Hey guys, what is up? Welcome to my channel. So for today's video, I am going to be talking about makeup products that I thought I would use more. So basically, I bought them with full intentions of using them a lot and loving them and what they brought to the table and I just don't really use them. I think specifically within the last year, it has definitely kind of gotten out of hand for me just because I've fully invested into my YouTube channel and providing you guys with content and my opinions on new makeup releases. So this year especially, my makeup just hasn't gotten as much use as it used to. So there were so many products I could have pulled but I tried to pick the products that I was most excited about at the time of purchase and then like nothing. What's also really really exciting about this video is that it is in a collaboration with Kelsey Breonna J here on YouTube. I literally like I've been obsessed with her videos since the day I found them. She is so funny.  She's so talented. She has so much knowledge about makeup to give and it really is just so enjoyable to watch her. So I wouldn't recommend her to you guys but I didn't think you would like her. If you don't already know who she is, please subscribe to her. She is awesome. I'll link down below her version of this video. Please go check it out. She's awesome. I love her and I don't know getting to talk to her on the phone and discuss the collaboration and all of this was like really surreal for me. I just can't believe I'm getting this involved with YouTube and it's so awesome to meet these people that I watch online. I just can't believe it. Yeah, I will gush more about her at the end of the video. So let's just get into it. So the first thing I'm just going to start off with a BAM. Not too many people ask me about this just because it's really not my niche but I did pick up the Morphe and James Charles collaboration palette. I just felt as though it was a collector's item. I thought I was going to do a ton of looks with it. It's a very inspiring and great palette to have in my collection. As you can see, there's so much color. I bought this the week that it released. It was in my Ulta. I didn't like search it out and wait online on the website to order it. But when I did see in stores, I was like, all right, I'm going to pick it up before it sells out. This came out, I believe. Definitely around like Thanksgiving time and guess what? I'm honestly ashamed to say this but I have not used it once. I've swatched a few shades and I really, really do need to use this but this has just really been put on my back burner because you guys don't seem to express too much interest in it and there's so many other things that I want to try that I think you guys will like. And I mean, I'm not a big Morphe diehard person but I do enjoy picking up their stuff because they market so well. But I, you know what? I'm creating a look with this palette. I need to. It's ridiculous that I've had this for months and haven't used it yet. I guess I'm just going to continue with eyeshadow palettes. This one also is another Morphe palette. It's not because I don't love it. I just have so many other palettes but this is the Jaclyn Hill palette. Obviously this came out a couple of years ago and I mean, this was before I even had a channel and I was hounding this palette down. I have used it numerous times but especially in the last year I really haven't picked it up that often and people still use it, talk about it, love it. And I was obsessed with this palette. I thought I was going to use it every single day. It was going to be my go-to palette and it's a good palette. It's really beautiful. I just have so many other palettes that I had spent so much more money on. This just wasn't one of my top drawer products. But it is gorgeous. I don't have anything bad to say about it. I just thought I would use it way more than I ended up. I mean, that is very easy for me to say because my eyeshadow collection is ridiculously large. That's my favorite thing to collect. It's just my thing whether or not I use it. I was just reminded that I probably should bring that guy out. You know what would be a good idea? This honestly would be a good palette to bring on vacation. I mean, it's kind of large but I'm going on long vacations this summer so maybe I'll bring that, give it some love. Okay, so the next one, this is kind of not particularly this product but the concept in general. The Too Faced Holiday Kits. I'm just so obsessed with the Holiday Kits. This was from two years ago. This year I did not fall victim. But I mean, the palette that they came out with this year looks exactly the same. This actually I don't think is very bad quality. It's pretty good. It's not the best quality in the world but like I said, it's good. It's usable and this palette has really pretty colors and really good looks that you can get out of it. But I just don't feel inspired by this palette for one. It was just more about possession. I think when I was going to get this palette because there are a lot of different colors but I just don't feel very inspired by it and it's the same with all of the other Too Faced Holiday releases. So I finally have been strong enough to say no. Well for one year I've been strong enough to say no. For a while I was collecting these every year and they just never really got the use that they should have gotten. So I finally have stopped but they're just so pretty to look at. The whole concept is cute and I always confess myself I'm going to use these a ton. They're gray and I just forgot. So last eyeshadow palette I was obsessed with this palette. I had to fight to get it. I feel like it's so gorgeous. I thought I was going to use it all the time. This is the Blood Sugar Palette by Jeffree Star. I don't even think I've ever mentioned this on my channel. Maybe I have but I really don't think so. I thought I was going to love it. You can get neutral looks. You can get purple looks and you guys know purple looks are my favorite and I just thought I was going to reach for this a ton and I just don't. I don't regret buying it. There's nothing wrong with it but I just feel like I have so many other more palettes especially now like purple palettes that really speak to me so I just don't grab for this as often as