Okay, but as George mentioned, I'm there. I'm from Kyrgyzstan, but I live in Vienna nowadays. So if you're ever in Vienna, you can get in touch with me Okay, so what are we doing in this talk? I'm gonna demo a project and I'm gonna demo some tools and Hopefully we will learn something together at the end. So we're imagining that we have Online shop where a one-person data team in an online shop It's not the biggest shop in the world, but it's a shop that has hundreds of customers maybe thousands of customers all around the world we have to ship orders every day and We get a request from our bosses saying that we need to make predictions on the order So we need to be able to know if an order will be returned within one week This is something they want to do for I know storage or something I don't know but they want to know if the order will be returned within one week and For now, it's okay to do it in batches. So at the end of the day, we look at all the orders for today and we do a prediction and But ideally they and we will get bonus points for this is they want it to be live so they want some kind of an API endpoint that they can call and it will give you a prediction right away and Yeah, so that you know even before the user clicks order They know that this order is likely to be returned or not Yeah, so because we are in an automated shop We need to build a pipeline that trains ML models and uses ML models And ideally it's an automated pipeline So we don't want to by hand sit and train it every every day Ideally we have an automated system that's training and then choosing models and then deploying them and using them And everything is automated. So all we have to do is press a button and monitor from time to time What which technology are we going to be using to to build this MVP of this feature? We're going to be using dbt for transforming and cleaning the data. We're going to be using dbt for training ML models dbt is going to be used to make predictions and dbt is going to be used for automated deployment So could you raise your hand if you know what dbt is? Okay, awesome. And could you raise your hand if you work with dbt at the moment? Awesome. Okay, so for you, it's going to be a little bit boring next five minutes But I hope I hope I clarified if I do something wrong, please tell me that you know what I'm saying is wrong Okay, so in a traditional setup with a data Infrastructure we have ETL pipelines. We extract Transform and load into our data warehouse, but nowadays Compute and storage are separated. So storage is super cheap So we can just dump everything into our data warehouse and this means that You know, we can just load all the raw data from all of our data pipelines straight into our data warehouse And then the challenge is we have to go from raw data to transform data in order to be able to consume it in Downstream when we do like machine learning do reports to dashboards. And so this is where dbt comes in dbt is a tool that allows you to relatively easily define these transformations and productionize them and Run them in a more more or less reliable manner Okay, so how does it work? So what what is actually dbt dbt comes with a project? It's a CLI tool so you run dbt run and It needs a project which is a centralized structure of your transformations Within the project you'll have dbt models models are like tables or views inside a database You'll have tests to make sure that the models being generated properly And it can be done automatically and then you have a DAG because models can depend on other models So you'll have like a like a DAG of models, you know going like I want to run model a to be and we'll run it What is a dbt model dbt model has to be a transformation. So when it's a sequel model, it has to be Like a select statement. Yes. So here for example, we have a simple sequel model where we're doing some selection And you notice that dbt supports Jinja. So here we have from ref model C This is this is the dependency. So it shows that model D depends on model C So before you compute model D you have to compute model C Okay, and dbt also supports Python models and Python models like sequel models They have to define transformations and in order to define transmission in dbt. It has to be a function So you have to have a model function. It's definitely just called model and here inside a model function You have to return a data frame. So that data frame is that is then going to be persisted as a table in your data warehouse Okay, so and here you see that this model has a dbt object that references model B. So model C depends on model B and Yeah, so not all the dbt so dbt by itself is not a database, right? It's just a transformation tool so you need to connect it to the database or data warehouse and for Python models Not all the dbt database is supported so snowflake supports it data bricks supports it they both supported natively Big query supports it kind of you have to set up a data proc cluster to run it And if you know how to do it, that's awesome. If you don't yeah, well, it's an adventure That dbt supports it, but you have to do it on your, you know local machine And then there's this thing called dbt file dbt file is now our open source package It's a that that we build and what dbt file allows you to do is allows you to do Python models on almost all Adapters of dbt. So but what we do is like when you just install dbt file It allows you to do the transformations locally on your computer So not on the data warehouse, but on the computer So what happens is that dbt file downloads the data from the data warehouse does a transformation and then uploads it automatically to the data warehouse Okay, so this is how we define a profile siaml This is a credentials file in the dbt project and here you see we have a staging db These are the credentials to our staging database It's a postgres database that I have on my computer and then there's another staging a credential definition of type file And this one is we're basically telling dbt that we're gonna use file with Postgres together so file will do the Python transformations and postgres Is gonna do the sequel transformations All right, so we have I have a test project. This is this is the project that I was talking about We have a shop. We need to do some predictions on the orders and we need to be able to You know persist those predictions in our data warehouse This is the you know, I get what the github repo looks like So we have some requirements in order to set it up This is dbt postgres and dbt file and Jupiter notebook because you know, we need to be training and predicting things Okay, so this is our models directory. So inside a dbt project. You'll have to the code This code. Oh qr code. Sorry. Sorry. Sorry It has to do animations now There we go, okay, I Think there's one more person Okay, awesome Okay, so we have a models directory so this is where we actually define the transformations and as you can see we have four models here and Don't worry about the bottom two. These are what we are actually building here But the top two are the models that already in our data warehouse So this is some something that either we or somebody else already made and we have customer orders model and customer orders labeled model So as you might guess customer is labeled is a table that has customer orders Labeled with whether or not they were returned within seven days and then customer orders doesn't have that label Okay, so now dbt as I said comes with a CLI So all I have to do to actually run a calculation on these sequel models is I do dbt run I tell with profiles deal where where my credentials are and I with select statement I'm telling which models to do the calculation on and so now dbt You know talks to Postgres. It's a it's a Python package But it talks to Postgres and it runs the models in the order that you know, it knows from the references Okay, so Now we go into the Jupyter notebook. This is the same project I just opened the Jupyter notebook in this project and here. Yeah, I'm opening a notebook and here we