All right cyber traders good afternoon and welcome on back here this Thursday April Fool's Day I should say April 1st shortly after 2 30 p.m. Eastern time welcome on back here for our afternoon meeting Let's get a quick sound check just to begin probably wake a few of us up live inside our trading room Great to see Michael and Lori loud and clear from them both at least Gary is well great to see him All right, there we go. Here's everybody else Kathleen Michael Ellen toe Norman Jerry great to see you both as well perfect All right, so without further ado, let's hop into it might I say welcome on back for all of us joining us live on Facebook live and YouTube like like Scott here on YouTube here Scott H welcome welcome back Hey heading into this afternoon. I was gonna copy and paste the news anyway for UX I end because we didn't really see the news headline when I did the morning meeting You know at 9 o'clock this morning when I ended up subbing in for Fausto and doing the morning meeting We didn't see the headline on the stock it ended up making a really nice push after the market open So I'm just gonna copy and paste really quickly right here No, we don't really trade this stock directly because of the news We're not gonna throw a dart on the wall and hope it lands But you know It's the news that allowed this stock to gain some more interest from other traders And once we happen to see you know a big breakthrough Resistance or support or you know just a lot of volume getting filled after the market opens That will provide us a better opportunity So really quick because this stocks moving right now if you're in this position I want you to be managing your stock as I speak and you know really if you're trying to get in the stock I want to go quick. So you have a chance to make some money, too Once the market opened this ended up making a bit of a slingshot as we call it ended up kind of Consolidating and pulled back towards 120 ish and then took off from there So really the resistance levels to watch on this trade throughout the day were 150 165 actually of all price levels roughly 165. I think I'm not mistaken 163 roughly the dip point before it ended up just taking off here. So outside of those well 180 There was a huge resistance on the ask earlier today heading into the late morning. I don't remember the size I'm just looking for my bookmap graphic here. I'll actually copy and paste that at least for all the slide inside the room You know, we ended up seeing a really big resistance off 180 and although it broke above it It ended up coming right back down shortly after and you know, here We are again to see the stock make a second big move up into the close Especially after it already dumped off pretty nicely from the from the early afternoon It's hard to expect this stock to make like a really big squeeze up higher before the close You know, perhaps by the end of the day maybe in the after hours We see this make you know that the faster more volatile move with there being less volume for that to break through on the ask So, you know, maybe best-case scenario is this goes up to two maybe a false breakout from two comes back down That's probably what I would be expecting on this trade if the stars align if you get any more God bless But otherwise, you know really nice push here and we'll see where it goes So for right now the UXI entry easily gonna put on the top of our watch list on probably won't stay there Probably by the end of it. All right, so with that in mind though I mean the other runner that we ended up catching throughout the late morning the early afternoon The ACY stock and you know, this one ended up taking me for a bit of a surprise in the sense of this squeeze throughout the late morning Like I said, but you know, we saw the stock going back into the end of last year. I just remember this the pop and drop I mean, it's hard to forget this type of move, right? Like you don't remember it exactly But you remember oh ACY very low float stock history repeats itself You know it ended up making a big pre-market pop this day This is what I was more so commenting on when I was doing the meeting this morning You know the same thing so for right now this stock has had an extremely nice run, you know run up today Let's count the halts actually while we're at it my trade station platform gives me a good ability to kind of see the Hults throughout the morning. So let's just count. Yeah one two Three this is a four. This is a down a hole here number four Five six seven eight Nine down halt here normally I would say when a stock gets halting on the way down. That's like game over. It's a wrap I guess, you know, this is kind of the rarity number ten right here and Let's see what else we got anything else in the other halts so far throughout the day Stock's been halted ten times today. That's crazy Probably making an 11th once it ends up really diving back down you're heading into the close 15 was a really big resistance You know, I always like to semi joke with this Let's say I mean it's not fully joking around but there's a reason why we call this the watch list, right? The morning watch list the afternoon watch list here It's not as if we're gonna throw a dart on the wall for this one and hope it lands We're gonna watch it. We'll put this on our list, you know In a rare case to see this squeeze up even higher into the close No, it'd be pretty interesting to watch and probably just study to see you know What what to expect the next time a low float runner like makes a move like this, you know But otherwise I'm not really that jumpy to trade it. So I'll put this on our watch list right hence watch list there But yeah, I feel like this is just gonna begin to dump back down It is kind of making a higher low off of 12, you know kind of back to support that was here, perhaps So maybe it does pop, but we'll kind of just watch a little case study Let's see what else we got though here. Otherwise, we'll go from top to bottom now We talked about numbers one and three actually really quick because Jake live inside a YouTube on our stream here is asking the LTRP B try saying that three times fast Easy easy asking thoughts done This does not look worth our time. This is like acy, but like even worse I don't know what the float is on this but probably less than a million. It's not even gonna pop up on my Scanner here, I don't think so So I don't even know the float on this Liberty trip advisor LTRP B I thought we traded the stock before if not this stock that may be a LT RPA Yeah, going back into 2020 again, like you remember these names offhand. So let's see LT So I type that in right LT RPA There we go. I Think this was the one that we ended up trading at the time. I could be wrong. It could have been the other I think it was the other This one actually looks just much more tradable, but yeah, either way Jake just I it definitely done I mean to see a stock squeeze off of that a little volume across the morning afternoon It would only just continue to dump back down. It's like 40 and under Oh, I forgot to do this when I came back So I was in the office office in say Austin, New York earlier today when 15 minutes back to my house Here I am in my home office, but once I ended up resetting up I forgot to remove all my crazy lines here So I ended up marking up this UPC chart heading into the open this of course was on our morning watch list We ended up doing pretty well on pre-market on this after eight o'clock and then after the open We ended up catching this nicely at first But you know this stock ended up coming right back down as you could see right So I think