I thought I would. Really no rhyme or reason. I was just so incredibly excited to purchase this palette and I just never really ended up using it often. I did use it a few times. Definitely way more than I've used the James Charles palette. But this I don't know. I feel like it's definitely a collector's item. I was so tempted to get his new blue eyeshadow palette that came out and I finally said to myself nope, don't do it. It's a waste of your money. If I bought it, it would just be a collector's item to look at and touch but I hardly would ever use it. So I decided to allocate my money to something that I would at least like to review and use for you guys and that I would use more than once. So I said no to that but it's hurting my heart a little bit. Over the past few years, I've always been taken by Nars Eyeshadows. They have since I believe repackaged. I'm not sure about reformulating but I loved buying their single eyeshadows. These have been collected over the past few years. It's not a ton but I'm always so enamored by their eyeshadows with the lights hitting them and Sephora you know. So I picked up these like dual intensity. I have three of them and they're so gorgeous and I thought I would reach for them a ton. I mean these ones in particular, these dual intensity are years old. They're probably far past their expiration date but I can't get rid of them. I can't find it myself to get rid of them. I paid so much for them and they're so pretty. Every time I think about getting rid of them I swatch them. I'm like nope keep but I never use these. I also this I picked up actually within the past year. This Nars eyeshadow in Lunar. I've used it once since I've purchased it. I'm missing myself. I would use this one a lot because it's purple and I love purple. And then this one I think is my newest purchase. I purchased it within the last six months. Right before they repackaged this is Kauai. Haven't used this once. I don't know. There's something about the Nars single shadows that I'm always so tempted to purchase and just based on the fact that they're single shadows. I don't reach for them. I will just grab a shadow that's already in the palette that I'm using. Nars in particular, they really just catch my eye and I always convince myself I'm going to use these all the time and then I never do. It's just inconvenient. They're packed away in a litter drawer. I never think to use them. I really need to look into starting that like my top makeup drawer for the week or for the month kind of video because I think that's a good way to force me to use this stuff because it's not that they're not good. I just don't think about it. Also, these Inglot shadows, I walked into an Inglot store one day and it was my first time ever being in Inglot. I had heard of Inglot, especially from the UK makeup artist. You know that I follow on Instagram and I feel like Inglot's kind of dying but it used to be quite popular and I loved the store. I had a lot of fun. They had a lot of really neat products and in particular what really stood out to me in the store were these pure pigments, just these loose pigments. They are so beautiful. Here are the three that I purchased. I mean, they're so reflective and so shiny. I tend to be attracted to really glittery single shadows in any form, cream, liquid, powder. If they're glittery and shimmery and duochrome and reflective, I'll buy them and I thought I was going to use these a ton and I think I've used one of them once. And again, there's nothing wrong with them. They're so gorgeous. I don't know why I just haven't reached for them. A lot of times I'm testing things for my YouTube channel. These really haven't been relevant to my YouTube channel.  These swatches right now, I want to put them all over my eyes right now so I'm going to use these tomorrow. Also, colored eyeliners. I never, ever, ever use. I try to get into them. I do like a good colored eyeliner in my waterline but things like liquid and gel liners that are not black, I'm not going to use. So I bought this. I don't even think they'll sell us anymore but this is Bobby Brown. This is the Longwear Gel Eyeliner in Hunter Ink. It is a dark green.  I love green eyes and I think this will really flatter my brown eyes and it's just something different from black.  It sounded like a good idea at the time when I bought this but I think I've maybe worn this once. It's just not... I will only ever use black or brown eyeliner on my upper lash line. Unless I'm doing a crazy look for YouTube or something but other than that, especially in everyday life you will never catch me reaching for a colored eyeliner. I also have this NYX White Liquid Liner. First of all, this is difficult to use. This is an even good quality, in my opinion. Just colored liners in general that are in a liquid or gel form I will... I just never use them. As long as we're talking just like a line on the upper lash line I will never, ever use these. I'm not artistic enough to get really fancy with my eyeliner either. I know there's a lot of amazing artists here on YouTube and on Instagram that are always using colored eyeliner and doing cool pictures. Like I'm just... I'm a... I'm a beauty artist. I do like beauty simple makeup that matches your features. So this is kind of the last eye product that we have here. This is the Wonder Brow. There was a time that this was really trending. It is just like a gel eyebrow product that's supposed to stay on forever. That's kind of its niche. I thought I would use this all the time because I mean it really does stay on like forever. It is amazing for longevity. But it's kind of a pain in the butt to apply. I use it with just my regular eyebrow brush. But it really... I have to put it on my hand. I have to put it all over my brush and kind of wipe it off as not to get too much product.  I'm really into either eye pencils or eye powders. Not even pomade so much anymore. So this is just a lot harder to use than a pomade. You have a lot less control.  It does a good job. Once it's on, it's on and it's good. And I thought I was going to use it all the time. I tend to use this a little bit more in the summer because you know you're hot and sweaty. This is going to withstand the summer. So this is a really good product. And I do try to reach for it every now and then.  