have This from file import dbt file. So this is again, it comes from us This comes from the dbt file package. This is an unofficial dbt Python SDK Yeah, so you can create this fall dbt object with and tell it where the credentials and where the project directory is And once you've done this this means that now this fall dbt object is now aware of your entire dbt project Context yeah, so what this means now is that you can actually download Your dbt tables as data frames into your Python runtime. So here for example, I'm getting a customer orders labeled Table and you see I have a data frame and I have a return Column is one or zero and it's telling us whether or not the order was returned within seven days It's just a data frame I can you know look at the statistics of the data frame just to see you know what the distribution is how many different things they are this is a Completely generated data, so you know Might show some AI bias so don't don't judge but so this is the data that we have we have total price and we have age and the red dots are the dots that were the orders that were returned and The blue dots are the orders that were not returned and as you can see there's some correlation between price and age Where when when a when a customer is younger and the price is higher the orders tend to be returned more often So this is a very simple logistic regression that you can train that that would be able to you know give you the prediction and So this we can do it straight in the notebook of course because we're in a Python runtime and so here I'm just getting a simple logistic regression and I am Incentivating it and I'm training a model with it and then I'm looking at the classification report So seeing how good that model was yeah, and it's just in these lines You can train a simple ML model and we have an accuracy here. You say accuracy line. It says point eight four This is eighty four percent accuracy for this model for the given data So at this point I would go to my boss and be like hey is eighty four percent good enough and they would be like It's better than sixty and you can go ahead and keep them to me MVP Yes, this is the kind of thing that you can improve over time I think for me what's important most of the time is to get MVP out so that we have something to improve Okay, so now that we have that now let's put it in actual dbt so that it can run automatically with a dbt run Right, so I have a model here and in the model. I'm calling this function called train ML model So this is now the function that I defined in my notebook I just have to port it into a Python file, which is a dbt model Yeah, so this is a train ML model function and you see here It takes a features data frame and the labels data frame and that's exactly the same thing that we were doing in a Notebook except now the model that we trained with scikit-blorn We get some metadata off of it and we store that metadata in our dbt table So that it's persisted in our data warehouse. We can come back to it whenever we want We also store model weights, so we tell it the location where to store model weights We create a pickle file and we store it, you know here ML models home directory We just put it there and our model weights are now persisted So now we have a mapping between, you know, our model metadata and our model weights Okay, so we can just run this I think Yeah, first we tell it where to store the models and then we run our dbt model and that runs and it does a quick, you know model Fitting and it's done. So this means that now in the ML models directory I have a pickle file d48 This is now the model that I just trained and so now that I have a model I have to make a prediction and then store the prediction in a data warehouse First I do some experimentation I go back to my notebook and here I now open the table customer orders that does not have a label So this is a this is a raw data that is not labeled We don't know if the orders are going to be returned or not And so now my objective is to make a prediction with our model. So here this is now Sorry, this is now a dbt Table that you see it has a model named d48 So this is the model that I just trained and I just put in the in the pickle I mean I trained it in April because I prepared this presentation April, but it's okay Yeah, and so now I write a little Python, you know, a couple of lines just to say, okay picked a model with highest accuracy You know Loaded from the local storage and then run a prediction by using that loaded model And here we do a prediction and then this orders new df sample will be the sample of our data frame that now Contains our predictions and now we see that we have a predicted return column that has you know zeros or ones And we can go ahead and try to do a plot of it So it's plotted and it looks about right So the color dots are approximately in the right place where they're supposed to be So this we could be happy enough to now put it in the in a dbt model So that again it can run automatically and this is it This is the model definition as you can see we have two functions here the model This is a dbt specific function that tells the dbt to run the transformation and all it does it call It calls make prediction function and in the make prediction function. We're doing the same thing. We did in the in the notebook. We're just Finding the best model opening the pickled model definition and then Model weights and then running the prediction on the dbt table. And so now we run dbt on this so it's running the Python it did the fitting and it saved Save the result so now I have a new table called order return predictions And so now this is a dbt table. That's now that's now in our data warehouse And now we have our predicted returns there Okay Now this means that I can now run Oh Okay, so now there's a problem in that the dbt Fall runs Python models on our local computer, right? And I cannot deploy my local computer to you know run the dbt models for the company So I have to put them somewhere and one of the places you so you can put it anywhere You can put it on github action. You can put it in docker Like digital ocean or whatever but we have file serverless offering. This is Project that that we have it's a product that we have and what it allows you to do is It lets you import this decorator call isolate it and when you decorate a function with this isolated decorator It now runs on the serverless platform that is you know file serverless So this is our platform you can set it the requirements You can set it the machine type and what it does is that when it runs the function It cold starts a machine it cold starts a process it installs the requirements if it needs to install them And then it runs it and then it kills the machine. So I'm doing the same thing here for this other Model and now this means that When when these models are calculated now They can be run on a GPU machine like you can control the machine size So you can run it on a GPU machine or you can run it on an extra large machine depending on how much data and how much You know training you have to do Yeah, so now with those defined I now have to tell where to stall the store the weights because serverless now stores weights in a serverless directory This is a persistent directory. And so now I can just do dbt run and this is the training So now like I said, it takes a bit more time because it needs to start the machine. It needs to run the thing Here it did it and then it needs to turn off the machine Now we do so that was training now we do the predictions And we're doing the same thing So again, it takes a little bit of time because depending on the requirements if requires are different It will be a different process that will start and now that's done and we can be happy So this means that now in order to test we can test everything end-to-end So if I remove all the select statements now it runs the sequel tables and Python tables all together in the correct Dag, and so we have the end-to-end solution. We did the training and we did the prediction Okay, so now it's