it's still shortable from trade station I thought for a couple of us it was hard to borrow maybe it was like for like a little bit But you know for what goes up comes back down if you could try and short this stock heading into the afternoon That might be your better play Outside of that I would just say 74 million shares for this stock where it's like kind of a new IPO or new listing here at least It's like pretty exhausted. I wouldn't be as keen to you know Anticipate another run-up on this for the close. What else we got here EST is near the highs of the day I ended up missing this one from earlier when I was in commute pretty much So this ended up making a nice sneak move up kind of off like super low volume You just see like the biggest spike is from the close yesterday But outside of that even zooming in here This whole run-up has been super light volume compared to what we normally look to trade That doesn't mean it has a big spread right now. The order book looks pretty decent So I'm not expecting to find a whole lot of like larger green transactions larger red transactions Etc. On the tape provided we're seeing retailers much smaller traders than that push the stock up So for right now just keep that in mind But I'll put this on our list this EST E trade hopefully maintains this tight spread. All right We got the MDL why stock now it's kind of skimmed through but I saw later call this out earlier off the early afternoon pop Unfortunately, it's dumped off to I don't know the amount of halts on this one. Let's see. We'll do another count Another head count here. You got one So this were reopens a pretty funky reopen here that too and that's it So yeah, this is normally what happens once the stock gets halted on the way down. It is game over So we move on from this at least but that that goes to show how crazy that acy was from this morning Or you know the early afternoon to see that get halted or like two or three times on the way down at least and to see You continue to fly up shortly after but you know, that doesn't happen that often at all All right, we got the Sun W stock Sunworks, right? We're pretty familiar with this trade at least But not doing a whole lot right now. So I'll probably pass The SNO a stock this was one that I put on the watch list again kind of hence the name watch list But you know, it doesn't mean we're gonna trade it necessarily It was kind of dropping off heading into the open That's what I was most afraid about and with that it ended up just continuing to drop off It was a very choppy move at the least and Or at the most and from there tailed off I you could say the same to be said with this Jag X trade Jaguar I pretty much hit this head on but you know I put this on our list because we're familiar with the name it was up off of news It was up big at the time still is up, you know decent percentage wise But it was dropping going into the open and with that odds Tell me that it's gonna likely to continue to drop before it even has a chance of running back up So how about this? I got a question here.  How much time do you have? That's my answer really because at the end of it and I might believe is that this person is a new trial member Just joining us from today's live event It's gonna take a lot more than just a couple of sentences a few days for you to actually Learn and master any method let alone hours, you know, it's not to say ours is the fanciest most advanced But for anything in life let alone trading and any strategy of trading It takes your months for you to master, you know, the methodology not just learning it But really, you know reacting to it all the fast moving level three on my screen the time in sales programs And actually while I'm talking just take a look folks. I want to just cut right back to the UX IN stock It's making its push.  I imagine that things gonna pop and drop from two pretty hard Is anybody in this trade live inside cyber group? I told you I wanted to move on move on from this stock quick I wanted you to be focused on this trade Still a few more stocks. I got a cut through on the loser side But uh, Jerry says he got in at $1.83. Excellent Jerry very nice right off our coaching session from earlier today All right, all right Jerry is the big winner so far perhaps a couple of us that will look to post a profit very shortly They might just be watching the stock ready to you know get out and post the trade here But nonetheless great job Jerry and for anybody else. I'll keep my eyes on the chat But I definitely anticipate a false breakout over to coming up. All right, so that in minds Let's go to the losers list right now folks. Don't really have a whole lot else on the gainers and maybe SM Maybe stock that loves to trend nicely look at this thing if you folks go back to an afternoon meeting I don't remember the day But if you follow me regularly then you've heard me talk about this stock and the one thing that I said was you know Anytime I see this stock on the percent gainers or losers list for that part, too It's like always on the best trends. This stock is always on like the best trends across the one-minute chart. It's insane Probably last time I said that was back here probably rewind back to March 19th, but nonetheless here. We are now SM great trade here taking us into the close Don getting in at 179.  Like three o'clock rolled around and at a nowhere We ended up seeing this make a huge pop off of news You know, this was the huge runner that we caught taking us into the closed news that came out with Microvision AB Microsoft partnership or something in lieu of that I got to jump back on the headline there But either way right it goes to say that we're not gonna you know Trade the stock because of the news directly, but we'll certainly you know become more interested in the trade But if it provides us a good opportunity to jump in then we'll take it from there So it took a couple of like breaks over 15 you could see so we caught it like on the way up We had to lean into it didn't really provide much room for a pullback But once it broke 15 first attempt it failed came back down on the next bar And then on the following bar there after it broke back above 15 and made its move up from that point So yeah, that the news on this was actually Microsoft winning a US Army contract for augmented reality headsets and I guess they get some modules or parts from micro vision this company here MVIS. I Was like this the new snippet there So nonetheless ended up making a huge move up did really well in the trade yesterday But for what goes up unfortunately comes back down We figured it was gonna come back down today But man it's still at the lows right now down 16 plus percent, you know, perhaps at this point now We'll begin to see this build some support and move back up That would be the hope and if it doesn't squeeze up higher than if it breaks down even more it is shortable So that would be a good opportunity. All right Let's see what else we got. I want to jump back to that UXI and trade pretty quickly I'm certainly keeping an eye on it on my other screen. I wish I couldn't you know tell you and pat myself on the back that I'm in it But I'm not I kind of came right back to my computer about five minutes before we started this afternoon broadcast So normally I would like try and be in a trade or something if it was moving well But I got to reset myself here. So I hope everybody's killing it on that UXI and To be honest, there's not a whole lot here on the loser side that looks great for the afternoon at least or at least a familiar name Maybe the XL here. We called that out at least from