Good day. It's a pleasure to be able to speak to you today about managed services and model delivery. My name is Audrey Resnick, and I'm a data scientist with Red Hat. In understanding what the deal is about managed services and model delivery, we'll be looking at the following agenda. We'll be looking at what our managed services, who cares about them, where do I find managed services, and what do you manage services have to do with model delivery. In that instance, we'll actually be taking a look at an AI ML use case, an actual project that one of my colleagues is working on. And then finally, we'll hope to answer the question, are managed services easy to use? So, managed services, if we took a look at it in the generic IT perspective, are the practice of outsourcing the responsibility for maintaining and anticipating the need for a range of processes and functions and in order to improve operations and to cut expenses? And I decided to start with a generic IT service, IT managed services look in order to get you familiar with everyday examples of managed services that you would find in a normal IT organization. And hopefully that would go ahead and wet your appetite to think about what managed services would be good for a data scientist. So let's take a look at these generic IT managed services. First one, I think everybody is very familiar with. We have help desk. We have equipment installation. Now with the equipment installation, that also goes with moving hardware and along with hardware maintenance. Definitely, we look at firewalls that are within an organization and the security that goes along with that. I think everybody's heard about the anti-virus and patch updates. So again, those are services that can be used to make sure that your software is bug free and up to date. Systems monitoring is a very important part of generic IT managed services because we want to see the overall health of the system. Are there things that are causing the system to run more slowly? Could we do things to make the system run better? Disaster recovery. Obviously, we don't want to be caught in a situation where we could lose some of our information on our servers if those were somehow damaged. And hopefully we'll have a rollover somewhere where our important information to our company is saved. And then we have managed backups. Definitely for anybody who's working with a lot of data, we definitely want to do backups in order to save our data resources. And there are many, many, many more managed services that I didn't even list, that I'm sure that you can think of. But that doesn't really matter. I've just given you a general overview or flavor of what managed services there are. Now let's flip to the data science side. Now, today in data science, we have an added complexity. We're just not only on on-prem. We're adding the cloud as part of the platform that we work with, our hybrid cloud environment. Therefore, the security, data repos, the servers, any type of communication and sharing services we use have to be just a little bit more complex. So let's look now at what managed services would make sense in data science and particularly where we're looking to deploy an AI ML model into production. Now as we think about managed services for data scientists, whatever services we create, we have to allow the data scientists to focus on building their models. Because while they're building their solutions, they want to experiment with the latest bells and whistles, so our managed services have to be very flexible in order for them to work with open source products. The data scientists don't want to deal with any upgrades, supported versions, say supported versions of Python's compatibility issues between Python libraries. They want to focus solely on building out their solution. However, that doesn't mean the data scientist is able then to walk up to IT DevOps, hand them a laptop where they've been creating an AI ML model in isolation and say, I need this model to go into production tomorrow. We need that collaboration between IT DevOps and data science. So really when we're looking at managed services for the data scientists, we also need services or methods that IT DevOps can use to help the data scientists easily put the AI ML model into production and then also be able to monitor the performance. So keep that in mind. The first type of managed service or category that we would like to look at is something to do with extracting and transforming the data. So this all kind of starts with data acquisition where we extract and transform the data. We can integrate streaming data from things like OpenShift, Apache streams for Kafka, or reaching out across the hybrid cloud to pull in data for analysis from multiple platforms. And data sources. And we can use services such as Starburst Galaxy to help us curate our data. So the first step, unlocking that value of the data by making it fast and easy to access data across the hybrid cloud. Next we would look at running experiments and creating models. We can provide a Jupyter notebook environment for data scientists to go ahead and model their experiments and we know that the data scientists would like to have access to curated data science packages. Therefore, we do, we could have things such as Anaconda Commercial Edition integrated for those data scientists that are looking to take advantage of AutoML. They can also make use of IBM Watson Studio. And once that data scientist has coded those experiments, if they need access to hardware accelerators to speed up the time to value, they could use something such as GPUs. I have an example here of the NVIDIA logo for NVIDIA GPUs. But really at the end of the day, we just like to be able to run the experiments and build run models using various data science library package and open source products. Let's take a look at the next service. We would like to actually go ahead and deploy models as services. And once you have your models developed, you can use an OpenShift source to image template to deploy and endpoint for testing. You can also, instead of using OpenShift S2Y, you can also access Selden deploy for model serving. And the idea again with deploying the models as services is just really to simplify and accelerate that process of deploying and managing your machine learning models. Let's take a look at the next set of services. Then we have monitor the models and to track performance. With this, in order to monitor that model performance and glean insights from the analytics and monitor things like the drift of a particular model, you can continue to use a service such as Selden deploy or Watson machine learning and Watson open scale for model monitoring and performance tracking. Just to know when you need to retrain and redeploy. When we look at the overall picture, keep in mind that for any IT ops person, this flexibility can be a bit of a nightmare. They really want a reliable, stable and reproducible environment for their customers, for their data scientists. Now that we've defined a set of managed services, let's look at who besides data scientists would actually care about these services. And I think I've alluded to some of those individuals. It's not only data scientists, but also the data engineers, those people that are going to curate or transform the data. And you've already heard me talk about IT ops really do care about these services because the jobs that they do actually these managed services will help them do their jobs quicker. And there's another thing that we have to look at it. It's just not just caring about the managed services, but there are other things besides the managed services that all of these folks care about. The first being kind of the AI ML model operational life cycle. And we sort of went over that when we're talking about the four different types of managed services that we had. The other thing is to actually have a production ready platform. Remember IT ops has to really feel good about those managed services because they really want that reliable, stable and reproducible environment for their data scientists. And then we look at flexibility to use available open source services. Being open source means that it's essentially free to use and being open source also means that you have a large network of users and developers who contribute towards an open source projects, updates, new features and offerings for new users. And this flexibility to use those open source services, this is something that I have seen as myself as a data scientist have done when I'm experimenting with a model. There may be some sort of resource out there in terms of a managed service package that will actually cut down the time that I have to code and will allow me to concentrate more on my model performance in terms of trying to build something to get my model out the door. Lastly, there's the ability to deploy and portability to move from the platform that you've initially developed on. This ability allows you not to be tied to a particular vendor. And I personally feel that to be very innovative, you have to be able to try a wide variety of open source technologies and services. You don't want to be married to one particular vendor. And I know that there is a middle ground where we can make everybody very happy in terms of looking at these managed services. So with that in mind, talking about what we kind of think the four managed service areas are for data scientists, let's go ahead and actually build a platform that fits our managed services requirements. The first thing that we have to look at is really the infrastructure. And I'm going to go with a hybrid cloud because that's typically what I develop in. And that hybrid cloud platform should offer very consistent experience on the on-premises public clouds and even the edge locations. And it has to be, again, efficiently managed by IT operations. In this example, I'll say why don't we build something really on the public cloud and use AWS. So we would actually be building kind of a platform or a managed services on AWS because that's a typical cloud provider that folks use. Next thing, let's look at compute acceleration. The hybrid cloud platform should have integration with hardware accelerators. That