time now I have an end-to-end pipeline it's time to deploy it somewhere so that it runs regularly without me having to press a button and For this my favorite tool is actually github actions and in github actions First of course, I need to set the credentials right so it's not going to be on the Postgres Database that I'm running locally So I have we have a redshift cluster From AWS and this is where we store our production data. So we have dbt file and dbt redshift adapters and In our credentials we have to make sure that we have production credentials as well that now have the credentials from redshift and With that set we can actually define a github action We just have to create this dot github workflows directory and there you can define a YAML file and the YAML file I'm just saying on which conditions this YAML file should be executed And so here we have pull requests or like a cron schedule or workflow dispatch is actually pressing a button and then what happens when the This work this job starts is that now github is starting a machine and running the process and what github does here Is that it starts a Machine and it basically runs dbt and you see at the bottom there We have dbt run we tell it where the profile is and then we tell run with target production so that it writes to our Production database. Okay, so now once this runs we're done. We have a batch level ML pipeline that a trains ML models be selects the best model out of the train models and then And then does predictions on the best model and but as I mentioned our bosses actually want Something extra they want live predictions, right? And this is something that you can actually do With with file serverless as well, but first how would you even do a live prediction? So in order to do a live prediction you have to get one row, right? You have to get a single row with a single you know the column values and We create a fake data frame out of it just so that we can give it to the To the model and then the model does a prediction and then we return whether or not, you know the prediction will be Positive or negative and here we test this particular turn function We give it different values and it predicts either true or false. This is now our ML model doing those predictions And so we have this web endpoints directory here So what it has is a this is simple Python file where we basically have the same function So we have some environment variables where we tell Which model to use and where to find the model and then the function itself does the same thing It just opens a pickle It takes age and total price as arguments and it opens a pickled model Waits and then does a prediction and then returns this Dictionary saying prediction and this is my value zero or one okay, so our file serverless Package comes with a CLI tool So first I tell it where to find the models and which model to use and now as you can see we have this Command called file serverless function serve We tell it which file to serve and which function to serve So we'll return is a function name and I give it an alias so that I remember What this function actually does and when I call this it gives me a URL This is a URL of that function. That's now an app. That's a cold startable app Yeah, so this means that I can do a curl to it and when I do a curl what it does It starts the machine it will You know install the requirements if it needs to install the requirements and then it will just Return the result so here. Yeah, you have to set up credentials and I'm passing a simple JSON that has age and total price as as keys and values and Yeah, so now I started running it and it returns result prediction zero So I can try it again now now it should be faster because the machine is already started So maybe it's still you know not off yet. So it's prediction zero So I change the value and now it's prediction one So now we have a web endpoint that we can call that loads the model that we told it to and now it can return us their results Predictions life now we can give it to a front-end engineer and they can just call this endpoint and and get Get the information they need All right, so in summary we have a dbt pipeline that trains models We have a dbt pipeline that predicts batch data We have an automated workflow that runs the whole batch process we can serve our model models by API and Everything is in a single dbt project. This is a single repository Now you don't have to like run around in different places figuring out, you know, what's doing what everything is in one place All right, so I'm from I'm there. I'm from features and labels We're a company focusing right now on serverless Python and I'm very happy to be here This is our team if you have any questions about file serverless or about dbt or about anything else I don't hesitate to find me and thank you for it Thank you my dad for your talk. It was great If you have any questions so you can use the microphones We have almost five minutes Sorry quick and unrelated question right here. I'm here. Oh, hi The mouse cursor was moving while you were talking what black magic was that is there someone else controlling while you were Yeah, I have I have a slave Okay, no, no, no, it's it's a recording That's why it said it was in March. I mean in in June Thank you for talk just wanted to ask do you do I need to always return only pandas data frame Or can I use something else instead of pandas? That's a good question. So for our for dbt file, it has to be a pandas data frame right now Yeah, but it depends like why are you asking about the different? Yeah, so so I can perform transformations. I don't know using bars or something like that Yeah, I mean the the model has to return a data frame in the end. So yeah, that's the requirement right now Yeah, so it can so it can be like pores that data or spark data frame. I think so. Okay. Yeah, thank you Thanks very much for the talk one question. Do you support the entire machine? I'm learning model life cycle such as Monitoring data drift more model shift or also how do you do you have like a model registry? We do we do yes We have we have a model registry and we have models that that can detect that but we also provide your platform To make it easy for you to to deploy them, right? so if you have if you have a model in mind that you want to use and It's pickleable and it's loadable onto the on tower service platform. It will run without an issue and For example in the github actions pipeline you showed like is there any way to ensure that if a model doesn't reach a Certain accuracy that it won't run in production. So it gets cancelled for example Yeah, so this is this is something that dbt can handle on its own So for example, if you are if a model if a parent model fails, no children are going to be executed Is that is that the question? Yeah, kind of all of the Training stage does not reach like a certain threshold. You want to like cancel the entire Yeah, yeah, so you can you can fail you can fail the training model and the children models will will not work in that case I don't say no you can find the model outside to ask more questions if you want or in discord. Thank you again. Great. Thank you


This month I've been reflecting on how thankful I am to be part of such a special university. I just want to let you know how grateful I am for my friends, my family, and my amazing work team around me that supports me to be the best I can be for you guys. Happy holidays! I'm very grateful for my very supportive family and friends as well as all the faculty and staff members at SCU. I'm most thankful for my family and friends including my extended SCU family. After 15 years I've been through a lot with so many of you and my heart is really filled with gratitude. Very very grateful for the support system I have locally with all my family. Very lucky to have you so close to school and extremely grateful to the SCU staff. Thank you to our students, faculty, and staff and the many alumni and friends of SCU will offer their time and support to help SCU go beyond as an outstanding institution.