earlier. It was off the lows Not really too much going on there, you know squeezed up a little bit, but I'm gonna pass putting this on our list for right now The e-bond stock we talked about that during the morning meeting news came out about a direct offering on that trade It didn't break down to six dollars and ten cents the ADS there, but What else do we have I think there was one more that came out with oh, that's right And I you know what perhaps I could have just you know talked about this to begin the meeting We're certainly not not gonna trade this thing going into the close But holy crap what happened to this FT FT trade right to see it Just you know kind of metal back down taking you into the mid to late morning Maybe you know hopeful to see it make another move up going into the afternoon perhaps but out of nowhere I think that was the direct offering as well the common stock directing a direct, you know Offering of shares perhaps ended up just getting killed ended up getting halted on the way down even opened up You know and move you know lower from there, of course So unfortunately for the good news that came out on FT FT This couldn't be any better of an example to show as to why we can't hold on to stocks throughout the day And you know maybe turn a day trade that you were anticipating on getting in and out of quick and Turning that into like a swing trade or like a day swing So to say you never know when news like this could happen. So, you know, don't have hope you all right That's that's the the the worst drug that you can be on all right. So with that I think that's all I got on the losers list here Let's jump back to the UX. I ended up making the false break like I thought ended up at least You know popping over to coming right back down Don got out right at two bucks off the whole number. They're very nice Don great trade really nice trade You know, it's the type of stock at least right like, you know, even if you didn't do this right now This is the type of stock that of course is cheap meaning that you could trade more shares of it It definitely has a very liquid order book. So, you know, although it's making a big move It's not like a level five stock as we would categorize it, you know level one or two So, you know what? Hey, even if you didn't trade a lot of shares, you know on this position At least you could study from it and you have the confidence to trade more shares Perhaps the next time you see something like this make a similar move That's the best thing that you could do Don. So really nice trade either way Look at this thing drop back down now, right? Looking like a wizard getting out at two bucks right there All right, so folks for YouTube live Facebook for all this live inside the trading room Let me know if there's any other stocks you would want me to look at anything popping or dropping that I just didn't have a chance to look at myself You don't need any other stocks really it's UXI in all day, then I'll take it I'll be honest. This is like probably like my second easiest call out of the day the false breakout from to the first attempt here You know if it ends up building back up and breaking higher later on or coming up here Then you know perhaps you try and take the entry But you know the first attempt of it breaking that that too we're testing to you just can't keep expecting it to move up Higher and higher it's up 67 plus percent So, you know at some points got a reverse or maybe at least pull back a bit You have another iceberg order on the ask you're at two dollars and ten cents for right now in the UXI Jerry exited this trade right now at 196 live inside cyber group. You got 13 cents on the position very nice All right, cool.  Yeah, I got nothing else really here from Benzing a pro I mean, I'm sure there are some stocks, you know breaking highs or you know trending nicely here, but No real consistent stocks are popping up on my radar. It's fine by me I'll tell you what folks so we'll call it for right now We only have five stocks taking us into the end of the week, right? Keep in mind tomorrow. The markets are closed We have good Friday, you know, I hope everybody celebrate for those that celebrate I hope you that you have a great day for those that don't I personally do not I'll enjoy the day off I'll relax and have a nice day off myself. I hope that those who celebrate Easter coming up I hope that you have a fantastic weekend. I do celebrate Easter I'll be having a nice Italian dinner with that in mind So I you know for all of us in social media make sure that you like subscribe notify right on our YouTube channel YouTube comm slash cyber trading you Otherwise as as I always like to say feel free to join us live inside our trading room Go right down to the link to the link on the banner below to sign up a lot of talking today with that in mind So I'm gonna hop back to the trading room right now. I'll be back on actually right around 315 330 taking us into the close Talk to you soon. Take care


Hi everybody, we're back. This is Dave Vellante of Wikibon.org, and we are live at Oracle Open World 2011, and we're here looking at the backup and recovery spotlight.  I mean, effectively the CIO, right? I don't believe that's your title. Let's senior vice president. At this table, anything you want to call me? Yes, and SEI, very interesting company out of, you a baseball fan? A little bit, a little bit. So not crazy Phillies fan? Not crazy Phillies, Eagles, unfortunately. Sadly. It's early. Well thanks, Steven, for coming on the cube here at Open World, and before we get into it, I'd like to tell the audience a little bit about SEI, very interesting company, doing well, wealth management firm, to give us some details. Sure, so SEI's been around for a long time, not commonly known, I suppose, among Oracle world attendees. We don't have a big retail presence. Our presence is primarily through banks and intermediaries, but we provide a holistic set of wealth services to intermediaries to deliver to end clients, and that's in the form of asset management products, technology solutions, and operational outsourcing. The company started back around 1968 by the CEO, where we began a service of delivering outsourcing services to banks for trust processing. We built a technology product back in 1968 to deliver that, to support that service, and about 10 years ago we realized that to really extend beyond the U.S., we needed to build out a global capability, and the sort of novel thing that SEI took on as opposed to what legacy providers typically do is we took a Greenfield approach and started from scratch, and built out a brand new platform as opposed to taking a legacy platform and bolting on new capabilities. So we started that back in about 2001, and now we're in market, and again, it's a platform that provides holistic wealth services across all capabilities for a wealth provider, for any provider in the world. And we built that on a brand new infrastructure, comprised of both Oracle components for the under layers of the application, as well as EMC's products on the backend for storage subsystems. So talk a little bit about your role before we get into it and to the case study. What is your role as SVP? I'm calling it the CIO. Is it a classic CIO role? So SEI is not traditional in many, many ways. We're very avoiding titles, but I was actually the co-leader of the project from its inception. We actually took the organization and did something very unorthodox and said rather than build the platform with traditional competency units, we built a whole new organization reporting directly to the CIO. We housed those folks