We're going to start off with blush. So we have the NARS Illuminating Loose Powder. I was hounding it down for a long time. It's one of those items where I would always go into Sephora and swatch and just go, oh look how pretty. And then one day I finally decided to shut myself up and purchase it and I never use it. I would have thought that this being on my mind so much I would have used it more. But honestly for my skin tone, I should have known better.  It's too deep to be a highlighter. Too light and shiny to be a blush. So it's just like I don't even know what to do with this. I think on medium to deep skin tones this is fabulous. I do have the advantage that I can put this in my makeup kit when I do some money with a medium to deep skin tone. So I am lucky in that regard. But I thought I was going to love this for myself. I do love the whole orgasm shade. I do love that gold shift. But this is just like awkward on my face. This is an awkward color for me. Like I said I swatched all the time. I loved on it. I stocked it and I never use it. Oh my gosh. When this came out I was obsessed. Especially with the first one. I had to have to have them. I was like this idea is revolutionary. I'm going to use this all the time. I love color. Holographic highlighters. This is kind of a general statement. I did pick up Moon Child. This was kind of the first of its time. There was like crazy holographic colors. I remember thinking this is gorgeous. I love colored makeup. I can't wait to rock a purple highlighter. And also I did end up picking up Aurora as well as if I didn't learn my lesson the first time. I didn't pick up their other one that they came out with. I had learned my lesson by then. And I mean I still keep this in my collection because I actually will say I do use these during Halloween. But that's a one night a year kind of thing. And every now and then I'll do like a crazy look and I'll pick up a purple highlighter just for peace of mind that these are getting a little bit of love. But it's not like I don't use these. But I just remember saying to my mom of course I'm gonna wear a purple highlighter.  It's just not practical, okay? I'm sorry. I hate to tell my old self that but it's not practical. The two Fenty Beauty the Kilowatt highlighters I do have a couple highlighters from Fenty that I do like. But okay first of all how many carrots? It's gorgeous. I love it. I do need to use it more. I don't know. It's just something that I never think to use. This putting all over your decollete or sometimes I literally will just take it and pat it on my eye. I don't use this enough. I don't have an explanation as to why I don't use it. I definitely thought I would use it more. I am a glitter girl. So no explanation. I just need to remind myself that I have this. So I'm holding myself accountable. Okay this one trophy life. I bought this knowing it wasn't going to be good for my skin tone. But I didn't care. I just wanted to have it because this is one from the first launch of Fenty. This is gorgeous. I mean I need more highlighters of this quality in a better color because this really only looks good on really deep skin tones as a highlighter. I have used it a few times. I really have. I've used it as an all over at lid color and it is gorgeous, glittery, gold. It is such a good product. I just convinced myself to buy this giant pan that I can only use for eyeshadow because of the hype and I thought I was going to use it a ton because everybody, everybody was trying to make it work for them. But let's be honest at the end of the day this could never work as a highlighter for me. It does a shrub as an eyeshadow color. Absolutely great. Great. But you know it's a lot of money for this giant pan of glitter eyeshadow and if you're only using on your eyelids you better be using this everyday if you want to put a dent into it. So I knew, I guess I knew I wasn't going to use it all the time but I convinced myself that I would use it all the time. So I hope we're ready to move on to the last product. This was kind of a gimmicky product that I fell victim to. This is the Siate Glitter Flip. Mine is called Hollywood. This is from the first release. And so essentially what it is, it's a liquid lipstick. You put it on your lips, purse your lips together and then glitter magically appears. I mean you can get a bigger gimmick than that but I was like this is amazing and I love glitter. I'm going to use this all the time. They have since come out with some more wearable colors of this but like this is the biggest gimmick. I mean it's a gorgeous color and they say like you have to let it set and then kind of rub it and then you'll get the glitter to show up. I just had convinced myself that I'm such a glitter fan that this was going to become one of my go-tos and it's a pretty color, don't get me wrong. I don't have anything really bad to say. I mean it is a gimmick but it's pretty on the lips. I've been into more nude lips lately. I do go in and out of phases.  But for the last few months I've been all about the nude lip and girl I just never use this.  So that was everything. I probably could have named a billion other things but these are things that I specifically remember purchasing and just being like I need this. It's going to become one of my all time favorites and as you can see they just didn't. I'm so glad I could kind of focus some light on them because of this video. Gave them a little bit poor use and I'm so excited to bring out some of these products and actually start using them. Like I said before, check out Kelsey's version of this video and subscribe to her. She is so funny. She really makes me laugh and I learned a lot from her. She is just such a sassy person and I cannot get enough of it. I think that's all I have to say for today. Thank you guys for watching this video. I hope you liked it and I want to know some of the products that you bought that you thought you would use more. It doesn't necessarily mean you don't like them. It's just for one reason or another. You don't grab for them as often. So I hope to see you guys in the next video. Remember to subscribe to my channel so that you can see me in the next video. And yeah, so I hope you guys have a great rest of your day. Thank you for taking the time to watch my video. Bye guys.