The office that I work in is about six people, and last summer every single person was riding their bike except for me. There was one day they were all chatting and putting their bike helmets on at the end of the day on a beautiful, you know, summer day. And I had the real clear thought in my head, I wish I was sporting like those people. Like I wish I was active like those people. And then I thought, I could do that. I could just get a bike and ride. It took me a while to work up to riding the whole distance to work because I was so scared. I thought I couldn't.  I didn't think that that was me. That class helped a lot just in getting me out on the road, first and foremost. I was petrified. And I didn't think I could do the distance. And he was like, no, it's not going to be that long. And then in the end, I think it was 11 miles we did. And I was shocked. You know, it didn't feel that arduous, but they tricked me. So I think that helped me a lot to realize that what in my mind I perceived as a huge distance that I would never be able to do, I could do it. First time I did it to all the way to the office, I felt like I'd conquered something, you know. And then often I would drive, do errands or drive at lunch too. And now that I don't have my car, I usually take a walk at lunch. So just doing that, the cycling to work and walking, you know, not even every day, but a few days a week at lunch, I've lost about 22 pounds. I think as I've gotten older, I've also realized how I age is up to me. So I really want to keep moving, you know, even if I never win a race or do anything else outstanding, I just want to keep moving. And riding the bike has helped that. I wouldn't have done it if it was just me sitting at my desk. I wouldn't have had that brainwave. So having a boss who rides all the time and having CSU, my employer encouraged bike riding got me on the bike. I wouldn't have done it without that.


What's up everybody, Jason here from jasperyscasebreaks.com. 2021, Gold Rush Premier players dual case break just sold out. This is a football edition, random teams number three. Again, 12 total boxes. You have six boxes per case, two super premium hits per pack or per box. And again, total of 12 boxes, total of 24 hits, all spots in, all 32 NFL teams in. Any one premium card per pack, two packs per box, six boxes per case. And again, some of the potential chasers you can get out of there. Now I personally haven't seen any of these chased yet out of the first couple that I've seen, but we have pulled some pretty nice stuff. So they definitely got to be hiding in one of these cases, right? So here's the dice roller, here's the customer names again. So started from Carl all the way down to Richard. Then you have Arizona Carlos down to the Washington football team. Let's roll the dice. Got ourselves a five out of four, nine times. Good luck. One, two, three, four, five, six, seven, eight, nine. Christie down to Ryan. Seven to four, nine times. One, two, three, four, five, six, seven, eight, nine. Five, six, seven, eight, nine. Time's Green Bay Packers down to the Cincinnati Bengals. All right, so Christie of the Packers, Steve Wright with the Chargers, Carl with the Titans, Dave with the Saints, Steve Wright with the Colts. Excuse me. Patrick the Carolina Panthers, Dave with the Bears, James with the Bills, Thomas with the Washington football team, Harrison of the Rams, Richard with my Eagles, Adam with the Ravens, Blaine with the Chiefs, Brock with the Falcons, Gary with the Bucks, Josh with the 49ers and Raiders, Peter Lombardo with the Steelers, Patrick Evans with the Vikings, Mark with the Jets, Patrick with the Seahawks, Jason with the Browns, Carl with the Patriots, Matthew with the Lions, Nick with the Texans, Richard with the Jaguars, Dave with the Browns, Paul with the Dolphins, as well as the Giants, Harrison with the Cowboys, James with the Cardinals, and Ryan with the Bengals. All right, well, if anybody wants to trade, I'll give you guys a quick minute or so. And if no trades, then we'll close the trade window. I don't want to get this break started, guys. Mark is saying Jets for pretty much anyone. Jets might have some Zach Wilson in here, but Mark is looking for the Broncos it looks like, so date if you're watching. Another 30 seconds, guys.  Call them the Boston Dodgers. All right, guys, I think I've given it enough time. Carl, you want to trade the time for Jets? If not, I'm closing it, man. What's up? I think we need to go to every break. I thought I was special. You get that whole special. I only have nine bars in a 10 hit draft. Should I just grab one? There should have been 10. There should have been a box. The option box. All right, sorry, guys. I had to help out Teddy really quick. Whoa. Okay, Jets for Texans, cool. Sorry, guys, this is an unusually long trade window, but I was helping out Teddy, so I'll allow this one. So we have Jets for Texans. There couldn't be Mark, technically, yeah. So we had a one trade, guys. Mark and Nick traded Jets and Houston Texans. So that's who we have right here. Drew Lock, that is a rookie Drew Lock pink ice, number to 10 rookie there. And again, still on the Denver Broncos, so that's gonna go to the Denver Broncos. And Dave. And for the Chicago Bears, Brian Urlacher to 10 signatures. Chicago Bears going to Dave. In-person autographs, that's pretty sick. Miami Dolphins edition though as well. Beckett autograph 10, that is a rookie as well. Miami Dolphins, that's going to Paul