means things like GPUs to help speed up any of your ML model development and inferencing tasks. Fortunately for us with AWS, there is GPU availability and that comes in the form of NVIDIA GPUs. Let's take a look at what else we would need. So having a hybrid multi-cloud platform with self-managed services would be ideal. So on top of this infrastructure and having the ability to do this compute acceleration, we should have kind of a little area where we could pick and choose which type of managed services we would want to work with. And these are open source managed services that I've listed here. Things like Starburst Galaxy, as I mentioned before for data creation. IBM Watson and Jupiter where we could go ahead and experiment with building our models and looking at some of the results. And then of course if we actually wanted to go and deploy our models, we could use something as seldom deploy. So we've kind of built our layers. Again we have the infrastructure. We have compute acceleration. So now let's go back to those groupings or headings of our managed services and see what we could pick in each one and see what other details they have in them. So we've talked about gathering and preparing the data. That also takes into account anything like data storage. Where are we going to store our data? Is it going to be an S3? Does it need to be on-prem? Does it have to be on another server somewhere that is in S3? Is it a data lake? We have to do the data exploration of course. Data prep for any of our models. And there also may be time series data or stream processing data that we have to consider. So with gathering and preparing the data I should mention that we could use something like Starburst Galaxy. Now that we have our data what we're going to be looking at is be able to take that data and do some experimentation with it. So in this case we would like to use some sort of machine learning notebooks such as a Jupyter notebook and use some libraries. So we might be interested in using TensorFlow for example or PyTorch or Scikit-learn. And we'd want to actually have those Jupyter notebooks if we're working with them actually have access to those packages. Maybe in the form of a containerized image. And then we could look at deploying our models in an application. That kind of goes with the model life cycle. And of course we could talk about pipelines at this portion. And we can use actually something again I mentioned before that we could use seldom deploy to actually go ahead and deploy models and build pipelines. But the other thing that we could use is if we were in an open shift environment for instance we could use something like source to image to actually build out an endpoint to our model. And have something like a REST API so that we can connect to it. And building the model is really good but the work doesn't stop at that point. When the model is deployed we have to continuously monitor it and manage it. And we really want to make sure that those models are not only making the right predictions but that they slowly aren't drifting over time so that we don't notice that they're making incorrect predictions. And of course at the very end we have to have the ability to retrain the models as needed. We don't want to have to be able to pull everything down and start from scratch. There has to be a graceful way that we can actually retrain those models. So let's take a look at an AI ML use case that one of my colleagues has been working on as kind of a test case or pilot for using this type of managed services platform that we've been slowly building. The project is being undertaken by my colleague Guillaume Montier for Metro London. And what he's doing is he's monitoring traffic movement in particular car registration fees and that's all done through license plate detection. The machine learning model that he's developed is actually going to detect the license plate on a vehicle. And if the vehicle is angled like this one here it actually will write the license plate and then the characters would be gathered through ML. Data can then be stored, read, analyzed. In this case through Kafka and Kafka is an open source software which provides a framework for storing, reading and analyzing streaming data. So in this case they might right away take a look and see if there's an amber alert for that vehicle before they do any further processing so that they can flag that vehicle as soon as possible. Data is then stored. We can store it in an object data warehouse. We may have a vehicle registration database. And then finally after that data is stored customers have the ability then to go ahead and use analytics tools to analyze further for traffic movement congestion parking those types of items. So let's take a real managed services platform and as a data scientist build out Guillaume's project here build out this AI ML detection service. So this is confession time for me. The architecture that we put together for the managed services that a data scientist could use to deploy a model actually exists right now. It's in a product that we call Red Hat OpenShift Data Science. It's managed services offering that sits on top of OpenShift, specifically OpenShift dedicated. And that OpenShift dedicated instance sits on top of Amazon Web Services. So that way as a user you kind of have the best of both worlds. You have a very stable platform where you can go ahead and use managed services. Then you can take advantage of what AWS offers in terms of their storage and also being able to use items such as GPUs for hardware acceleration. So as I mentioned this all comes together in Red Hat OpenShift or Red Hat Data Science managed services. And this is where the user once they log into this platform can discover and basically access a number of open source solutions. So I mentioned beforehand if they wanted to do data exploration, model exploration where they're creating their models they could go ahead and use Anaconda. In my case we'll probably be looking at Jupyter Hub so that I can run that managed service and run some Jupyter notebooks. But for each managed service here whether it's a Red Hat or a partner managed service component, the other thing to remember is that with this platform there are also a number of quick starts or tutorials. So there's kind of at your fingertips help that will help users kind of understand what some of these services are and to get users started working quickly in there with any component. And this is an example that once the users have enabled the components in this case they've enabled Jupyter Hub and what will be available for them are all these quick starts and tutorials and documentation for common tasks. And one thing I'll mention too is once a user goes ahead and chooses a managed service doesn't mean that they cannot go backwards and choose not be able to choose anymore. Absolutely can. That's part of the discovery and exploration when you're creating an AIML model is you want to be able to try a lot of different items, a lot of different bells and whistles to see if that will help you with your model development process. Our data set is already curated so I don't have to use Starburst Galaxy to curate the data set. Therefore we're going to begin by using Jupyter Hub so that we can quickly experiment with the data and again note just because we use Jupyter Hub managed services. It doesn't mean that we can't integrate with other other services and you can use as many managed services as you like realistically other parts of the system will fill in gaps that you may not have built out in your in your workflow. So from this this applications menu I'm going to choose enabled and you'll be able then to see three managed services. So at this point here these are three services that I have within my own user project enabled so I'm able to use them. We're going to go ahead and launch the Jupyter Hub managed service just by clicking this launch icon. And what that allows us to do is to go right into Jupyter a Jupyter Hub notebook where we can actually build up a Jupyter notebook image which means it will be packaging up a Jupyter Hub notebook into a container image that you can deploy to OpenShift or you could deploy it to Azure. You could deploy it to AWS or to Google Cloud. In this specific step here you're going to be able to customize your notebook image type deployment size and environment variables and remember that when we're creating this this image or this container that the container for those who may not be familiar with it. It consists of an entire runtime environment for an application plus all the dependencies so any of the libraries binaries configuration files that are needed. All those items are included and then it's bundled into one package and by containerizing this application platform as dependencies differences in OS distribution underlying infrastructure those are all abstracted away. I'm busy to build an image. We're going to take a look at the available notebook images. We have CUDA, standard data science, Anaconda, PyTorch, a minimum Python. And then what I want to use which is TensorFlow which has according to the specs here Python version 383, TensorFlow 241 and CUDA 11.03. The next thing that I'm going to do is choose a deployment size so that's really going to be the container size for an image. We specify this size depending on how we feel the need is going to be for our machine learning model. We in this case are going to choose a large container size with limits of 4 CPU and about 60 gigabytes of memory. Now we also have the ability as I mentioned before to go ahead and use GPUs so some hardware performance to help us run our models quicker. And in this case I'm not going to use GPUs so I'm just going to select zero. And a little mention about environment variables. If you are working like I do sometimes in AWS and you have data stored in an S3 bucket, you want to use a Jupyter notebook. Instead of going ahead and keying in the access key ID and the secret access key right into your Jupyter notebook where the world could actually see it. There's the ability right here to use environment variables and set them up with those secret passwords so that when you go into your Jupyter