Over 4,000 intelligently displaced persons living at a Kudesan Maraban radio IDP camp in Chikung Local Government Area of Kaduna State have received clothes and food items to alleviate their challenge. The 10 communities displaced persons including women, children and men seeking refuge following terrorist attack on their communities after losing a source of their livelihood. Plus, the news correspondent, Habila Darfa, I file in this report has been presented in our studio. The Incident attacks on communities especially in northern Nigeria has increased the number of widows, orphans and many families displaced in most affected areas. Some of the affected persons have fled their ancestral homes to the urban centers to seek refuge without any source of livelihood. This ugly development prompted the humanitarian group a co-smile support and empowerment initiative to restore hope of the widows, orphans and men who are worst hit by the insecurity and are in dire need of basic amenities to cope with life challenges. Speaking at the distribution of the food items, the team leader of the group, Blessing Sundays says the gesture is to support and put smile on the IDPs. Displaced from their communities, once upon a time these people were doing well. They have everything it takes to take care of themselves and their children and we all know what is happening in the country. In fact, some of the kids are not even going to school and these children cannot just sit at home like that. The women, some of them are not even doing anything. They're not doing anything. The youths among them, they have nothing doing. So we really need to assist these people. They can't be left like this and that is why we came here with our team to see how we can support them in our only two ways. They need our support in your only two ways. You can please come and support these people. Also the IDPs camp coordinator says life has been difficult for them since they left home a year ago. They commanded the effort of the group for providing food and clothes to the IDPs. They also asked the government as a matter of urgency to restore peace and order in the affected communities. The most support we need now for our place to be calm so that we want to return home. All these are like our culture, we are not used to this. We are hard workers. We work with our hands, we work with our strength to support others. Now we are the ones waiting for support. It's not in our code to at all. If we go there, we firm, I think we can produce, we can support our safe and support others outside. We have three churches from our side. So we appreciate for the supporting us and other villages that are supporting us with race, rights and clothes and other things. As the government continues to find a lasting solution to insecurity, the affected persons are hopeful they would one day return to their ancestral home.


It's not the living wage that was campaigned for. Like Lenin said, there are some... Definitely it's where nothing happens. We've got plenty of time for the enemy. We've got plenty of time for the enemy. So we've seen Matt Hancock age by 20 years. The Trumpy Corbyn gets into power all these billionaires don't play. Well, I don't take that view. There's actually feminist Marxist theorists who write about... Mining people up, probably, on the right. Oh, I love that you... You do? I don't get it. It's a lot of them. I'm gonna let them see pro somebody like Obama. I'm not pro Obama. I've been a critic of Obama. I'm a critic of the Democratic Party because I'm literally a co... But there's one thing I literally never get asked to talk about. And that's Islamophobia in the Conservative Party. Why? Earlier this year, it was found in a survey by YouGov that 60% of Tory party members think that Islam is a threat to Western civilization. 54% think Islam is a threat to the British way of life and a previous poll found two-thirds of Tory supporters think that there are parts of the UK governed by Sharia law and it's not just randos in the grass-roots that are the problem. This week, the Guardian published a dossier of Tory councillors who couldn't help but soil themselves at the thought of having to share a country with people from a different religion. Trevor Hales, a parish councillor from San Diego, took to Twitter in a froth because apparently my Islamic brethren are the enemy within and wrote that spineless governments had sold us to slavery of Muslims. Beverly Dunlop, a councillor in Bournemouth Christchurch in Paul, posted in Facebook groups saying I hate to ban anything really but I suggest we start with mosques. There's Tory priorities for you. Fracking good. Places of worship. Very bad indeed. Hang on, she doesn't know that we're allowed to pray anywhere, right? Like it doesn't have to be a mosque. And Malcolm Griffiths, a Conservative councillor in Redcar, approvingly quoted a Dutch fascist who said the genetic damage done to the Muslim gene pool since their prophet allowed first cousin marriages 1,400 years ago is most likely massive. For comparison, here's what the alleged genetic pinnacle of Western civilization looks like. But it's not just local councillors and enveloped stuffers mouthing off about Muslims. This form of racism goes right to the top. Barely sentient, sexually incontinent, Mark Boris Johnson refused to back down over referring to Muslim women who wear the niqab as letterboxers and bank robbers in his Telegraph column earlier this year, despite his comments being linked to a 375% direct increase in Islamophobic hate incidents. Thing is, Conservative Party Islamophobia isn't just distasteful or downright offensive. It has huge implications for how Muslims are treated by the state. Michael Gove's views on Islam were described by Tory Piers Ayuda Wasi as deeply, deeply worrying. Baroness Wasi suggested that David Cameron had been radicalized by Gove's book, Celcius 77, in which Michael G. Unit's selective use of polling was used to indicate that extremist views were commonplace amongst British Muslims. Speaking at the time of Donald Trump's Muslim ban, she said, if Michael had been left to run this anti-extremism policy in the way he would run it, we would be seeing the kind of things that we're now seeing in the White House. And despite Sajid Javid bouncing or would-be Tory leaders into agreeing an inquiry into their party's Islamophobia problem during the contest this summer, it's since been kicked into the long grass. Should we have an external investigation to set a party into Islamophobia, guys? A few days ago, Michael Gove couldn't tell the Today programme whether an inquiry would be independent or internal and suggested it wouldn't even focus specifically on Islamophobia at all. So why don't the media take Islamophobia in the government as seriously as they do pockets of bigotry elsewhere? Simple, because they're complicit in it too. Earlier this year, the Muslim Council of Britain launched the Centre for Media Monitoring. And in their debut report, which analyzed over 10,000 articles and clips, they found evidence of widespread and systematic bias against Muslims in media representations. I've said it before and I'll say it again. 