Norton. Carson Wentz, sensational signatures from my Eagles. That is eight out of 10. Very nice looking card right there. Not an Eagle no more, but still love the guy. Fly Eagles Fly, that's going to Richard Kim, last spot mojo. Wow, what is this? NFL Shield, Nelson Aguilore. That's pretty cool, another one for the Eagles. Very nice, everybody likes to talk about his second to last year or last year with the Eagles when he'd drop balls and just didn't play well. But man, that Super Bowl year 2017, he was one of our best receivers and performer in the Super Bowl as well. He had like eight catches. Very nice, NFL Shield. And that is from 2015 Donner's Signature Series. Very nice, another one for the Eagles. Phillip Rivers for the Colts edition. Steve Wright, look at that, two out of four. He's been a pretty decent case so far. Keenan Allen Silhouettes, rookie Silhouettes, two-color patch on-card at $299 for the Chargers. Steve Wright, there you go, another one. Supplemental PSA-8 1990 score, Emmett Smith for the Cowboys, it's going to Harrison. Two more boxes guys, then we have another case. We have a little playoff stamp, Absolute Ink, Troy Aigman. Number 23 out of 30, another one for the Cowboys. Going to do a Harrison. We have Darius Leonard for the Colts, pristine 10 to that of the 18 Donner's. Another one for the Indianapolis Colts. Going to Steve Wright. We have a one-of-one flawless collegiate rookie patch autograph of Devin Singletary. Nice little bowl patch right there as well. And is Devin Singletary still a Buffalo Bill? Yep, he is. Left one of the Buffalo Bills and James. Last one here and then we've got another case. Jason Witton, 37 out of 55 for the Cowboys, what a break, 95-9 autograph. This is from 2013, museum collection, Proble edition as well. Alrighty guys, that was a pretty fun case. Some pretty decent names there. Still hoping to see one of those big chaser, big chaser cards. Whoa, what the hell happened to my camera? I don't know what you told me my camera wasn't here guys. Oh guys, sorry, I didn't even realize my camera wasn't there. I might have to finish this break and then fix it. One second, I remember Nick try to show me how to like fix this. Oh well, alright I'm just gonna have to finish this without you guys seeing my face and I'll fix this and then I have to save the settings because I realized that we didn't save the settings from yesterday so that's why I didn't detect my face here. Sorry about that guys. Well, not that there's a specific chaser card, it's the cell sheet. Like I haven't seen one of those. This is what, our third break, meaning our sixth case and we still haven't seen any one of those bigger cards that they show on the preview page. So I mean, I feel like we eventually have to hit one of those, the amount of cases we're going through, right? I would love to know how many cases they actually made of this, you know, so that way we can kind of figure out what are the odds but I still have yet to see one of those bigger players like the Mahomes rookie, the Aaron Rodgers, the Josh Allen rookie, Justin Herbert, any one of those. Terrell Davis, three out of 15, Majestic. Yeah, well if they have Brady, I don't have no idea if they have Brady. I would assume if they had a big Brady, they would show it on the premier players list. I mean, that's just my thinking going on. I mean, I guess he still could be here. Maybe like a nice numbered card. TD Machines, Nick Chubb, Pristine 10. Let's go into the Denver Broncos. Oh, sorry, not Denver Broncos, but how am I doing? I meant to say Cleveland Browns. Yeah, not for real, man. I'm trying to get you something, man. Obviously, I mean, hopefully this case has one of those two teams, but there's all, we've spoken into existence, Carl. There was a Tom Brady in here, and it's numbered to 75. I didn't expect an autograph with Tom Brady to be here, but a numbered card would suffice. 64 to 75, dual threats from elites. Hey, I'll take that, anything Tom Brady numbered. So yeah, these ones can be a little rough though, because there's still only 24 hits. Whoa, look at that. Rookie Shield, one-of-one duel with Mike Evans and Austin Safari and Jenkins. Uno of Uno. Tampa Bay Bucks, Garrett and Fontenot. Look at that, that is pretty sweet. Danny Dines, Daniel Jones, 12 out of 50. There you go. Giants, Paul Norton. Best in Herbert, Mosaic, PSA 10. Chargers, going to Steve Wright. Reed, two-color patch, on-card autograph to 25 for the Baltimore Ravens. Very nice Baltimore Ravens, Adam Levy. And a Baker Mayfield to 3.99. Father's Day pack, that's a rookie as well. Cleveland Browns. All right, two more packs, guys. Or two more boxes, four packs. Damien Harris, wow, NFL Shield, one-of-one of Damien Harrison. There you go, Carl. You're not going to be over three anymore. You're going to have a batting average of 3.33. One-of-one, we have Marcus Allen, 1994 Pro Line. Autograph PSA DNA. That's going to be a Chiefs edition out of 500. Kansas City Chiefs, that is going to blame. Last box here, guys, good luck. Member number four is in the store, on the website, jossbyscasebreaks.com. We can get another two case break rolling today, if you guys want. We have CD Land, Cran Royal, patch autographed to 15. Collegiate, going to the Dallas Cowboys. Yo, Dallas Cowboys CD Land. And last pack, Peyton Manning, the sheriff from 2008 Topps, 95. Indianapolis Coles, that is going to Steve Wright. And there you go, guys, this was break number three. Appreciate it, guys, jossbyscasebreaks.com.