notebook, nobody's going to see those items. Okay, so then we have this server go ahead and start up. This can take a few minutes for an image to spin up. You can click on the event log right here so that you can see what's happening in the image deployment process. But because this is just a can demo, we're just going to go to the next step. So now you're inside your Jupyter environment and as you can see it's a web based environment, but everything you do here is in fact happening on the Red Hat OpenShift Data Science Cluster. And that means you can do all of this without having to install anything on your own computer and without disposing a lot of some of the local resources like CPU and RAM. And you can still conduct your data science work in this fairly powerful and stable managed environment. So let's go ahead and populate a Jupyter notebook with Guillaume's current license plate repo. So we're going to have the ability to go ahead and choose Git and clone a repository. We will enter in the repository name where we have some of the work that's been done for this license plate recognition software. So we'll just enter in the name of the repository and then press clone. And the license plate workshops will appear then under the name column and they will show up in their own repository directory. So now we're actually ready to experiment using a Jupyter notebook. And as I mentioned, we're using a Jupyter notebook to basically recognize a license plate and extract the license plate numbers from these car pictures. So we've gone ahead beforehand and installed some libraries that were not part of the container image because that's the other important thing. Or if there are some, in this case, Python libraries or packages that you need that weren't in that base container, you can definitely go ahead and install them and then be able to do more experimentation. And we're going to experiment with our model and at the end make sure that we can detect a license plate number. So we'll go in, experiment with some of the code and hopefully it'll work so that we can actually go ahead and get that license plate number. So when we learned how to create that code that we'll be able to extract the number from a given license plate, but of course you can't really go use a Jupyter notebook like this in a production environment. I have known people that have tried to do that. It's not a good idea. Reach out to me if you want to find out more about that, but yeah, please don't do that. Therefore, instead of using the Jupyter notebook, what we did is we packaged all the code that you saw previously as an API that can be directly queried from another application. And we do this by creating a flask app. But first, some explanations. The code that we wrote in that notebook has been repackaged as a single Python file. So basically we're calling it prediction.py. And basically it's just the code that was in the cells of the notebook that was put together in that single file. And then to use that code as a function you can call. We just added a function called predict that takes a string as an input. And then the name of the picture and then the model machine learning model does the recognition and sends back the result. And we can open that file directly in Jupyter lab. And we could recognize some of our previous code and see what the new function has added. Now there are other files in the folder that you can use to provide functions to launch a web service that can also be used to serve our API. So in this case, we're going to go ahead and launch the server. We'll go ahead and test out our flask app. And we can do this by just using a curl. And then we can wait for the results. So in this case when we did a curl and we got a prediction of S7JDV for the license plate on the car that we were previously looking at. And now that the code, at least from what we can see appears to be working, we're ready to package it as a container and have it run directly into OpenShift as a service that you can call from any other application. Remember, we could have used something like Salden to deploy our application as well. We're just using a source to image just so that we can show you what that looks like. So let's go ahead and build the application inside of OpenShift dedicated. That means we're going to step away from this Red Hat OpenShift platform with the managed services for a second. And then we're going to go into our main OpenShift dedicated platform. And this is not a far jump. It's just one click to get back into OpenShift dedicated.  I know that that's something that makes data scientists fairly irritated when you're in the middle of a model and you want to test something out. As I mentioned, this is the main OpenShift dedicated platform. And within OpenShift, we want to make sure that we have a project or a namespace setup for us to work in. And we do have one, just a user one project, very bland. And then what we'll do is import our license plate code from our get repository. So we had cloned a get repository and as we made changes, we could then push those changes back up to get. So that way we get back into OpenShift dedicated, we can import code from our get repo in order for it to be built and deployed. So there are a number of options that you can use to create a deployment of our application. But most importantly, we want to be able to create a route. You can do health checks as well, build configurations, deployments, scaling resource limits and labels. But the route is something that is important because we're going to use that to be able to access our application. And when we go and hit create, the automated build process will take a few minutes. And then OpenShift will go ahead and deploy the application. In this case, I just went further into the menu and pulled up the information on routes. And we have an HTTP URL here. And that's a route that was created specifically for our application. And that's the main public URL that we're going to use to send images to for processing by our machine learning model. First we want to do though, is to actually do a test status by clicking on that link to make sure that the REST API is awake and alive. So when we have an application listening at that route that was created by deployment, we just test it by just clicking on that route link or just going ahead and doing a copy of that URL and pasting it in the browser. And in this case, the status was okay, so everything seems to be working. And just a note, you can also test your app status through Curl or Invoka Web Request. As our application is now a REST API endpoint, there are multiple ways to upload images to it. We can also run our app right back from our Jupyter notebook. We could basically go ahead and add an image, the card JPEG here, and then copy and paste that URL that we created in open source. That's from our route. And then when we go ahead and execute that cell, we can actually see whether or not that prediction comes out. And in this case, for the car image that we put into our AML model, we are getting the prediction of VU69YDE, which actually matches the car image that we uploaded. All right. So we had that conceptual architecture for managed services and model delivery. You see how those managed services really couple together with the model delivery because they can be helped to use to deploy your model and also remember to retrain it. If we look at what Red Hat has for the typical AML workflow lifecycle, it's going to resemble very closely what we kind of built on our own with managed services and a platform to support that. And again, remember for managed services, we're looking at gathering and preparing data, being able to use something like Starburst Galaxy to help us go ahead and curate that. We could also use OpenShiftStreams for Apache Kafka. We can go ahead and then start developing our model. In managed services, we could use something like Jupyter Hub, which I demoed, and you can create a Jupyter notebook image that can use a specific data science package or library, in this case TensorFlow. We can set the container size for that image to be larger, small, depending on how big our model is or how much resources our model is going to use. We can actually go ahead and then again deploy that model. We could do a source to image, which I demonstrated again. SeldenDeploy also has the ability to deploy your model. And then finally, we want to have some model monitoring and management, and that model management and monitoring can be done actually by SeldenDeploy again. And of course, then we want that ability to retrain those models. So in a nutshell, I hope that you've gone ahead on this journey with me and learned what managed services are. You defined those four and kind of broke them down into smaller ones. Seeing who cares about them, it's not only the data scientists, but it are the people that work with the data scientists, such as the data engineers. It could be some developers and IT DevOps. They have to have a say in those services as well. You can find those managed services through open source. In our case with Red Hat, we created a platform where you can access those managed services to create your actual model and work with the model delivery. And those managed services in our case were through a public cloud on AWS, but you could also find those managed services on a hybrid cloud or on-prem. And really at the end of the day, what those managed services have to do with model delivery when we looked at that use case, we could use stuff like Grafana to again look at the data that we've created. We made a lot of use out of our Jupyter Hub instance, which we created a Jupyter notebook that we used to experiment with our model. And if we wanted to go further, we could have used Selden to deploy everything.  I think they are, especially the ones that are in open source, if you can have them in one area so that you can see what options are available to you. And that's what we hope to do with the Red Hat OpenShift data sciences platform. So I thank you for your time and I think we have enough time for questions, so please feel free to hit us up on the chat here and with your questions and we'll see if we can answer them for you. Thank you.


