the local office And everybody that we got to meet over in spain. It was absolutely awesome getting to know this university If you're interested in checking out more about nebideha, you can visit the local office If you have one come here and send us either oh if you're living here in peru And if you're in another country, you can go straight to their website and send your information But you can also go to prepescore.com and hit us up and talk about how we can help you Get admitted to nebideha with some serious discounts


can use in a wide area.  So we'll go into the skills, I'll give you all the examples, but I want you to have the right mindset when you're in this situation. So first of all, when I'm in this situation, a lot of us, we see the defender in front of us where maybe he's a little further back and we think we have to beat him.  I wanna put the ball into the box, especially if my teammates have already made the run. If your teammates have already made the run, you see this guy, now you're trying to beat him, they've stopped their runs, they've already gone into the box, now they have to shuffle back out, defenders are marking them, so you've taken too long to make the decision. So sometimes the best move, the best skill in this area, you just get it out of your feet and whip it into the box, put it in a good cross into the box. So play me another pass.  Well, if I have time, I'm just gonna play it, well, I'm gonna get it on my right foot and I'm just gonna play it into the box. Okay, so realize that your job as the winger in this area is not necessarily to beat the guy, it's to deliver the cross. Now, if the cross has to come early, because your teammates are making the run, where the space is there, then take that option. If, however, your teammates haven't gotten to the box, no one's making good runs, there's no options, well, now you're in a situation where, yes, I may have to beat this guy, so now I can get my head up, I can get a better angle and I can make a better decision and give it a better quality cross. So I want you to use moves, but I want you to use them when they're necessary.  Is your positioning, so we'll just wait for a second. So a lot of players, they might be marked by this guy, maybe the ball's coming across in the midfield, they're the winger, they're the wide player, they're receiving it like here. Well, he's already on top of me, he might get on top of my touch, he might force me to go backwards, I don't have the time and space to put in the cross or to go at him with speed. So what I want to do in this situation, as that ball's traveling, I realize that it might be coming to me and obviously this is a standstill example, I'm probably making a run, but you want to think about getting space for yourself. So as that ball's coming across, instead of trying to receive it here, I want to check out here. So now I have space to either put in that cross or I can go at him with speed and make my decision. So think about getting a little space for yourself. Now when that ball does come to you, play me again, if I'm deciding that I'm going to go at him because I know it's not the right time to cross, my teammates aren't in the box, then I'm going to do that without hesitation. So again, the ball's coming across, I'm finding my space. I already know, boom, I want to get at him. So I'm going without hesitation. I'm not receiving the ball for me one more time. I'm not coming here, receiving the ball, now getting my head up, now, okay, hesitating, hesitating, okay, he's closing me down. And even if I do beat him, where am I going? He's right on top of me. So go without hesitation. Think about getting your space for yourself. Analyzing, is it the right time to cross or should I make a move in this situation?


The big step in the right to readiness in college completion to the success of our young people and significantly to San Antonio's success as well. And so I want to first of all introduce and thank several folks who helped make this effort possible and four of our superintendents who are going to describe the collaboration that's happening and the implementation of policies and strategies so that we can reach that 85% goal. I want to first thank Special Assistant to Education Secretary Arne Duncan, Greg Narnina. Please convey to Secretary Duncan our appreciation as well. The Cafe College and then they get into our universities or community colleges and they're faced with a ton of remedial plastic because they're not adequately paired with courses but too many times they end up stopping out or dropping out and never completing their studies because they get discouraged. That's particularly true in our Hispanic community and so this is part of the effort. We blow us more than just in education and to talk to you about the strategy that our school districts would employ to ensure that we meet that SA-2020 goal. Let me bring up four superintendents who are doing a great job. Before I do that though, let me introduce Gerald Byrd. I forgot to introduce Gerald, the CEO of SA-2020 and driving volunteers to important efforts in education and otherwise and so we're very proud of the effort. At the heart of this though will be the role of our school districts. They play the most vital role along with parents and families in ensuring educational success and they're going to play a great role in this. So let me welcome to the podium Ray Monigal, superintendent of Perlendale and he's asking to come out. We're leaving the city, a city that really changes education, San Antonio and would like to thank everyone who was part of this process starting yesterday and into today and it's our privilege and our honor as superintendents to come out and be able to stake the goal and stake the metric and talk about some of the strategies that have been developed through the work and the effort and dedication from the many of you who are sitting out there today and this will be one of those things that changes the skyline of the San Antonio and the investment of youth is tremendous. So the goal that we came out today is to implement a college and career-ready culture in San Antonio. The metric to measure that is 85% college rate by 2020 and I get to announce the first strategy that the superintendents behind will reach them up in the next strategy. The first strategy to attain that goal is to infuse career guidance and exploration of cross-court content areas from PK through 12 in all of our collective school districts. I'd like to bring up Mr. Madrigal. The last days and all the collaboration and dialogue that we have I know definitely from Harding, I'll be certain 97% of Hispanics we definitely benefit from in the last days and we're able to take a lot of things that we've already discussed today here and yesterday back to Harding though so we can help our children. But again, one of the other measures that we agreed on was that we're going to train all teachers, administrators and all staff in all levels of rigor and readiness through the implementation of AP strategies in pre-K 12 classrooms. And now we're going to turn over to Dr. Woodst. Students that we serve in Northside ISD, I want to thank all of the members of the committee, the mayor and certainly his staff and all of the folks associated with this effort. We are