we are ready to begin the second segment of our morning program if I could ask you all to get comfortable and in approximately 10 seconds I would like to hand over to our organizer Vange Padilla for the next segment. Vange over to you. Thank you. Have you checked the photo booth outside? It's kind of really cool and then they said that they're going to like add some lighting so that our picture will look gorgeous. Anyway we are on the second leg of our morning session and for the next speakers or presenters for today may I call on An Chen and Weenie Chen contributing women's voices and let's hear about the wiki women Taiwan journey but before I give the microphone over to them I was reminded that if you are going to have questions later for our presenters can you please please face the camera over there so that our live audience will be able to see and listen to you so if possible kindly face the camera so and I'm remand reminding that there are good-looking guys over there so you can just say hi and wave and also for the speakers for the rest of the speakers you can also glance once in a while to our camera people over there okay thank you and welcome good morning everyone our panel now will be in Mandarin so please can you all make sure to log on to event yay and connect with your headsets for interpretation we will facilitate just a two-minute break for everyone to log into event yay and if you need help there's me and maybe one of the volunteer for anyone who's struggling anyone anyone need help with logging into event yay okay thank you so much we will use a Mandarin to share our experience but please feel free because we were our slide will be in English today we are going to talk about the eight years of social development and history of the WQ woman Taiwan and the three main parts of our sharing the first part is the foundation of WQ woman Taiwan and some of the activities that we have done in the past the second part is the development of the community and our gathering process and this part can also be provided to some of the social participation in the development and then the third part we will introduce our volunteer that first of all is to introduce to you we come from the country of Taiwan that according to the report of the economic student magazine here is the most dangerous place in the world but you are at the same time we have a lot of delicious food such as pearl milk tea pork rice or small red bun and so we are here is a dangerous and bitter place that in March 2015 is the first gathering of the community of the WQ woman Taiwan and the WQ woman together with the first meeting of the community that WQ women's room is our Chinese name and English is a room of WQ woman then why is our community called this name? In fact, the main thing is to use the word WQ women's room is that if a woman wants to write, she must have money and her own room so we will take the name of the community of the WQ women's room that this is our gathering type probably divided into two one is the arrangement of each month that another is the arrangement of the artistic and women's group in the arrangement of each month we are the partner of the women's group that activity time is about three hours that the theme of the group is not limited that artistic and women's group arrangement we open to all the partners of the WQ women's group that activity time is about an entire day 8 hours of activity that we will work with some Taiwanese local partners like the WQ woman she is a media that focuses on the WQ women's group that WQ women's group is the WQ woman long-term in promoting the WQ women's group then this we will sort out our activity theme and partner that is about four types the first type of activity is the project of the WQ women's group that focuses on the WQ women's group second type is the offering of the WQ women's group the third type is the WQ women's group that supports international activities and then the fourth type is the contents of the content that is the activity of the work between us like TCA and TWA that we share with the WQ women's group that is also a WQ women's group that is also a WQ women's group He is also a victim of a white horror family. On the day of the event, we invited the teacher to present his artworks and his creative ideas. This is what happened in Taiwan in 2018. The most special thing is that we received a message from a international LGBT partner. He said he would like to help us put the information on WeT News. On the right is the content on WeT News. These are the results we took on the day of the event. The most special thing about the event was that Taiwan was promoting gay and legalization. The people who supported the event went up the street. That year, we participated in the creation of a historical high. We are very happy that in 2019, Taiwan became the first country in Asia to legalize gay and legalization. We are happy that Taiwan became the first country in Asia to legalize gay and legalization in 2019. Next is the collaboration between WeT and Taiwan. We will transfer the information on the women's victim of gay and legalization to the WeT data. In the past, when we talked about white horror, the women's victim is relatively easy to be protected. So we think this event is very meaningful. Next is the collaboration between WeT and Taiwan. We invited a doctor to introduce some women's common diseases, such as painkillers and so on. In the later half of the meeting, we will edit the information on WeT.  We will go to the police station together. We will take some photos of the police. This is very special. There is a partner in the community who has been promoting gay and legalization in Taiwan for a long time.  As you can see on the screen, this is our event banner. All of them are from Taiwan. After the call, we will use English to say hello to everyone. This is the title of the video that we edited today. We will write the title of the video of Taiwan's popular snack bar. Next is the collaboration between WeT and Taiwan. Taiwan started from 2016. We have held it almost every year. In the middle of 2022, because of COVID-19, we have been holding it for a year. In 2021, we have tried the first online female family editor. This is the photo of the event. We used Galatang to make our event site. This is a picture of our love when we took a big photo. Next, I would like to share with you the history of gay and legalization. Since 2015, we have been working in the community for more than 8 years. In the past two years, we have been working in the community for a long time. After that, we have been working in the community for a long time. In the community, we have done some things that are quite important. For example, the name of the community, the difference in the logo, and the confirmation of the gay model. We also think that one of the most important things is how to create a space for women to feel comfortable and safe. So, in the second meeting, we invited everyone to share their thoughts on how this space should look like. This is some of the results of the meeting. In the end, we have taken some of the elements of the community to make some adjustments in the space. In addition to this meeting, we have also tried to do a lot of very interesting or different theme