in the complex or the campus separately, and we took a very novel approach by saying what do we want to do to build on a new platform that is completely revolutionary in the industry? Things like client circuit computing. Rather than bolting on a CRM-like capability on a legacy platform, we actually put client-centric capabilities right into the DNA of the app by building from scratch. But my role was to kind of foster the development of that platform from its inception, taking the direction, taking from the direction of the CEO. And now I'm in a more pragmatic role of driving the infrastructure and supporting the platform from a production perspective. Do you currently report to the CEO? Currently we do not. He's actually, now that it's kind of reached its cruising speed, if you will. He's working. He's put it off to the CIO of the firm. Okay. All right, so, but so he's put it off to the CIO, so you report to the CIO? Now the CIO reports to the CEO at the firm? That's great. Okay, so do you, I was going to mean, in a lot of organizations, that's not the case. Yes. Right, right. The CIO might report to COO or to finance, right? What's your thoughts on that? I mean, you- It was interesting, what this project is, the CIO actually was doing the care and feeding of our legacy platform. We, unconnected to the CIO, worked for the CEO to get this project off the ground. So it was a very non-traditional organizational structure to get this thing to the place where it was viable. All right, so let's talk about this thing. I'm interested in the Greenfield approach, which a lot of guys don't get that advantage, but paint a picture of your environment, your shop, what are we talking about here? What's it look like? Sure. You know, virtualization, VMware that you're doing. Yeah. I still, it's a large team of folks, around 600, we've been doing this about seven, eight years in active development. The application architecture is your classic NTR application architecture. On the backend and the infrastructure side, we heavily use EMC for storage subsystems. We use the classic Oracle stack for WebLogic and backend DBMS. So it's a mix of Oracle and the middle tier of the platform and EMC on the backend. Okay. And so you've been an Oracle shop for forever. Since, with this platform we have actually. Okay. We were not prior to that. And we were talking offline. You looked hard at Exadata, did you not? Yeah, we have. We have. And presumably for analytics or data warehouse, right? Well that and just scale.  And for a large institutional, money center bank if you will, for fortunate to have that in the near future, something as scalable as Exadata would be a critical infrastructure component to facilitate running our platform. But frankly for us, we were able to, in a project we recently spawned, get a little more runway out of our traditional, if you will, platform infrastructure capabilities with VMAX and the Oracle stack that we have. So if I understand it, you were a DMX shop and you migrated as part of this project to VMAX? And that was part of it. So we took sort of a holistic look and said listen, rather than launch into an Exadata proof of concept or actually do a purchase, we said is there any novel ways by which we can get more runway, more leverage out of our existing infrastructure? So we went across the stack. We looked at software striping with ASM on the Oracle layer. We looked at the backend, DMX going to VMAX. We looked at the chipset, which was Spark and looked at migrating to Intel. So across the stack, we took a holistic approach and we're able to facilitate an incredible improvement in both proficiency and cost by leveraging those existing technologies. How about the OS? Linux, yes, we migrated on it from Solaris to Linux. Solaris to Linux, exactly right. A lot of migrations, a lot of transitions. Exactly right. How did that go? It was an exceptional win for us. We improved performance by about 40% and reduced our cost by up to about 50%, unit cost on the infrastructure layer. How were you measuring? All right, so it's purely on the hardware side. So if you look at the hardware side, not the software side, we're able to improve our cost structure by 50%. So you use an ASM, which is clearly a best practice in Oracle shops, right? And so taking advantage of that, how about the migrations itself? I mean, a lot of times, CIOs would tell me that migrations are risky. How did you mitigate the risk and what advice would you give to others that are going through something like that? So we've had, to be candid, we've had some, because of the amount of change that we're implementing in this platform, both functionally with new clients and with infrastructure changes, we've had a lot of issues, both at the hardware level and the software level. So we mitigated that risk by building an infrastructure that replicates production and totality bit for bit and replicating production workload in that environment for weeks, if not months, before we actually went live with the change. So we call production validation. We create an environment and create the workload that emulates production as close as possible. And through that exercise, we are able to implement those changes we just talked about without any impact whatsoever to our production level. Okay, so your advice would be to... Emulate production. Emulate production, spend the time and effort to do that and take that, I mean, essentially a brute force approach but it's the safe approach. Yes. How much data are we talking about here? In this platform, we have about 150 terabytes but we also have a lot of development work going on in parallel to our production workload. So maybe a half of that is development oriented and not production oriented. So okay, and you've got... So you've got your Vmaxes, is it fully loaded?  Pretty much, yeah. It's more than 150 terabytes. Yeah, yeah, it's probably about 200 when you look at what's allocated. So 200 terabytes. So there's a lot of talk at this conference and other conferences about big data.  But this is more of a traditional industry. Do you consider what you have big data? Big data is in the eye of the beholder. What do you, as the beholder, say to that? That's a great point. So when I think about big data, I think from a pure definition sense, it's data that's being generated from things like social media. Just an incredible amount of information that isn't relegated to the traditional view of the financial industry where you're either adding accounts or adding transactions. So I don't think we have a classic big data issue to contend with but we certainly do have, through the application architecture that we've developed, the ability for clients to generate data in a non-traditional way. Loading up documents, adding relationships that are necessarily applied to the firm and creating more information than maybe a traditional legacy financial application would. And I think what we're doing is looking at surgical use of technologies like I described a little bit ago and then also doing a POC around Exadata or a Green Plum to see what would we do if this data started to really explode. In my view, it's all around preparation and POC work so that you're in a position where when that time comes, you're ready to manage it. So I mean, everybody talks about this data growth. Tucci today said, you quoted some IDC data, 1.2 zettabytes shift last year. That's a lot, billion, trillion, trillion, billion, however you look at it. Most of that is unstructured. Are you seeing the same thing in your organization? Yeah, the only unstructured data that we really contend with currently