certainly proud to be a part of this effort. Another of the strategies that we're going to implement to achieve our goals is to investigate a college and career readiness profile and to pilot it in our schools. We certainly look forward to seeing the efforts of that strategy and all of the others that you've heard about. Thank you. In the school district, this is a very critical time in our history and I can tell you that we have a school board and staff members that are very willing and ready to take our district to the next level. I'm very proud to serve as your interim superintendent. A couple of the other strategies that we're going to implement is to operationalize a student data portal to develop individual college ready profiles and also to investigate the administration of a common college entrance exam through school day testing. In a great city. In fact, we sought out students from the Art Institute who are college students such as those and we hope to help them reach through this partnership. We asked them to come up with a name logo to best represent our campaign to ensure that more San Antonio youth finish college and gain meaningful work. I am proud to introduce Anthony Falun who will come up and introduce his team from the Art Institute that created the great Diplomass logo that you see up here. It combines the words Diplomass and Mass to reinforce the message of the organization. Diplomass serves as a representation of all higher education. We have a bilingual element within the organization as well as a plan on the rise of Diplomass. So we thought I felt that I think great. The logo itself was drawn as a silhouette in order to represent and lead up to the viewer whether it's a boy or a girl. The left leg of the logo is stepping forward on to show forward progress in its first step to success. The graduate place in front of the student was to represent the position you'd be later on in life by receiving up with secondary education. Orange didn't write to represent the institution as well as the vivacity that is used for this collaborative effort. I'm going to introduce Doris Palos. It was a great experience when we look forward to moving forward with it. Thank you. 2010, Juan Zavolveda and Jose Vico as part of the White House Initiative were Hispanics and we were here with representatives from the national cities with probably a good number of you in the audience but over those two days a couple hundred people participated in the initial phases of the San Antonio 2020 educational plan so it's wonderful to see progress has been made in just a little over two years and that's only happening because of the people in this room and the fact that you have educational leaders who are engaged with these intermediary operatives of people so we have Judy and Jean and Hyra who are retitling operatives but they've been able to build a trust across this community the business community, the nonprofit community, the mayor's office, the universities, the school districts and so on and we've talked over the last couple of days a lot about collective impact and what are those distinct metrics that we as a community are going to hold ourselves accountable for moving and many communities are talking about that across the country but it's another thing to take it into action and so I just applaud you for taking those words and activating them and operationalizing them and having those courageous conversations on what does it mean to be ready for coming here what does it mean to be on third grade and track the reading level and what does it mean to be proud of your ready leaving high school and so I send again just applaud what's happening here congratulations on your childhood initiative that was passed last week that's just fabulous this doesn't happen without mayors and superintendents stepping forward and understanding of the future of their communities is the talent within them and so Mayor Caster I just applaud you again for driving that San Antonio 2020 plan and framing it because it's so much easier than all the other community resources to galvanize around implementation when there's that broad framework and I totally agree with your comments around district leadership and taking that framework and then taking it to the next level it's just so much easier to invest those resources and basically also understand what's the added value we've had a lot of conversations over the last couple of days about what is the added value of any particular resource to impact any of those specific measures so congratulations I look forward to coming back soon I love this place this time of year July is my favorite time DC is not my favorite place to be in July either so congratulations it's fabulous work and I'm going to take this back to something different so thank you from the Darylburg it's one of your videos on a race status in the city it's not hard to say that you're proud of the San Antonio particularly when we look at what's going on across all sectors of this city this city is one path to its next level of greatness I really really believe that I could not be more proud of the day to be a San Antonio people don't really talk about the investment in the most precious asset that we've got and it's our children and the most exciting thing about the work that we're doing today is the commitments that these superintendents and other partners and universities are making today is that this is an investment that is going to generate an enormous return on investment in all sectors of San Antonio the parties that we're setting that we're signing off on today are the parties that thousands and thousands of San Antonio except for themselves didn't come from one high or from the side one way or the other came from San Antonio themselves saying children and the investment in their future will generate the biggest dividend for this city so that's exciting so every action, every strategy that you've heard today when employed is going to get you a new one you a new one for the future of the city of San Antonio like most things we make commitments we've got to be serious about it and that means that we have to use pens and sign our names to commit this right contract so I'm going to ask our force and theintendents to come up now they're going to ceremonially sign their names here so they can commit to those matters come on up guys in this room you have to write small first the commitment to this course that we have decided to embark on going to the year 2022 and signing it here across the United States from San Antonio the San Antonio is a great, great scenario it's not the case, San Antonio is an ingrained game situation in San Antonio we just learned today is the sixth out of the top 50 rich golf players in San Antonio San Antonio brings six out to the New York City and the Boston's and the Northern California's of the world applied their skills and talent and expertise and come along to innovate, create business and drive as a company so imagine what it's going to look like in a year to age between these investments that we're all connected to making today brings San Antonio to the number one city in all of America in terms of this great game we need to sign up because of Dr. Juan, that's your favorite come to our children over to New York City