59% of all articles analyzed and 43% of all broadcast found that Muslims were associated with negative behaviours, with terrorism, unsurprisingly, being the most frequently associated theme. The most biased outlets tended to be from religious or right-wing print publications and the worst TV programming tended to be topical news and investigations programming. Interestingly, national broadcast media tended to be much more negative about Muslims than regional media. Leading the report's authors to suggest that while on the day-to-day and local level, Muslim communities aren't actually that different from their non-Muslim neighbours, in national representations, their portrayal is skewed to suggest that our presence in this country is some kind of existential threat. And that means that anyone who's perceived to be an ally of Muslim communities in the UK, particularly if they're on the left, gets tarred as a terrorist sympathiser, which in turn allows Islamophobia to be laundered through the prism of anti-socialist sentiment. And then stuff like this happens. It's not just the Tory party who give the impression that Muslims are an easy target for discrimination. In relentlessly enforcing a double standard when it comes to racism, the establishment media are letting the public know that Islamophobia is an acceptable form of racism, just so long as it suites the purposes of those in power. So if you're wondering why the media don't seem to care about Tory Islamophobia, the answer is simple.  Just share this video. Make sure that you're following us on Twitter, on Facebook, and subscribed on YouTube. See you next time.


Hello everybody. I know that is a bad time because the most people are now in in the land area but we want to share with you a pet project that Moises and me created the last month. First of all we want to say thank you to the organization to choose us to be here. This is very important for us because it's a, like I said before, a pet project and we want to share with all people that want to be interested in this kind of things and join us to create a very powerful tool. First of all we want to present us Moises. Hello everybody. I'm Moses Martinez and say there. Currently I'm big data and architecting a single international company. I have been reshared in different universities related with machine learning and other kind like this. I'm an organization of TechFest. If you know what is TechFest, I invite you to join us in March and if you want to share your knowledge or anything in which you are working, we have a code for papers opening and everybody is invited to send our proposals. This is not me. This is Moises again. Okay. This is me. I am Ines Huertas. I work with big data and machine learning. I am an entrepreneur. Actually I am working like an advisor for two companies. One made TIF and the other is the wayside project where we are trying to build the most advanced we will share in the market. If you are interested, we will talk before, after. I am our latest magic coordinator. I am in the open data NASA program, a program from NASA to explore and explore the open data that they have. They have a storage. I am founder in WBU Startup C. It's an association for women in business that we try to promote and to get some networking. And I am training in Platzi and linking the platform. Here you have our contact. But we are here to talk about other kind of things. Who works with machine learning every day? Wow. Cool. We are not so much but the people work with machine learning. Who knows what is key flow? Wow. You are in the edge. Key flow is a tool that we want to share with you in the session how it works and how we create these pipelines with big migration. But this, Moises, we will talk more advanced. And now who knows about birds? Really? It's amazing. Cool. I think this is not the best forum to talk about birds. But why not? No? Because six months ago, more or less, I think so, Moises and me, we are looking for a new project, the project to work together because we meet each other two or three years ago and we are talking about the possibility to work together. And we say, okay, maybe we can search a project, a social project and try to build something together. And I think so was before the summer when he called me and said, I have the data. And I said, I am ready. But I don't know what is the data. I don't care. And he started to explain me about he finds a data set about bird migration that could be interesting to explore. And I say, okay, I don't know nothing about birds. Really, I didn't know nothing about birds. And he explained me, okay, maybe we can find some factors that can correlate with the birds migration. And I say, okay, maybe first we have to study a little bit more about birds. And this is a little bit introduction. Bird migration is a regular season trips, often the north in summer and goes to the south in the winter looking for warm areas. The migration that the birds migrate is about to get better representation, get a better representation and get better quality of life. Not because they need warm temperatures and they have to move when the season comes. Okay, if the birds migration are season and start with a change in the temperature or in the climatology, in the climatology areas. And this change is based in variation in temperatures. I represent another factor. So what happened with the temperature in the last century? I mean, the climate change is something that has happened. If we take a look at the global average temperature in the last century, we can say that there are an slope, that there are high. And we can think, okay, if the temperature is changing, maybe the roads or the dates or the duration of the birds migration are changing too. Also, we start to think other factors that can apply to this problem. For example, we start to think about the carbon dioxide. The carbon dioxide is very correlated to the temperature. I mean, the concentration of carbon dioxide is growing parallel to the temperature. And when we were there, we start to think, okay, if the birds migration depend on the change of the season and the season are affected by the different