activities. For example, we went to the night market together to take pictures and edit the scenes. We also edited the scenes of the military and military forces in Taiwan together. Through the continuous editing process, we also strengthened the editing ability of the community. In terms of independence, there is a very important element. The members of the community are already partners. They are equipped with the ability to quickly solve the problem of the criminal scene. Another thing we think is very important is the continuous editing process. This is not a very easy thing to do. Because the crisis is a self-employed activity. Everyone has to take care of their own work, life, and family. So we have a few simple things to do here. If you want to continue to do the editing process, you can consider the factors. For example, the selection of time period, whether you can organize the meeting, and the suitable real-time space and the current software. Next, I would like to share with you how we do the activity. In the first part, we will put our introduction page on our community page. Next, we will have a dashboard and reference data. The reference data contains some tool content, including the information about your microchip account, and some of the and if there is a theme at the time, there will be reference data. Before we start the activity, we will send a reminder before the meeting. The process we will do on that day is that we will invite everyone to introduce themselves first, and then you will want to edit the screen. Then we will invite everyone to start editing after the dashboard is connected. When editing to a period, we will invite everyone to share their results, and take a photo together. Finally, we invite everyone to participate in our community. Finally, we will organize today's activity, and then put it in our community, and then invite everyone to participate. Finally, I want to share with everyone that this is our volunteer. This is Xiaorang. Xiaorang is a dormitory. She joined our activity when writing the dormitory script. Although she has been here for about a year, she has already edited a lot of female content. This is Gao. She is a long-term partner of our microchip room, and she is also an action trainer. This job is mainly to help people to rebuild their mobility skills. She uses her professional microchip to edit the microchip-100-metre script to help us understand the culture of blind people, and also edit a lot of Thai language. This is Shiyu. She is our DCAC co-worker. She is a independent exhibitionist. Because of her relationship with us, she knows a lot of artists, she knows the contribution of artists, and she has a lot of information to help us edit. This is Jun. She is a former partner of our microchip-100-metre room. She is now taking care of her family, so she is currently the core of this mission. But she also told us that she hopes that when this mission is over, she will come back to participate in our activity. This is Bezzavilla. She is a partner from Italy. She has been living in Taiwan for three years during the pandemic, so she has the opportunity to participate in our activity. She is a senior director of the microchip-100-metre script and I asked her why she wants to edit Thai language because she said that there was no female president in Italy at the time, so she chose this script. We would like to tell you that we have a lot of self-employed partners who come from different industries. We would like to invite these self-employed partners to share what they want to express. The first thing is that we welcome all the female members of our community to participate in our activity. The second thing is that


Well, hello, sir. It's an honor. Hey, Sarge, did you hear? Oh, I guess you already got the news. What news? Master Chief is coming to Fortnite. What now? It's only one of the most popular video games in the world, but more obviously, it's where we are right now. Huh? Wait a minute. If you had no idea that we're in Fortnite, then why do you have a cardboard cut out of Master Chief? Perfectly regular reasons. Whoa. You guys knew around here? No! The Chief got eliminated by a sneaky blue! That's no blue. That's... that's ninja. It's such an honor. I'm a huge fan of yours on Twitch. Twitch? Is he twitching because he's blue?  We're Sarge and Griff from the popular web series Red vs. Blue. You probably watched it about ten years ago. What are you doing here? Well, it's more than just the Chief coming to Fortnite. This map is too. It'll be in creative mode starting at 10 a.m. Eastern time tomorrow. Oh! So that's what we're doing in Ragnarok! You mean Basin? I'm pretty sure it was called something else. Either way, you can start reliving your favorite Halo memories here in Fortnite and maybe add your own personal touches to the map. Hey, wait. Does that mean we can have dance battles? Come on, ninja. What do you say? Eh, save it for New Year's.


Hey everyone, today we're making kombucha marinated chicken kebabs with some zucchini bell peppers and pineapple. Now if you've never used kombucha before as a marinade, you're in for a little surprise because it really works well as a marinade. It's very, it's acidic so that means that it will really tenderize your meat, whatever it is. You know, you can use beef or chicken or whatever and it really imparts some nice flavor. Now we're going to be adding some other things to our marinade sauce but it really works well. I'm Rockin' Robin and I'm going to show you how to make it right after this. So let's talk about our ingredients. Now I'm going to be using chicken thighs for this because I just like them better. They're more flavorful, they don't dry out and the difference is that with the chicken breast though it's much easier to cut into nice little chunks, right?  We need to plan ahead with this recipe because we need to marinate our chicken or beef, whatever you happen to be using, for at least one to two hours or overnight. That would be the best. But today we're going to do one to two hours and that really works well. Now we said we're marinating this with kombucha. I just went to the store, I read the labels on them, made sure there were no natural flavors and things in there that shouldn't be and I picked out this one. They have a lot of flavors guys so, you know, pick a flavor that you think sounds good that's going to go with your dish. So I picked out ginger lemon for today because I'm doing the pineapple, it just seemed to be the right choice for me. All right, so we're going to start with our kombucha. Do yourself a favor, do not shake this up or you will not be happy because it has a lot of pressure. In fact, look at that, it's already trying to come up so be cautious when you open up your kombucha. All right, so we're going to take, uh-oh, look at that. Here we go, right on film. Going overboard. All right, we're going to take a cup of it out and boy, this stuff is alive. Look at that. All right, we're going to just toss that right in there and we're going to add some olive oil and I'm going to put about a third of a cup in there. I'm going to add a little bit of ground cumin, coconut, aminos, have you seen this before? Have you used this before? It's sort of like a soy substitute and you could also use soy sauce