is around what I just described around documents. So in the wealth management business, there's a heck of a lot of text-based information that intermediaries use with their clients and through our platform, we've just facilitated the way by which they can upload that information. So the most dramatic growth we've seen in unstructured data is around all those documents that they're able to put into a library form. But the other data is data that people are using in firms to do analytics and business intelligence on traditional financial data. Do you put that unstructured data on a VMAX? We do, we do. Okay, so how do you control the costs? So candidly, since we're in the early days of this platform, probably there's probably probably have 20 terabytes of unstructured data at this point, so it's manageable. I think when it explodes, which inevitably wills, and we'll start looking at some other tech. Are you tearing it, using Flash? Absolutely, Solid State, Flash, multi-tiered storage on the VMAX. So were you doing short-stroke drives before? We were not, so this in the past 12 months started looking at those kind of technologies in a surgical way. Yeah, so it wasn't as much of a no-brainer, some of those guys that were short-stroking, but what has the result been so far? I think, again, we're using it in a surgical way where the application needs it, where we have tables that require that high level of performance. Put them on there and let them rip, exactly right. Okay, are you using any kind of tiering software? We use what VMAX has in it to facilitate the movement of that data to the appropriate tier when necessary, when the performance warrants it. All right, good. So I know you're passionate about this project. Congratulations on having it be so successful. A lot of times these things, you know, you're refueling the plane in midair. Exactly right. And it gives, you look young, considering that. But I wanted to shift gears a little bit and talk about data protection. I know you're not a data protection practitioner per se, but as a senior executive, you got all this data, you got to protect the data. Talk about your overall strategy of protecting data. What are the drivers and what's your philosophy there? Yeah, I mean, I think when you think about data protection, you think about probably three things, the integrity of the data, clearly from a backup recovery perspective, time or speed by which you're able to facilitate that. And then thirdly, the thing everybody worries about it, which is cost. And then beyond that, it's sort of a commodity service to us. If you deliver on those three sort of dimensions of data, then back in recovery is rather straightforward. We do use a kind of a traditional approach. We use a data domain and network to facilitate virtualizing our tape library. That's been a very successful project, reducing amount of data through the de-duplication capabilities within data domain. So that's been very helpful. We have not yet gone into the space of SRDF for trying to replicate or mirror our data with our off-site provider. But the project that we facilitated through data domain and network has been successful in getting where we need, I think. What was life like before that? Was it tape-based backup? Yeah, tape-based. The logistics around tape and the error rate of tape and the amount of information we had to load down to tape was really daunting. So this project, I think, kind of delivered on our promise to get at those three angles that I just described. Do you find that de-duplication, obviously changes the economics of disk-based backup, but is it comparable to tape? Is it more expensive, but it doesn't matter because of the recovery? I think when you look at the end-to-end path where people are engaged, the people cost of working with tapes and moving those tapes off-site and the error rate of getting with tapes and then the amount of data that you end up moving down to tape as opposed to when you're exercising something like de-duplication, it's a pure win for us. It's a no-brainer. And I don't mean to minimize it as a commodity, but it's a rather straightforward decision to move to something like... So presumably you still use tape, right? We use tape for some of our backups that still need some off-site recovery, but for the majority of our backups are through virtual tape. And so it's virtual tape, and so for off-site stuff, you use tape or deep archive? Well, we do use the ability with virtual tape to actually move that data from the virtual tape library to our off-site provider in an asynchronous way. So we have taken advantage of that. That's disk-to-disk? Yes. Okay, and then they store it on disk? Yes, that's exactly right. In the virtual tape library, we're off-site. Okay, so you're not archiving everything to tape? No. You've essentially eliminated a lot of tape, is that correct? Yeah, a grand majority of it, absolutely. Except stuff that you have to keep around for compliance. That's exactly right. You answered my question for me, I appreciate that, you're perfect. Well, I mean, that's kind of where tape is. That's exactly right, that's exactly right. Now, we're getting a lot of demands. Our clients put a lot of stress on us to get into the space of actually marrying disk offline to really optimize that, turn around a disaster. We're just not quite there yet, but we will be, I think, in the future. So the driver there is just recovery time from a disaster? Recovery time. Yeah, and that's another big project, right? That's another big project. But, so what's your DR strategy today? Is it recover from? Virtual tape, exactly. Virtual tape from your off-site provider. That's exactly right. And then this would add a dimension to that, which is what gives you more control or faster recovery? Well, I think primarily faster recovery. I mean, if you mirror the data live, then you'd be able to facilitate a medium recovery. You'd still need your tape backup, obviously, in case you had a data integrity issue, but that's the way to optimize the turnaround of the disaster.  Just generally, and obviously the backup as well since we're talking about backup, what's your sort of five-year vision? I mean, I think inevitably, as this platform grows, as we expect that it will, it's going to be moving to the simplicity and the power and scale of these database appliances and middleware appliances. It's inevitable for the industry, I think, because the amount of time it takes to do the care and feeding of provisioning hardware and maintaining hardware, the simplicity of those boxes is very impressive. And then there's just a scale. To be able to handle extremely large amounts of data in a scalable fashion, we'll inevitably move toward those appliances. But as I described earlier, a project that we talked about is a way to sort of extend the runway of existing technologies before we get to that place. What, you're obviously a pretty big EMC shop. We are. We have been for a very long time, for 20 years. Oh, so it started with, what, Symmetrics? Yep, Symmetrics, way back in the very beginning, yes. Yeah, was that one of those deals where Dick Egan came and like stuck one on the loading dock and said just try it? They were very helpful, let's leave it at that. So it's been a long relationship. It has, it has been. Why, I mean, obviously it's worked, or you wouldn't be sticking with them. Why? Why has it been so successful? Well, I mean, I think EMC is a very client-focused firm. I mean, that's a cliche, but they clearly step up. And I'm maniacal about clients. They are. That's what we said. When there's an issue, they step up every time. So