factors like the temperature or the carbon dioxide, maybe we can find something that can correlate. And, on the other hand, we find this kind of news or studies that there is not only that we say, okay, maybe it can be. We find that the BBC, the Guardian, the European Union has studies that say something like that. Then, Moises and me say, okay, we have to try to create a CORO project. The CORO project tried to find patterns that affect bird migration and find external variables that can affect too. All these using machine learning. And what we use to deploy everything because it's okay, we can find the data, but we need an infrastructure where we deploy all these things. Also, we want to do it in an automated way to be easy to use. And for that, we use Google platform and a lot of tools that Moises told us that the platform has. But we start by the beginning. What is the machine learning? I know that if you work every day with machine learning, this is so easy for you. Okay, but it's a little resume. Well, we can have samples with different information and features, and these features could be labelled. When we say that the data is labelled, is the data has association with some status. For this kind of data, we can throw in algorithms when we input the labelled and build the model and then try to predict with raw input the new output. In this kind of algorithm, we call supervised set. There is other kind of data where we have only the sample. We don't have the labelled. For this kind of data, we used to use algorithms that can segment or classify by groups different features that the data has. At this kind of data, we call this kind of algorithm we call unsupervised set. And then we have other kind of data that is a group of action, states and rewards where we work with agents and with an environment. We are trying in design, usination, actions, and updating this state. With this algorithm, we try to predict what is the next action that the algorithm should use. This kind of algorithm is called reinforcement. In our project, we have different kinds of data that we will tell you. And mainly, we are using supervised set and unsupervised set algorithms. Trying to predict temperature change and the carbon dioxide from change emulation or identify typos bars using unsupervised data. And now my sessions told us how we can create all of these infrastructures to get more information about the behavior. Okay. Then after finding the data and define the different machine learning algorithms that we can use, the next question that we make of us was who we can create models using machine learning algorithms? The first problem that we have is because the data that we found was a lot. Many, many gigabytes of data with information about location, different factors related with the temperature, the climate, weather, and many things. And the problem was that we can manage all of these data with a normal computer. Then we have to define an infrastructure or something to do this. And usually when we have to face a machine learning problem, we have to define a process of training and a process of evaluation. This is the useful and normal process in which we have training data, a group of algorithms, and we have to create several models until we choose what is the model that obtain better result for the thing that we are doing. And after that, we have to deploy this model, okay? We need an environment in which we have to deploy this model in an automated way if it's possible to execute the inference that allow us to get our result depending on the thing that we are predicting or classifying. Then the question is, okay, we have all of these and how we can do that? How we can automatize this process? How we can do it this easier according with the big number of data that we have. One option is to use codeflow. Codeflow is an alternative to create pylons in which we have several processes that have to execute it in a sequential or a parallel way to create like a machine learning pylons. And, okay, perfect. We have the technology, we have the data, we have the different algorithms, and now we have to choose who we are going to construct or build this machine learning process, least machine learning system. If you know there is a really famous paper for the machine learning team from Google in which they say that usually the part of machine learning, that part in which we are going to create the models, is the smallest part of a real system. Then if you are working only with machine learning, someone have to provide all the rest of the parts. In our case, initially we are focusing on creating the model, but we want to create models that can be used for other people for the community or another kind of technologies. Then we have to face this problem. To try to solve this problem in a simple way, we use codeflow, which combines Kubernetes and machine learning, allow us to create really simple pylons for our machine learning algorithms. But, okay, how is working this? I suppose that everybody knows how to work Kubernetes. Everybody knows who knows what is Kubernetes and how it's working. I suppose that many people, because there are many talks about Kubernetes today here, okay. This is the normal structure of Clouds which is standing with. We have different kind of Clouds environment, Google, Amazon or Microsoft. The structure of the computer, the operative system, and we have that middleware that is Kubernetes that allow us to put a lot of different machines or different containers divided in pods that execute different kind of service. All of this element that we have in our real machine learning system, not only in the part of machine learning. Who is working QtFlow? Okay, QtFlow offers another middleware over Kubernetes that allow us to execute all of these things using the pods and the infrastructure that Kubernetes deploy. And what is inside Kuberflow? All of this and much more. There are many tools related with machine learning, starting with TensorFlow, of course. TensorFlow, Boa, Ergo, Seldom Core, Keras, everything that we need to create our models and deploy them in a simple way. Perfect. Okay. We have the data, we have the technology, we have everything. Then we can start our pet project to try to create a model to see if it is possible to predict what is the, it is possible to predict the climate change. Okay. But to do that, we have to create a pipeline in QtFlow and who is working this? Who we can create a pipeline in QtFlow. Okay. Usually, pylons in the machine learning environments are full of process, different kind of process, related with data ingestion, data storage, visualization, exploration of the data, data