if you want or tamari sauce which is a gluten-free version of soy sauce so any of those will work and I'm going to add about a quarter of a cup, toss that in. I've got some fresh garlic here, I love fresh garlic in my marinades, just put some of that in there. Now when we're using the kombucha, the idea here is not that we're thinking we're adding probiotics to our dish because we're not because when you cook this and it's subjected to heat it's going to kill all that. Now we're just using it for the flavor and the tenderizing ability of it so I just want to make sure everybody understood that. Now we're going to add a little bit of maple syrup to sort of counteract all of the acidity and then if you want to add some heat to it I like to add just a touch of sriracha sauce which really kind of just adds a nice little touch to it and I'm going to add a nice pinch of salt to that and I'm just going to squeeze in like a little bit of lime here so I'm just going to squeeze some of that fresh lime juice in too. I like the fresh flavor of that. I'm going to take some of the marinade and put it in with the chicken and I'm going to save some of it to use on the vegetables in it so I don't want to cross-contaminate, that's why I'm just doing this separate. I just want to make sure that this is covered really well because remember we're going to go ahead and marinate this for at least an hour before we make our little kabobs. I'll cover it with some saran wrap and we'll pop it right in the fridge. I'm going to prep up the veggies for this now so I've got my zucchini here and you just want to cut it into chunks basically that's going to fit on your skewer and by the way skewers, I have skewers here, there they are, and what you want to do with these, these are wooden skewers, you want to soak them. I just placed mine right here in a ziplock bag because that was easier than trying to find a dish that was big enough to fit them in and soak them.  Okay, no I'm just kidding, but yeah you do want to soak them. I'm just going to cut these into chunks, now you can leave them this big, you can cut them in half, you can do whatever you darn well please. You don't want to make them too small because they cook up fairly quickly. Okay and then I'm going to take these, I'm just going to toss them into my marinade. For the bell pepper pretty much the same thing, you're just going to cut these off, get rid of the seeds and just cut this into pieces that will fit on the skewer.  So while we're waiting for our chicken to marinate in the fridge in that beautiful kombucha marinade, now would be a good time to take a moment to hit that subscribe and like button and that way you can catch all my new videos every week. So we've marinated our chicken for about an hour and a half and now we're ready to assemble our kabobs. So I've got my sticks, I took them out of the water, now I'm going to show you how to, we're going to line them up and put them on the skewers.  So that's what I'll probably do here with some of these because they're kind of big. Alright, so you just do pretty much whatever your dog on want, okay. Now you want to leave some part down here, you know, open so that you can hold it, turn it, cook it, whatever. Now with the chicken, here we go, let's just take a nice little chunk and you're just going to slide it on and hopefully it'll behave itself and stay on there.  So my left hand is for vegetables and my right hand is for chicken. I'm just a fanatic about that kind of thing.  And you can stack chicken chunks, you know, if they're too small. Next comes some zucchini, bell pepper, and then we can finish with another pineapple. Perfect, right? So we're just going to continue with that until I get these all lined up and then we'll start cooking. So here's my setup. I have a couple of grill pans here. This one happens to be cast iron, oh boy, that's pretty hot. And then I have this other one here that's more of a non-stick. You can also do this outside on your barbecue if you want to.  So basically we'll just start laying these on and you can hear them sizzle.  This marinade is really tasty. It also has a little maple syrup in it as you know and it's probably going to make it stick a little, but that's okay. I like the caramelization. And right now, like I said, it's making a lot of noise but I only have this on low and we just have to make sure that chicken gets done. All right, so I'm going to check these here. These guys are getting pretty charred. This section here is a little hot so I'm going to rotate these over. Looking good. They're not so much. These guys are getting there. Just rotate them around until that chicken gets done. We don't want it too hot so that it, you know, doesn't have a chance to cook to the inside. So cook these until the chicken is no longer pink inside in about 165 degrees Fahrenheit. So keep your temperature up a good medium high. That way you'll get a nice charring on your vegetables. And again, use an instant read thermometer and check your temperature of your chicken. Don't these kebabs look amazing? Look at this. Nice and charred up. Let's try one. Okay. Perfectly cooked. Love that. Man, that pineapple is sweet. Mm. Fantastic. I really like this marinade. So delicious. Everything is cooked just right. It's amazing. The vegetables aren't mushy. They're nice. They got a nice little crispness to them. And I'll put a chicken. Love that marinade. I think it would be better to go longer than an hour and a half for marinating though. You'll get more flavor into the meat. So definitely do at least, I would say four hours minimum. Overnight going to be dynamite. That's delicious guys. Hope you try it. Thanks for watching everybody. I hope you enjoyed this. If you did, leave me a comment down below. Let me know what you think. And if you'd like to subscribe to my channel, go ahead and click that subscribe button. Hit the like button and share the video. Thanks for watching and we'll see you next time.


On Wednesday, the Fed's Barken said the U.S. economy will need sustained support. We found out that the U.S. factory orders climbed 6.4% in July in a third straight increase. Spain's unemployment has risen amid COVID outbreaks and tourist restrictions, and that the U.K. house prices surged to an all-time high in August. Welcome to the Tick-Mill Update. I'm Kiana Daniel, the founder of the Investdiva movement. Make sure to subscribe to the Tick-Mill YouTube channel and support us by liking and sharing this video with your forex trading friends. On Thursday, we'll be eyeing Canada's trade balance for July. Today, I'm looking at the dollar-cap pair, which appears to have found support at 130. This level has acted as a support multiple times since October 2018. If we see a rise in the next few days, we could expect the pair to bottom out here and start a new uptrend. On the other hand, a break below 130 could open doors for further drops towards 126. Do you think the pair is ready to bottom out? Head over to the comment section and let me know. Of course, trading in the financial markets involves the risk of loss, and you should only trade the money that you can afford to lose. If you liked this video, give it a thumbs up, subscribe to the Tick-Mill YouTube channel, and I'll get back to you with more updates tomorrow.