That's the second time I've done a Whitney Houston blowout like that Gonna get copyrighted. Oh, no Please follow us on Instagram From all juicy God damn Today a Lot of you've been screaming at me on social media It's me too and they said this has been trending screaming and in India and And you as they said I don't watch these shows. I do and I'm really glad for some reason I've watched every season of world of dance except this one Hmm, which is great because I haven't seen this gotcha, but I love world of dance, but it's from the world of dance This is from the Kings and let me tell you world of dance. There's some good dance shows that they're on that get So you think you can dance gets legit dancers world of dance Legit dancers. This is this is the best of the best Well, these are called the kings. I believe they're from India. Yep And so we've got I can't there's nothing I've seen more of in the past couple weeks So thank you for your patience guys. I've been letting you know we're gonna get to it I promise we've been waiting for the right One for us to see we wanted the full performance.  I know that sound That's gonna be a 97 something Give them give them a 98 No, this was their first exposure to that song Yep Yep Things we've never seen before this is what I mean. Yeah, you should sleep so good Laughing I'm just being good. Wow, that's cool. It's just an array of flavors. It's fantastic Yeah, so I want to hear what they have to say about it. They're pretty good yet. Oh Culture integration. I like the dance Yeah, see And it's so you you're guys Teens y'all the complete package y'all the kings No joke But the dancing yourself needing workshops Again amazing with the tricks, but we need more dancing Dancing to music that only you can pull off Fireworks like y'all like y'all to change your name from the kings to the fireworks Exploding I'm sorry. I'm still trying to wrap my head. I'm gonna see the score That's not the full performance if they don't show you the score. Well, they said it's the fourth The full performance, so There should have been a score this whole show is about the score You don't advance unless you get a 95 or higher usually I'm trying to see and if they got a hundred. That's really really rare. Oh, so yeah, we'll find out dang The height on the those rows was nuts. That was fun to see a song We knew oh, I gotta tell you because that's all my my my playlist guys the stuff that I listen to in the car When I'm driving around in me and it's obviously my workout mix But even when I'm just driving around I have a whole playlist of stuff that is just they can't see that Yes, see like a layer. No, see there it is. It doesn't help That song I have listened to that song so many times now I have an emotional when that started. I was like goose bumpy and Like they were playing. I thought it was that other one at first Like it was the one we just watched. Yeah, you thought it was there. Yeah, I thought it was that attack friend I was okay. No, this is not that. Yeah. No, it has a very I mean it still worked the moves This is the celebratory Yeah, which is a great song which they've done and I think you've seen it It's really funny where they have they've imposed Trump's face on it. Have you seen that one? Yes? I've seen it's one really it's really well done. It actually looks like him. It's quite disturbing. Um, don't but don't put his face Put out that yeah Someone's gonna now. Oh my goodness if one of you did that with with one of our faces We'd be so happy, but that's that performance was Exactly what I would expect from a dance troupe from India at high level energy But Ranveer is really proud to see that level of energizer bunny energy Creativity all the labor when you said he needed more energy. Yeah We're joking and I totally wasn't joking like when you said that she was really ugly. Yeah. Yeah So our chasm. No, you guys they should be really proud I think and it seems like all of you in India are really proud of them for repping and yeah Let us know what they got because I really want to know and I want them to win that that was Freaking fantastic. Yeah, that was great. Yeah, I know they have a bunch of other dances that we've missed This is just one of them ready to watch it that for the stuff we've watched as far as group dancing That's my favorite thing we've seen what else I was a song, you know, that's a huge reason for that helps a great deal


When I am screwing my prosthetic foot back onto my socket, how do I make sure that the angle is right? Because that is way more important than you might think it is. If these screws are off just a little bit, I'll start feeling it maybe in my lower back, maybe in my hips. Maybe it'll just feel funny when I'm walking. I'll feel like I'm walking a little duck footed or something like that. So here is how I make sure that that doesn't happen. So when I found the right alignment, something that felt really good to walk on, I just took permanent marker and marked up the four sides of it. So as I'm screwing it back in, I just make sure those lines line back up and as long as they do, I'm good to go.


The Division of Human Services celebrates St. Lucia's latest centenarian in the person of Ms. Julian Marie Menard. Her muddy mark provides some insight into the life of Ms. Julian, 100 years strong. Birthdays often call for celebration as they signal the growth and advancement of an individual, not only in age but in life. On the extraordinary occasion of her 100th birthday, the island's latest centenarian, Julian Marie Menard, received a special visit from the Division of Human Services. The aim was to pay homage to Julian as she achieved a feat that many have been unable to. At a small celebration of her birthday, the Division of Human Services presented Julian with tokens of appreciation. Andrea Alsid is a case worker in the elder care unit of the Division of Human Services. On behalf of the government and people of St. Lucia, we want to wish you a happy 100th birthday. We're very grateful to be celebrating this day with you and we hope that you are happy to reach that milestone. So again, on behalf of the government and people of St. Lucia, we want to say happy birthday to you. Julian Marie Menard, still very much a vivacious spirit, spent most of her life catering to others as a wife, mother and homemaker. She birthed a daughter and a son and is fortunate to see three generations, which currently consists of seven grandchildren, nine great-grandchildren and four great-great-grandchildren. She continues to be the matriarch of the family. To the youth, she encourages them to heed the counsel of their elders. Despite her accomplishment, Julian says she did not expect to become one of the island's centenarians. I am very grateful to be a part of this family for the great-grandchildren and the great-grandchildren of St. Lucia. Everywhere. I wasn't able to tell my mom what I was supposed to do, because I was very tired. I had a nightmare. I had this nightmare. I was already at my Field Day. I was wishful thinking, wishful thinking, I was sleeping, I was sleeping. Julian celebrated a hundredth birthday on 16th June 2021. From the Government Information Service, I'm Homa Di Mac reporting.