transformation, data verification. And we are not interested in any of this. We are more focusing on how to create models in an easy way to allow people to use it. Okay. Then we need a tool that allows us to automatize these or simplify the process of deploying all of these. And the way it's using QtFlow. QtFlow, it's really easy to deploy. Not really. If you want to install inside of the cloud, you have to do some things. But, unfortunately, Google offers a system that deploy QtFlow using a web form. Okay. Oh, no. There's no pointer. Okay. This form that we can see is the easier way to deploy QtFlow in a Google cloud platform. The only thing that we need, it's an account, of course. It's really easy to get one. And a project. We have to create a project inside of our Google cloud platform. To do this, we go to the website that is there in the bottom of the slide. And we fill the form. The two most important things are the authentication mode because you can use the credentials that Google offer you or you can choose for a user and password system. And we have to define which is the location or the zone in which you are going to deploy all the elements of your cluster. And the version, of course. Some months ago, you have different version of QtFlow, but there is only one. There is not a lot to choose there. But when we try to do this, we can face a problem. If you are using the free tire that there are many people that try to use for a small project or to try new technologies or something like that, the authentication or the credentials using Google Cloud Platform don't work really well. For this reason, you can use the login and password thing. Okay. If you can create your own cluster, you can deploy it. But first, you have to create all the things related with the authorization. You have to define the name, the super email and the different domains that are authorized to execute your cluster. After all of this and 30 minutes more, you have your own cluster of QtFlow deploying in your Google Cloud Platform, which is amazing, because if you don't know anything about infrastructure, architecture or something like that, this is really good for you. And you can focus on the important thing. Create a pipeline of machine learning. Okay. Then how we can do that? We can use this website, which is the website that offers the system that we deploy it. And we can act then to use the different resources that QtFlow offers, that are two. The first one, is this dashboard that introduce all the things related with QtFlow. It's really similar to the Google Cloud Platform but in a smaller way. And there are the two important tools here. The first one is the notebook server. Okay. Everything that you want to deploy in QtFlow can be developing using a notebook in your own console or in your own system. But if you don't know many about this, this is a good way to do all of this. And the other thing is, of course, our pipeline and run server. This is the system in which we have to deploy our pylines and our servers and run our experiments and automatize all the things that are related with the machine learning process. Then using these two elements, two tools, we can create our pylines and don't care about anything related with infrastructure or requirements related with the architecture. Okay. Perfect. We are going to create our pyline to try to create that model to predict the climate weather. Okay. The first thing is the information. This was the most important problem. Okay. We find a good number of information that was published for NASA and the government of the United States. But finally, the information wasn't really good. Then because there was a visit in pictures in which they located in a map the location of the different group of verse. And we have to find more information. Finally, after two months looking for information, we found this paper and we have more information about several radars located in different parts of the United States and Canada. And we started to work with that information. The information wasn't really good, but there was a lot. A lot of gigas. But the information was related with many things and not a lot with migration and things related. And the other thing was temperature. Of course, at the beginning of this project, we want to try to see if the temperature of the environment have anything related with the migration of the verse. And for this reason, we choose this data set from NASA in which we have the variation of the temperature in the different years. Okay. We get the data and we have to see how to join all of that and prepare our pipeline. This is the pipeline and a really simple one. No parallel things. Only sequential threads. We have our Jupyter notebook. And three process. One for data ingestion and data loiter in which we are going to introduce the information that we want. Then we was in Google Cloud Storage and in Google BigQuery. A training phase that had to create our models in Google Cloud Storage in BigQuery for solar analysis. And finally, we have a serving process. All of this allowed the problem because we have a full machine learning pipeline. And all the things related with the infrastructure tour was delegated on Qt flow. Perfect. Good. Then how we can create this? The first things and mostly of the time that we employed here was in the data. In the longitude, we didn't understand really well what's the data. We didn't have any information about the different measures the different things related with that. And in this table, you can see some of the attributes that we have. For example, we have the period in which the sampling all the information of the different radars located in the United States and Canada was working. Of course, we have the weather defining like a percentage. We have the air pressure. We have the amount of snow. We have many, many information related with the weather condition and not a lot of information related with the bears. Only the movements of the bear located by location and longitude and latitude and not a lot of information. But there is the only thing that we have. Then we are starting to work with them and we try to convey all of this information with the temperatures. After that, we are starting to create our patterns. The first thing that we have to create was our ingestion phase or data loader. The first thing that we defined was two processes. One for data spliting. We are collecting that a lot of information that was for 20 years of collection of different files in different data sets because there is a lot of information and we have tried to combine there with temperature and another data for nature. This process was like an operation in QtFlow. It was the first step of a pipeline. How? We did. I don't know if you know many about QtFlow but this is the way to create a Python component. The different elements of the pipeline can be components that you can reuse or not if you want but have to be defined in this way. Why?  