it's a service, really. It's a service, they've got some features. We talked about Exadata. It's a wonderful box without a doubt, but something like Cloning, which is incredibly critical to us, we use Cloning all over the place to facilitate our intraday or operational recovery. And the absence of that in Exadata is sort of a shortcoming for us. And until that becomes resolved, which I'm sure it will inevitably, we need to continue to leverage EMC technology. That's something we didn't talk about. The copy services around Exadata, you felt were not as mature or robust as what VMAX could offer? Certainly in the cloning space. I mean, I think that, I mean, I'm sure that they have a great data recovery or a backup and recovery capability, but I think in the sort of the operational recovery, the cloning capability, that's a weakness in our view. We use cloning everywhere. I was to say, why is that so important to you? Well, so I mentioned that we do a lot of development. So we're still in a heavy development mode, and we'll take clones of our production data to facilitate our testing, our development activities. And we do those over the weekend, so that on Monday morning, when our development shop comes in, they have a full copy of production. We use cloning to asynchronously get a copy of production and then perform a backup off of that. So that is highly effective and is non-disruptive to our production application by exploiting that cloning technology. So my last question is there's got to be a to-do list that EMC, you have for EMC. What's on there to-do list in your mind? What could they be doing better? Well, you know, this Green Plum thing, we're taking a look at it. And for us, it's comparing Green Plum to Exadata for that next chapter, as I talked about database appliances. And if they can deliver out a lot of the feature function that we feel we need in the data appliance space, we'd love to continue the relationship with EMC. So we'll see where those two products go, frankly. Steven Zay of SEI, it was a great discussion. Thanks very much for coming on theCUBE at Oracle Open World. You here for the week? I'm here for the week. Well, enjoy the event and hanging out with your peers. I'm sure there's a lot of events and some events you want to check out. So thanks again. Thank you. Great to meet you.


Hey everyone, my name is Paranitha Rahi. I am a Principal PM Lead in the AI Frameworks team within Microsoft and I don't have a history lesson on PyTorch to give because I've just been involved with the community a year back, but what a year, right? I mean, if we talk about the AI landscape, a lot was already covered in the previous talk, but there's so much game-changing work that's happening around us and all is powered by PyTorch in the back end. And so a fantastic year and we at Microsoft like have been part of this journey early on, collaborating with Meta in the nascent stages of PyTorch in now with the Linux Foundation and are so happy to be a part of this community. So as we talk about this AI landscape and if you remember the chart from the previous talk, one thing that's changing is the complexity of these models, right? GPT-3, 175 billion parameters. GPT-2 was I think a hundred times smaller. GPT-4, even more. So complexity of this data is increasing. The amount of data that these models are getting trained on is increasing. And so organizations that are using or building these models have a problem at hand in terms of how do we handle this and how do we do it efficiently and in an agile fashion because there's rapid innovation happening. We've had like a slew of innovations just at Microsoft across various products and cloud-based solutions that we have, be it chat GPT, Bing-based solutions, Office, Github Copilot and so on. So a lot of these parameters and the data model complexity requires time commitment. So how do organizations scale up and perform these tasks efficiently and also responsibly because it's not just that we want to output a model for product innovation, but we want to make sure that these models are built in the right way and are serving society broadly and not narrowly. So there are a couple of aspects that I'm gonna cover in my talk today. The first one, how do we, if you are a business owner or a data scientist or an IT department lead, if you are posed with the question of, how do I train state-of-the-art models that are applicable to my organization? One of the tools that we provide within Azure is a pre-built environments and I'll talk about more later in terms of how we build them specifically for PyTorch as well. Another question would be, how do I innovate fast? Training times are longer. There are deadlines that everybody is working with. How do I equip my scientists and researchers to do it fast? We offer a bunch of training optimization techniques built on the PyTorch community as well as open source accelerators that I'll cover in the coming slides. I have a model, but I want to make it small enough because I want to deploy it on an edge device or I have limited GPU resources. How can we use Onyx Runtime to do faster inference? And then finally, as these organizations are building these models, you have to make sure they're secure, compliant, ethical. So how do I make sure that I integrate an audit trail for these models that I develop? So I'll cover the aspect of responsible AI in my slides. So deep learning on Azure, I think we have a pretty comprehensive and trusted platform for building intelligent enterprise-based solutions. You have responsible AI tools that are integrated as well as frameworks of choice that are integrated and it's all powered by flexible compute and capacity with GPUs at scale like the A100s and B100s from Nvidia. There are four pillars that we kind of bundle our value prop within Azure ML. First is to improve time to value with rapid innovation and integrated tools. Second is to operationalize at scale with ML Ops. Third is to develop on a secure, hybrid and compliant platform. And fourth is to deliver responsible solutions. And we have seen both startups and seasoned customers benefit from it. So Fashable was like an AI-based fashion startup that uses our curated environments so that the researchers can focus on the task of building these models and not so much of the interdependencies between the various packages and libraries. BMW realized time savings for the training of their image models with just a few clicks. PepsiCo was also able to improve their model accuracy back to 40% while still meeting the compliance needs. And Ernst & Young, they build models that predict loan decisions and so it was able to improve or reduce their disparity between men and women from 7% to 0.5% when they make these loan decisions. So all of these are examples of how the tooling that we have at Azure and ML help meet our customer's objectives. So we're at the PyTorch conference and why do we love PyTorch on Azure? PyTorch is a framework, aligns very well with our goal of making end-to-end model development seamless for our users. It's widely accessible. The Python interface makes it very intuitive and easy for users to build their models. We've seen rapid development technologies and functions that help developers build out these models faster, as well as we've been part of a strong ecosystem right from the start. So PyTorch is very critical to our growth, but how do we make it even better? And so these are some of the principles that we try and apply to make PyTorch work even better on Azure. So first is performance. We improve performance of PyTorch through systemic high leverage improvements and