What is up, everybody? Jason from jazbeastcasebraze.com. We did it, guys. Thank you. 2023, Bowman's Best Baseball, eight bucks case break PYT number one sold out. All card ship. Remember, if you have a little character signing the music, we want that from the filler. We had to sell 10 teams in a filler, guys. Took a little bit of time, but we did it, guys. We did it. I know it was early, though. So I feel like, you know, not many people are watching at this time. It's talking to the website, but hopefully this inspires some people to start getting it to the next case, guys. Remember, only eight boxes, guys, but packing some heat, though, guys. Four autographs per box. Two in every mini box. So it is a fun rip, that is for sure. All right, I'm gonna rip four boxes at a time, too, guys. So a few minutes here of just ripping. And again, guys, obviously I came in early to try to get some rips going. We got at least one done. That's more than enough.  You know, and you'll see some of these teams that were in the filler, and hopefully maybe that inspires some people to buy them straight up. I'm telling you, guys, there's gonna be a lot of hits for a lot of these teams that were in the $30, $40, $50 range, guys, to get the next one rolling faster. You all have eight hours of job, so it's a good thing. But if you wanna get into a really nice one, five cases are on the other side. Five cases, maybe. I'm gonna help or what? Yeah. You're trying to? Okay. Wow. Special edition of mine go help you. So you can hurry up so you can be done faster so you can go and eat some lunch. Oh, okay. That works. And I'm losing my mind over there. Wand. You guys can see right there. You guys can see his hands. The question is, though, what are you gonna get to help you not lose your mind? What are you gonna get to eat? Chicken. Dang. And we're all done. Oh, yeah. Somebody buy us a little, right? A bolt bought a box.  Okay, see that? It's pretty expensive this year. It's like super expensive, dude. I hope those kabooms definitely go for some really good money.  Booms are not even guaranteed. I know, right? You'd see that are explosive, maybe. They're like a blast. Yeah, even a fashion item, man. Yeah, we have a five-caser on Fanatic's live. It's the same thing here, but basically times five, right? So five cases that we're trying to get done over there. Only nine teams left. Yeah, nine teams left over there. I mean, a lot of the teams are gonna see them in the $200 range, but realistically, they're probably only like $40, $50 teams. It's just, you know, there's five cases involved. So, just gonna hop over on that side if you guys want. And now I've seen some names over there that I've bought in from over here.  So I'm like, hopefully it sells quicker. It won't get to be the problem again. I've seen that one do early when I popped in just to see. He's all like, I don't know, he bought one. And he was just like, I just seen this right now. Oh yeah, he bought one. And I was like, yeah, it's like, yeah, dude, there's so many people ripping it. It's like, it doesn't matter how many times people just kind of stock rooms. These packs rip amazing. Yeah. I love these packs. And now we haven't sold it out yet. That's why. Yeah, it's because of just sitting there. All right, I'm gonna start right with this box here. Michael's gonna rip the rest of them here. So there should be four autographs in every stack here, guys. Looks like maybe one here. No, number? No, Randy De Jesus. First one there. That's gonna go to the angels and Joe. Oh, it's cool. I don't know what that is though, but. Gavin Cross, Jacob Wilson, and then we got a Grego. It's not green. Oh yeah, green right there in the 99. Bobby Miller refractor rookie. Wow, this is one of those first little Randy Johnson edition, Corbin Carroll. This must be out there like in Arizona, right? Yeah, it's like the screen trailer. Yeah, it is.  Yeah. I think I'm gonna end up driving too. I don't want to though. I mean, I gotta drive to my in-laws bro. That's like seven hours. I mean, I can fly, but it's just like, did I have to rent a car? My in-laws have a car, but it's just, well he has a truck, he has like a two-seater truck. I was like, bro, we have to put someone in the back seat. Cam Collar there. Can't even tell what's from what, it's all look nice. Felden Francisco Alvarez Green. That's a Mets team right there that was in the filler. It's a 99. And we got Daniel Guillarte from Milwaukee. All the teams that are fit so far are still available. Really? I know, right? That's what I was telling you to do. Don't shy away from those cheaper teams. Mendros, Corbin Carroll. All looks nice. All right, then you go for a service. Is it gonna be a four, August? One, two, three. Yes, it is a four. All right. I don't really know what to sleeve up guys, so I'm just gonna sleeve up a lot. Some of these may just stay sleeved, some of these may be top loaded as well, but thinking only like the bigger rookies, if anything. Just got such nice carts. All right, next box. Another Grego for the Phillies. Phillies were kept from a filler. Michael Harris, Jackson Chirillo, it's a 299. That's from there from Milwaukee, that's going to Joe. Other masterpieces Michael Harris, Oscar Colas, Yoshida, and Spencer Jones for the Yanks. When it's Tristan, Uri Perez, Luis Lara. That's a good young prospect there too. That's another one there for Joe and the Milwaukee Brewers. That's Jackson, Junior Caminero, with a Yordon Alvarez for the Astros. Joe also part of the filler, they're at a 50. Leonard Henderson, and a Devisan de los Santos for the Diamondbacks. Thank you, sir. All right, bye, guys. Reels are real, Julio Rodriguez. We gave him a Rana Rookie there, and a Jackson Holiday Reel, a Reel. I think another four autographs right there. And I guess they'll go back over there too, Eric. You can't stay away from the camera that long. So him helping packs that was more than enough. Sterling Thompson, Sorter's and Shippers' Sleeve, or finished top loading for us. Call it a Rockies with that one. That's going to Kev. And that is a Max Muncie there for the Oakland A's, Adley Corbin. These look frickin' awesome. And Emmanuel Bonilla. Nice one there, Refractor. That's for the Blue Jays, when Alan Murdock, Davis Martin, Jeremy Benio, another nice gold for the Astros. That was Bradley. And how about Luis Guanipa? That's a nice one right there. Out of 25, too. Gold mean, mini diamond, sorry. Top prospect there for the Braves, Joey. There's Volpe, Jordan Walker, Josh Young. And Eddie Beltray for the Badres Bonita Tristan. Reels are real, Drew Jones, Tyler Freeman, Joey Otani, and then Jose, Jose De Polla. It's a purple. That's right, baby. Gotta get you one, man, that's what I want. All right, next box here, guys. Halfway through after this box, Bryce Harper there, number to 199. And then we got a Daverson Gutierrez for the Metz. Metz Gwendojo, James Altman, and Samuel Zavala at 99 for the Padres. Gwendojo Tristan, Walker, Bobby Wake Jr. Reel to Reel, Corbin Carroll, Edward Julian, Minnesota Twins, Gwendoj Kev. Junior coming arrow for Tampa Bay Rays, Gwendoj Kev, Francisco Alvarez, and a Brock Jones for Tampa Bay. Aaron Judge, purple. Let's keep it going. Drop some base card stack. All right, halfway through, guys. So these are all the autographs we've got them so far. Pretty much has been all prospects for the most part. Like I said, it's mainly prospects and moments best, but you do have rookie autographs, and you do have some cool vets. So hopefully we can get some nice ones here in the second half. Zeke Otovar, nice Reese's Girojo and Giorgio Valera. And Daniel Montecino, San Diego. Chris Bryant, Colin Howick, another one there for the Metz, Gwendojo. And nice, Ezekiel Otovar, real to real. Kev with the Rockies. How much are the Rockies? Kev, are you watching right now? Tell the people how much are the Rockies. I don't think they were that expensive. But again, chances of getting some nice hits. 29.99, guys. What did I tell you? I'm telling you. Don't shy away from those cheap teams. Brett Beatty. And how about a little Ethan Salas? We'll take that.  There's Drew Jones. Jose Ramirez, Giorgio Valera. Like I'm not saying ditch all the big teams and just buy the cheap teams, but just be cautious. Like, hey, man, I know these teams are only 30 bucks, but it doesn't mean that they're terrible. Since the odds are probably a little bit harder to get a hit for them.  