Then we have to define some elements for configuration. Here, we have to define the basic elements of the component which is that. After that, we have to relate this with a piece of source code. This is a simple code that collects information from the Google Cloud Storage taking all of that information, the different files, create a global data set combining with the temperature and put again the information into the Google Cloud Storage. After that, when we have the information, we have to connect this with the next element of the pipeline and we have to define it. Then we have to pour this diagram to the component that we are going to use in the pipeline. Okay, and how we can do that? We have to write code again. In this case, the code is a bit more simple, I suppose because everybody knows more or less how to work with machine learning. The first thing is like before, we have there the definition of the component. In this case, it's the training component a configuration for the container that it's going to execute the different models. In this case, this training process. This is the location of the container because the container when it's created is in the Google Cloud Storage system because it's the better way to take it and execute in our cluster. And then we have to define the Cloud Storage location in which we are going to save the model, the base image that we are going to use to create our container and the location in which we are going to put that container and how it's going to use it. And we have to define which is the function that is going to be executed inside of the container which is this time function that we have here. This time function, the things that have to do is to connect to the Google Cloud Storage again is to create all the different data states. Okay, all of this is not into the code of the pipeline but are not over here because there is a lot of things there and the code is really long. And after that, we have to define the different machine learning model that we are going to use. This is the first pipeline that we created. Really simple. We only use a deep neural network. Okay, in this case was a deep neural network regressor because the thing that we want to predict, it was the location of the burst. Try to see if there is any variation in the movement that they did to try to analyze if that was happened for the change of the weather or temperature conditions. And after that, we have to do the normal things for a machine learning process, a train process, an evaluation process. And finally, we have to return the results of our system and the training model. Okay, we have trained our system. We have that piece. And then we have to deploy everything. This was the structure of the queue flow system. After do all of this, we have to create that small pipeline that you can see there. And this is the basic process to join all the different methods that we defined in the previous step. As we can see each operation, each element of the pipeline is defined like an operation. We have data operator operation. We have train operation. And finally we have serving operation. Okay. All of these are a part of the different pipeline. And this is the element that we have to execute in our queue flow environment to run the experiment. And finally, we have to compile it. Okay, we have to compile it and create a DSL file to execute all the elements that we defined in the pipeline. Okay. Perfect. We have our pipeline. We have to try to execute our system and see if we can train. After that, when we get this step, we saw that, okay, we change the focus of our project. At the beginning, we want to create a model to try the variation in the behavior of the different barriers of the environment. And finally, we have to create a big infrastructure to create different kind of models. And when we are starting to test with the data that we have available, all the things, we see, okay, what about the climate chain? Okay. We focus in creating the pylines. We focus in creating everything to create the environment that we have with other people. But we move our focus to the real objective. Maybe it was because the data. And we didn't find a way to predict the global biosphere behavior that was the object at the beginning. Why? Maybe it was the data because we don't know a lot about the data. Maybe it was because we didn't have many, many data. But the good point, or the good thing of this project, which is in an early stage, we are working yet in this, is because we discovered that, okay, we can predict the change in the temperature according with the movement of the verse. We can define or predict the change in the humidity according with the movements of the verse. And of course, we can define using really simple machine learning models which are extremely simple how to the verse can move in the environment. But finally, our objective that was predict the climate chain in our opinion was a bit complicated because that was something astra and we don't have information to define, okay, we have to predict this group of variables in Europe of attributes to see if there is possible to say that the climate is changing because of the human actuation or because of the change in the environment. And the conclusion or the final conclusion that we have is that we need more data. And we need to understand better the data that we have available currently. For that reason, we can start with different scientists and research related with the bare movements and behavior in order to see if there is possible to collect more data, more information, introduce all the kind of species or understand really well what is the meaning of the data that we are using. Which at the end in this small project that we started some months ago was the most important thing. Have a group of data and see what we can do for that. And with this is the end of the talk and thank you so much to be here during the lunchtime which is a bit difficult. And if you have any question, you can do here or you can send an email or contact us across using the different social media. And the last thing that I'm going to ask or say is if you have two minutes, we have an evaluation for and we will really appreciate if you feel the for and say and answer the different questions that we have there. And this is the last thing and thank you so much for your attention.