I'll talk about some of those later. The second is that we want to engage the largest production-ready PyTorch models at scale and also provide portability. Like you should be able to take your models and deploy it across all computing platforms that can be leveraged for AI and then of course, developer productivity. And we want to do this while building leadership on the open source community. So how does PyTorch work on Azure? This is a quick kind of a summary slide, but this is not just one way. This is like just an explanation of what I'll be talking about more in detail, which is the Azure container for PyTorch. So at the bottom layer, you see the hardware stack, which is powered by the best in class NVIDIA GPUs, like the A100s, H100s and more. And then on top of it, we have multiple ways in which a developer can come in and build their training workflows. So you have the Azure ML SDK, you have the CLI and the Studio, and you can access or build your training jobs using any of these. And then within AML, we provide comprehensive training frameworks. And this framework is packaged in what we call the Azure container for PyTorch, which is a curated environment. And I'll speak more about it later, but it has the latest and greatest technology that is required for faster innovation of your ML models. And then on top of it, users can easily plug in their scripts and then specify their compute targets to create their models. And then you can then further deploy it and convert it into Onyx for faster inferencing. And it allows you a common place where you can monitor and run these like repeatedly. And you can also share your training jobs, your models across all of the researchers and scientists within your organization. So what is ACPT? ACPT is an optimized training framework and the primary goal there is that when an ML engineer is starting to build their ML models, they don't have to worry about setup time. How can I make it easy for someone to start off and use the best and the most recent technology that's available to get going? So like, PyTorch versions keep getting updated every time. How do I make sure that somebody has the most recent? Onyx runtime got a new packaged out. How do I make sure that they have the recent and it's used kind of consistently across all of the developers in your team? Check one, two. Sorry, can you hear me? Okay, yeah. And as well as we also kind of provide it as a common place to package a lot of the open source training optimization technology that Microsoft participates in and provides as well as like the native integration with Azure makes it easy for you to like monitor and deploy your models as well as like share across your organization. So with ACPT, we now have PyTorch 2.0 included. Meta announced, or sorry, the PyTorch Foundation announced ACPT on March 15th and the Azure container for PyTorch released within a week, including this latest package. PyTorch 2.0 is accelerated, it's fast and runs large training jobs efficiently. And one of the good things about, the Torch Compile is the main API for PyTorch 2.0 which wraps your model and returns a compiled model and it's backward compatible. And so with ACPT, now you have an image that is already tested for against Python versions against CUDA drivers and all of this so you don't have to do the testing to make sure I'll figure out what package works with which other, what are the dependencies that I need to worry about as well as we test these curated environments across a lot of Microsoft 1P workloads. So one thing we try to do with Azure ML is that the goodness that you see in a lot of technologies that is out there, whether it's the Bing solution or any of the other things where 1P teams have tried or cognitive service team, et cetera where they've tried these environments and we've already tested them for security, compliance and all of these things. That is then fast on to the larger community in the sense that you don't have to worry about these basics of getting things right and just use the image at hand right away and do more. Similarly like PyTorch 2.0, we also have open source, a deep learning optimization library called DeepSpeed that's included within ACPT or Azure Container for PyTorch. With DeepSpeed, you can get training done about 10x faster for your models. The cost is like 6x cheaper. It's again open source and it provides excellent system throughput. It's an easy config to just apply to your training jobs with a couple of parameters you can enable DeepSpeed while using Azure Container for PyTorch. Onyx is again another collaboration we had. It's a cross-platform machine learning model accelerator. It has a flexible interface to integrate to hardware specific libraries. You can use Onyx Runtime both for training and inference and it is available within the Azure Container for PyTorch and ORT, it's a pretty simple wrap. Like if you want to use ORT module, it is instantiated from the Torch ORT backend in PyTorch and it enables a seamless integration for Onyx Runtime training in a PyTorch training code with minimal changes to your code. So with ORT module, you can wrap the module and just use it within the code. It's a simple one-line change and that can give you pretty good improvement in your training time. We've seen up to 1.4x faster training with Onyx Runtime for training. And similarly for inference, you can take that same model and you can convert it or you can import Onyx Runtime as ORT and create an inference session that then allows you to then deploy this model across a host of different hardware as well as if you want to do it on device or web, ORT makes it much, much easier for you to do that. And we have seen our customers spanning a range of scenarios be able to use this. So for example, we have InFarm, which runs a computer vision on a tractor and uses Onyx Runtime.  Some of the results we see with Onyx Runtime, so on the right, there is an example that was published by Optimum. So Optimum integrates the Onyx Runtime training through an ORT trainer API and this trainer extends their transformer and we've seen that training time can be reduced by more than 35% for many popular hugging face models. And the performance measurements here were done with Onyx Runtime for training in the second run and then Onyx Runtime plus deep speed zero stage one in the final run. And all of these things are pretty much easy for you to do because with ACPT, given all of these packages are available in one place, you can compose your base PyTorch with ORT or your base PyTorch with deep speed and get these results faster. On the right, we have a recent performance analysis done with PyTorch 2.0 with several hugging face models in both eager mode and compile mode. And what we see is that Torch Compile improves upon several models. However, when we compose Torch Compile with deep speed, the gain in throughput is even higher. And similarly, deep speed plus ORT provides gains for several model types. So this is an area where we continue to like improve and evaluate, but the key point is that these technologies don't work in isolation, but you can compose them to get even further gains and reduce the costs of running these models and reduce your training times even further. Also, we have introduced with the recent release of Azure Container for PyTorch, a new technology called Nebula, which boosts checkpoint speeds by up to 1,000 times. This is still in preview and it is fully compatible with PyTorch. It runs only on Azure ML, but again, Nebula can reduce checkpoint times from hours to seconds. So you can save as much as 95 to 99% of your training time if