Brett, I'm not sure. I mean, that only makes me believe someone must have got a refund because they didn't want to be in the break no more. But today can be a good day to do that today, man. We're so close. CJ Abrams. Mazataki Yoshida. Valdez. For the Cubbies. Cubs gonna Harry. Kayla Killian. Bryce Harper. Oh yeah, but you know what? The good thing is that today's a baseball day, so people might just want to do that today. You know what I mean? It is a fun rip, though. I've ripped a couple of boxes of that as personal. It's awesome stuff. Ooh, is that a red? Red, wow, Ariel Castro. Red autograph for the Minnesota Twins. Kev, having a monster break, Kev. I know you grabbed a lot of teams in here, but look at this. One out of five. Kev's one of their top prospects in this past year's Bowman Draft. There you go. All right, out of five. Yoshida Hunter Brown. Oh, wrong, Kev. Thank you, buddy. Wow, how about a Michael Harris? So 250 autograph, man. What a box this would have been as a personal. Shellacked autograph. Maybe we're starting to heat up, guys. Nice Jackson Holiday Green Minis diamonds. So 99. All right, two more boxes here, guys. Good luck. Ooh, we got a redemption. Someone decided not to sign in this product. Alvarez, Justin Crawford. Gold Joey Lotto for the Reds. Got a red shirt. And then a Johnny Farmello. Another top prospect there. This time for the Mariners. Going to Ryan to 150. And then we got a Tavares to 25. That's where they're for the Braves. Hector Rodriguez for Cincinnati Reds. Kote Senga, Aaron Judge there. All right, guys, never too late to learn, buddy. Never too late to learn. I'll tell you what though, man. Out of all the sports, baseball, besides hockey, but baseball could be one of the most affordable group breaks.  Basketball, super expensive. But baseball, I feel like, you know, with some products could be very, very cost friendly. And like I said, if you do it the right way, you know, if you want to buy to flip, then you know, get some chances of getting some good hits for a lot of teams. But hockey's another one that's really cheap, man. Affordable on the wallet and it's a fun breaks always. All right, best of. Starts with a D. E. Detroit. Max Clark for Detroit. Michael, there you go, man. Top prospect there for the Detroit Tigers. All right, here we go, guys. Last box here, Xavier Isaac, a Roman Anthony for the Boston Red Sox, going to Karen. Corbin Carroll there. Brian Anderson. And a Luke Keisho for the Minnesota Twins to 75. And Minnesota for Kev. Gunnar Anderson for the Orioles EA. That's a blue. James Wood. Jackson Shereel. Colby Thomas to 150 for the A's. A's is going to Joe. Real to real there. Wow, look at this way to end it off. How about a Corbin Carroll? From the Randy Johnson. 21 out of 25. Mini diamonds. Going to the Diamondbacks. Very nice. Well, pretty solid first case, guys. I will have to say. I know we're missing a couple of hits probably for a handful of teams, guys, but obviously still got a lot of hits in here, though. And like I said, some good names, a lot of good prospects, some nice color prospects as well. And then we managed to hit a rookie and ended up being one of the better rookies to chase in 2023. And we hit an out of five, too. Pretty solid, though, right? Corbin Carroll. Harris was also another rookie. Aracosta, a little Zika, told me a couple of rookies, actually. But I'm still chasing some bigger ones, guys. Want to hit like a show? Hey, a trout. All those other guys as well that are in the checklist, guys. So again, I appreciate it. Number two is in the store. It's in a filler now. If you guys want to try it out, jaspyscasebreaks.com, guys. Thank you.


Food has always been a big part of me and my sister's lives. Now, when my mom was 15, her mom actually passed away. So growing up, my mom was never taught how to cook. She literally had to teach herself. And I remember she would snip recipes out of the newspaper and then try her hardest to make them. So today I'm sharing with you some old recipes. These are recipes that we grew up with. My mom loved making casseroles. And so I want to share my special casseroles with you guys. So if you're ready, let's just jump right in. So the first recipe I'm making was a Sunday staple at our house. I think we have this like once a month. So we called it chicken devan growing up and it's one of my favorites. I decided you needed some cooked chicken. So we have six, well actually three big breasts that I cut in half. So we have six chicken cooked in the Instant Pot. We're just gonna pull it out real quick. Now if you need to know how to cook chicken in the Instant Pot, I'll put a link down below for you because it's my favorite to cook chicken in the Instant Pot. It just makes it go by so fast, you don't have to think about it. You're first gonna start with about four cups of broccoli. We cut it up into little pieces. It will shrink as it cooked. Now you need to steam the broccoli. We're gonna take all this broccoli and we're gonna put it into like a steamer basket. If you don't have a steamer basket, that's okay. You can actually put it right into your Instant Pot or microwave it to steam it or however you like to steam broccoli. So we're gonna actually stick this into our Instant Pot with our already hot water in there so it will cook a lot faster. We'll put the lid on and we're just gonna cook it for zero minutes. I know that doesn't make sense, but that's what you wanna steam your broccoli at. So we're gonna go pressure cook. You can go to zero minutes. So pretty much it pressurizes and then it's done. There we go. Okay, while the broccoli is cooking, we're gonna make the sauce that goes on top. So funny story about this, I was an awful cook when I met my husband and so I had my mom explain how to do this recipe for me. So let's just say it didn't turn out the best just because I didn't even know how to boil chicken or anything like that. So Instant Pot has now saved, pretty much saved my marriage. No, I'm kidding. But I'm getting, I am much better at this recipe than I was in college. I added two cans of cream of chicken soup and then we're gonna add about a cup of mayo. So you guys know I love to eyeball. We're just gonna eyeball about a cup or so of this. Then last, we're just gonna add a teaspoon of lemon juice and from the single, if you wanna see. You guys, the bellies coming, it's coming. And you're just gonna mix this all together. Okay, it took about five minutes to pressurize super fast and now we're gonna just let the pressure out and our broccoli will be ready. Okay, broccoli is done. Oh, it looks good. One nice thing I like the silicone things because I can touch it and it's not burning me. Okay, we're gonna go straight to the pan. One, two, three. We're just gonna dump it out here. Now we're gonna spread out our broccoli. This is a little bit bigger than nine by 13 but this casserole actually fits perfectly in a nine by 13. I just didn't have four casserole pans. So you get the big one here.   So we have all shapes and sizes of chicken here which is totally fine.  You want to cover the broccoli. You want it to cover the chicken. You want it to cover as much as possible. All right, so now we're gonna add on the cheese. I love using fresh cheddar cheese here. Now it's the best part of the recipe at least for me. So we have, I actually have two tablespoons of butter melted here and then I have my favorite white bread.  It makes like these amazing like homemade croutons that just make it taste so good. So you're just gonna put those all over the casserole. All right, we're gonna cook this at 350 for about 25, 30 minutes. Everything's cooked. You just need it to be melted and you know, all together. Done and done. Oh, it looks so good. So I've got my taste testers here with me. We have and sweet and maybe nice. Okay, so we're gonna taste it out. So the first one is our chicken to van or chicken and broccoli, right?  Broccoli is not really the best. Okay, fine. Eat the chicken. Have a bite of the chicken then. I'm gonna eat too. All right guys, we're eating, what do you think? I like it.  So I'm gonna give it a five. Five over here. I'm gonna give it a five. This is one of my favorites. Mainly four. Four, four. Four is four. Without the broccoli, that's fine. All right guys, we're done with this one. Let's move on to the next. The next recipe is our five ingredient enchilada casserole. Now this is so simple, you guys are gonna love it. So for your five ingredients, you're gonna have corn tortillas, some enchilada sauce. The old El Paso is my favorite. I like to use fiesta or Mexican cheese blend. You have some black beans here. Then of course, some ground beef. First you're just going to cook the ground beef over medium high heat. We're gonna use one pound of beef here. And if you guys didn't know, I love using a chopster. It's only like six bucks on Amazon, but it's a game changer when you're using ground beef. All right, we got a special helper helping us mix today, right? Right, all right. So first we need the ground beef. All right, so we're gonna pour the ground beef into a bowl. Oh, was it hot? No. No, not bad. Then we're gonna add about half of the enchilada sauce. So I'm gonna give you the spoon, okay? And then can you mix this all together? Yeah. Nice. Yeah, it's close enough, about half. And then about a cup or so of the cheese. Ready? Cheese is coming. You're gonna mix that all together. All right, so we're just gonna spray with some cooking spray. And then Sarah is gonna put her tortillas in. You wanna help? There we go. So the plan is just to cover all of the bottom of the pan. Next to it. And a half one. Good job. All right, and then I'm just gonna take a little bit more and we'll kinda just like piece it together like a puzzle. We just don't want any bottom to be showing. All right, so then we're gonna take half of our meat mixture and just put it right on top of the tortillas. Now, these are the corn tortillas. If you don't like corn, you can use like mini flour tortillas too, but I like corn with these ones. Okay, next you're gonna spoon on about half your can of beans. So Sarah, can you put some beans on? I can help you a little bit. Good job. No, no, I can do it. Oh, you wanna do it? Okay, so you kinda gotta sprinkle them all around, Kate. Oh, only you, okay. Oh, such a good helper. All right, next we're gonna do just about a fourth cup of sauce. So I'm just gonna take a few spoonfuls and just kinda drizzle it over. We like these moist instead of nice. You don't want them dry. We don't want them dry at all. Now put on the tortillas. Can you help me, Sarah? Yeah. Okay. Wait, why do you have to put them over? Well, it's layers. Perfect. Good job. Wanna cover them all up, okay? All right, here's that one. All sides. Yep, all the sides, huh? Yeah. Yeah, put that one in the middle. All right, now it's time to add the rest of the beef. We're just gonna dump it on and spread it out. Just makes it a little easier for me. That might have not been half the first time, but you know what, with this casserole, it doesn't really matter, so. All right, Sarah, time for the beans. You wanna dump them on, we'll spread them out. There we go. Perfect. All right, take this when you can help me spread.  So about a cup or so left or more. You know, if you like cheese, you go for it because cheese is one of my most favorite food groups. Okay, then we're just gonna cover it with foil. We're gonna cook it at 350 for about 30 minutes to see how everything is heated through. All right, it is done. Oh, yes. All right, I added a little sour cream, tomatoes, salsa, whatever you guys wanna add to it. Or if you just want a plain, you can just take a bite right there, okay? Okay, all right, everyone, grab a bite. Okay, Maly, you start out the ratings. What do you think? Okay, I think it's four and a half. Four and a half, okay. Endly? I really like it. I really like a lot of our meals. You do. Yeah. You're my non-picky one, so that's why you taste us a lot. Yeah, I really like it. I'll give it a five. A five, nice. I like it with the sour cream, especially. Me too. I'm gonna give it a four and a half just because it's a little dry. It could use some like avocado, maybe some guacamole on it. It could just improve it a little bit, but other than that, I like it. All right, guys, done with this one. Let's move on to the next. Now, this one is a casserole, but it's not in a nine by 13 pan. It's in a skillet, and it's our creamy chicken and rice. It's one of my favorites. Okay, so we have our skillet. We're gonna go to medium-high heat. There we go. We're gonna just melt one tablespoon of butter to start. I love this recipe because it's kinda like a dump-and-go casserole, which I'm a fan of. Our butter is all melted, so we're just gonna add in one onion, and then we're gonna do half a teaspoon of thyme leaves, and then half a teaspoon of salt. And then I'll just kinda mix this all in. All right, the onions are looking soft, so we're gonna add just about a teaspoon or so of garlic, and then we're gonna add a cup and a half of rice. Now, if you wanna rinse your rice, that's great. I don't rinse my rice. Please don't eat me alive because I do not. It's just how I prefer it. All right, then we're gonna cook this for a minute. Okay, it's been cooking for about a minute or so, so we're gonna add two cups of chicken broth. Now, I know this is four cups, but I'm just gonna eyeball until about half the carton is gone. Okay, then we're gonna add one and three-fourths cup of milk. We want this rice to cook, and this is going to help it. All right, and just mix that around gently, very gently. So as you strain carefully, you're just going to wait for it to come to a boil. You wanna make sure that your stirring has it's coming to a boil because you don't want the rice to stick to the bottom of the pan. Okay, as you can see, this is boiling, ready to go, so we're gonna add three cups of chicken now and pull on the P card. I am pregnant. I'm using rotisserie chicken, because I can. Actually, I'll be honest. If I even, if I wasn't pregnant, I'd probably still use a rotisserie chicken. You need chicken that is cooked and cubed, and rotisserie is the way to go on a busy day. Once you put the chicken in, you just need it to boil one more time. It took about a minute to get to the boil. So now we're gonna turn our heat down to low. We're gonna put our lid on, and we're gonna let it sit here for about 15 to 18 minutes, because we want that rice to cook all the way through. All right, so it's all done cooking. Let's pull this off and see. Ooh, looks good. Okay, I'm just gonna mix this around just a little bit. Nice, the rice is nice and cooked. That's what I like to see. Now we're gonna add the vegetables. So we have about two cups of frozen vegetables. We're gonna add to this. And this one is just like the vegetable medley. So there's corn, there's peas, there's beans. And then we're just gonna add a cup of cheese. We're gonna add a little bit more, and a little bit, but right now we'll just add a cup, and then we'll just mix it all together. Now these vegetables, they will heat up pretty quick because this is hot. So we're going to kind of mix everything in, then we're gonna put a little more cheese on top, and then we'll let it just kind of cook there for a bit. Hey, and we're just gonna make sure it's on low heat. Actually, you can pretty much turn it off if you wanted to. We're gonna just leave this on for about five minutes. All right, cheese is, ooh, ready to go. This looks good. Okay, last one. Maybe this has rice in it, are you gonna be okay?  This is like her cheesy chicken and rice. I have a recipe of this for the Instant Pot, but yeah, this one was made in a skillet, so it's a skillet casserole. I'm gonna grab a bite. You gonna try it? No, and if you noticed, guys, I am now in the second trimester. I'm feeling great, and food is not gross anymore. Paulina will win. Okay, I don't really like it, but I think it's just a little bit dry. Yeah. Yeah, so I'll give it a four. Okay, I'm gonna give it a four too. I feel like it needs a little more seasoning or flavor, maybe add a little bit more salt in there, but still good, but I'll give it a four. All right, guys, now you have your dinner ideas. If you want some amazing dessert ideas, you can find them right over there. I think I could taste this brownie. All right, guys, we'll see you later. Bye.


Labour unions yesterday began a five-day warning strike in Kaduna State. NLC national president, Comrade Ayubah Waba, said at the flag-off of the strike that it will run its full course unless the state government attends to the workers' grievances. He said the national leadership of the NLC had been informed that the grievances included the sacking of 7,000 workers from local government in the state. He said the Congress was aware that in the primary healthcare development agency 1,700 workers were sacked in the face of exorbitant increment in tuition fees, high cost of living, and other actions in ministries and agencies in the states that are uncalled for. We are here to resist a government that does not respect the rule of law. We are here to respect a government that does not respect the dignity of living. You are aware of the provision of Section 20 of the Labour Act. It made very clear provision that before any employer of Labour, be it in the public sector, be it in the public sector, the clear redundancy, they must first consult the union and the workers' representative. In fact, the law says sure. And the first item, they are in the name of the disease teacher in the list of people who have passed competency tests. And they must accept we are carrying out a post-competency test. We don't tell the issue to him, not only verbal, it was a writing. We never knew that it was a neoliberal policy. What happened is that he has seen workers, particularly primary school teachers, that are occupying higher grades and their salary is a bit higher than the lower grade. So he compared the needs of all those teachers and now said they have failed their competencies. These are teachers that are experienced. These are teachers that have grown from the rank and file to arrive at where we are. Most of the teachers were goers. And then he replaced them with newly employed teachers that were on lower grade level, mostly on level 7, 8 and 9. And he put them on a fixed salary. As I speak to you after two years, they are still temporary students. Is that a decent work? Is that what the law says? In 2017, it sucked 5,000 workers, local government workers. Thenly, as of last week, it sucked 2,441. When you had it up, it sucked 7,441 local government workers without any remuneration, without their severance package, either at their gratuity or any amount of money paid to them. Many of them have died. I've just raised an example of the former president of Norge. The man was sad. He became depressed. He died. And his family announced of thought to a number of local government workers in Kaduna Sea that Air Rufae has, because of these injustices, has led to their death. The other law says, before you disengage the worker, there's one we call the London Sea Law. You shall engage the labor union representing that worker. Air Rufae acted in breach of this Air Lo Convention, in breach of Trade Union Amendment Act of 1906. He has acted against fundamental human rights, which is the shatter on the Air Lo and United Nations. And he has acted in deviance of the favour he received from God Almighty. We are here to also address our retinas concerning the tuition fee increment in the entire Kaduna States institutions. 100% increment. This is an exploitation in which we are not going to accept it. Initially, the money we are paying, 24,000 Naira for non-Indigenous, 36,000 Naira for non-Indigenous. Some of our students are unable to pay that particular money. And now you have increased the money to 170,000 to 300,000. We don't want that increment. We want you to do total reversal of that increment. We don't want to even tell you to reduce it for us. But rather, we want total reversal because we are thinking of the future generation that are coming. Thank you.


That's three in a row for the Tennessee Titans with Amy Wells. I'm Mike Keith. This is getting familiar. General Manager John Robinson is here. Congratulations on the win in Indianapolis. Thanks so much. Great work by the ball club. Outstanding work. And great to finish up there. Yeah, it's always to get a, you know, a good win on the road, especially against a division opponent. Now I've got to ask that fourth quarter blocked field goal. How in the world did that happen? Well, I mean, I think the guys are really starting to believe in that play. You know, we say it all the time. You never know what play is going to impact the outcome of the game. It happened for us in Kansas City, you know, and Sunday against Indy Simmons and Austin Johnson and Dayquan. They did a really good job of kind of caving that side of the line down. Colu got up the field, created a gap for Dane to jump through and Pismith did his job. You know, he was waiting in the wings there to catch it and go. You mentioned a lot of names there, but one of the great things that we're seeing from this ball club is how many names are contributing in this three game win streak. Is that almost the most exciting part of what's happening with the Titans right now? Well, I think our guys are starting to really believe in complementary football, you know, in all three phases of the game, you never know which play is going to make a difference, whether it's Austin Johnson blocking a field goal or in the game, whether it's Harold Landry coming inside on a rush, which forced that interception that Byrd made or the 40-yard dagger that Ryan threw to Khalith there to really end the game. You know, the unnoticed thing was Fertcher holding off the outside linebacker just long enough so the rush couldn't get there, it sealed the game for us. So it takes every single one on every single Sunday. Now, we've got to talk about Derek Henry. He became the fifth player in NFL history to have at least 145 rushing yards and a touchdown in three consecutive games. Why is this run game working so well? Well, it starts at the line of scrimmage. It's about getting on your guy, getting movement and finding a crease. You know, it starts with making sure the play is going to work, coming up there and checking, making sure that we're going to have enough blockers accounted for, moving guys off the ball, and then for Derek or Deion to get downhill and hit the crease on the front side or find that backside seam and make yards. And our guys are really buying into that concept. Headed to Oakland this weekend, you're going to see another former Alabama running back, rookie Josh Jacobs. Why is he having such a nice year? Well, it's just like what we talked about. He came from a program where he played with a lot of NFL players and against a lot of NFL players in that SEC league. They've got a massive offensive line. They try to move the line of scrimmage and create some space for him. He's got great quickness. He's got great vision and burst. He's a really tough guy to tackle. Let's talk about the Raiders defense. What are they doing to get to the quarterback? Coach Gunther, he's done a nice job. You know, he came over from Cincinnati. He's an aggressive play caller. They've got a couple guys on the ends of line of scrimmage that are rookies in Farrell and Crosby who are long and they're hard chargers. They really get after you and they're really physical in the back end. You know, they got speeded off the ball linebacker and whether it's Harris or Joyner or Worley in the back end, they really make the receivers work for every yard. How do the Titans go to Oakland and get a fourth straight win, John? Well, I think it starts with a line of scrimmage. You know, we've got to win a line of scrimmage on both sides of the ball. We've got to come off the ball and move their line. We've got to stand up to their big guys on defense. And it's about making third downs, offensively keeping our offense on the field, getting into that red zone, scoring touchdowns, and defensively getting the ball back, getting off the field on third down and finding those special teams plays that have really swung the pendulum in our favor. John, you took some time away from Oakland Preparations on Monday to join us for the Tennessee Titans Mr. Football Awards at Nissan Stadium. Ten young men playing high school football in the state of Tennessee named Mr. Football. Why do you think it's so valuable for the Titans to be involved promoting high school football in events like Mr. Football? Well, I mean, it's the lifeblood of our sport. You know, those players, those coaches, every player on our team or that has been on this roster dating back to the spring, the foundation for their passion and their success was laid in high school by a coach or a teammate. You know, being from this state is extremely important to me. I'm extremely proud that Amy, her family, and really everybody in our organization has bought into that and really supported the growth of high school football in the state.


When you're trying to find your messaging, you're not going to just make it up in your head in your bedroom and get it right the first time. You have to actually push things to the market and see what people respond to. So it's a continuous act of getting messages out to the market, seeing which ones stick and then just double, triple, quadruple down on that messaging that actually works. So that applies to any business, but you want to think about what is the end result that my customers specifically are really after and go specific here and then just make that your brand name.