you use a simple checkpointing capability. So how do I use ACPT? Azure ML is available both as a command line interface, an SDK and UI. I'm showing you some snapshots of the UI view. If you go into the environments tab, you can see and search for ACPT or PyTorch. You will see a list of models and as you can see, they're available for various versions of PyTorch. Sorry, not models, but environments. And they're available for various versions of PyTorch. You can easily use a pre-existing curated environment for your jobs. But what if I don't want to use a curated environment as it exists because I have these additional dependencies I'm building or I'm training a Visper model and I have transformers and accelerators that I want to add on to a base image. We provide a capability of creating custom environment. You can start from a pre-existing curated environment and then you can add, so the first line here is just a curated environment that exists and then you can add and install additional pip dependencies to create your own custom environment. And once this is saved, these curated and custom environments can be used across your training jobs in Azure ML. So it's not like it's tied to that one instance. You can share it with your teams as long as the workspace is shared. Then how do I use this along with some of the acceleration technologies I talked about earlier like Onyx Runtime and DeepSpeed? There's a simple argument that you've provided to your training jobs. There's a capability to upload scripts or associate scripts with your job. And within that, you can provide the parameters. Some of the parameters are shown here. One is from the optimum integration that I talked about earlier and that just extends the trainer and transformer so you can specify a parameter here and use that. Similarly for DeepSpeed integration, you can upload a JSON file for your training job which has the configurations required. And it's a very simple and seamless integration doesn't take a lot of steps with a couple of clicks and a few lines of code. You should be able to use these technologies pretty efficiently within your training jobs. So now I have a model. What do I do with it? And how can I kind of use it and make it easy for sharing? So once you have a model within your training job, you can register it within Azure ML. And once a model is registered, it can be shared within your teammates. You can deploy it and you can create an online endpoint which can be tested within Azure ML but also you can call it through REST API, et cetera. But you can register the model as is in PyTorch or even better, convert it to Onyx. So that will give you the speed ups that I was talking about earlier. It can give up to 17x reduction in your inference times and you can also use it with ORT in your inference sessions to get the advantage of Onyx runtime across your deployments. So switching gears a bit. What we talked about earlier was how do I make it easier for developing these models from the part of the steps required to train a model? But how do we do it mindfully to make sure that it's also as seamless for our developers and engineers and scientists to integrate and make responsible AI part of their workflows? So RAI was not a reactive approach for Microsoft. We have been very mindful and proactive in our approach when it comes to building products that empower every person and every organization on the planet to achieve more. We started our RAI journey around 2016. There is a slate article from Satya Nadella which kind of spearheaded this whole synergy within our organization. And since then within Microsoft we have adopted various principles. So firstly we defined these responsible AI principles. What does it mean? What do we need to do? And then we established standards around these RAI principles and then built products that not only are practices within internally when we build models to make sure that these are applied and used. So for example, when OpenAI gives us a new model one of the teams that first gets their hand on these models is the RAI team within Microsoft. And then we also make sure that some of these learnings that we have as we build these models are available for developers to use as they are building their own models. And so we built integration with Azure ML for RAI dashboards and we keep improving upon it as we learn and do more. So there are six basic RAI principles that are the foundation of our RAI governance framework. These six principles serve as a guide for the development of AI technology. Now we take these six principles and we break them into multiple goals. And each goal has a series of requirement that sets out the procedural steps that must be taken and mapped to the tools and practices that we have available. And multiple facets of implementation, including training, tools and testing have been developed around these. These six principles, accountability, transparency, making sure that the models we build are fair. They are not biased towards one cohort. They are reliable and safe. They do not introduce vulnerabilities. They comply by privacy and security standards and they are inclusive. So these six pillars are built across and molded in our enterprise risk management framework and ensure the operationalization of responsible AI at Microsoft. We've already made some of our early work available as open source toolkits like Counterfeit, Econ ML and Hacks toolkit. And we continue to now integrate that more and more within Measure ML. So one of our toolbox's newest addition is Responsible AI Mitigations Library, which you can look up. And there's also Responsible AI Tracker, which helps visualize. So what are the steps required as we as developers go through our training cycle? Firstly, we need to identify where our models might not be doing things as intended. So if we can specify and identify cohorts which have a high error rate, while our overall model might be accurate, it might not be working well in predictions in a fair way for a specific cohort, like for job applications or for scenarios like giving loans or housing, et cetera. So how do we figure out? So there's error analysis tools and fairness assessment tools that are embedded within the RAI framework and it's a single pane approach which makes it much, much easy for developers to test these out and pick cohort, et cetera, and do the analysis. Once you've identified these issues, you need diagnostic capabilities and mitigation capabilities. We provide model interpretability and counterfactual analysis enabled within RAI dashboard as well as certain mitigation steps. And then you then need to make a decision to address these. You can understand the causal impact of your features or do counterfactual analysis, improve your model and then compare so that the issues that we identified earlier are they still occurring or not? So the cyclical approach can help make sure that you are improving your models and making sure that your models are being fair and doing the right thing. So like I mentioned earlier, there are these pillars of responsible development like fairness and explainability. There's automated pipelines, workflows like a YAML powered CLI experience that you can use. You can also customize which components to utilize to tailor to your unique needs and scenarios. And there's also a no code experience offering users an end-to-end experience to generate their responsible AI dashboard. And once you have run all of this analysis and you want to make sure that you're able to share that with both the technical and non-technical business stakeholders, there is an easy way of creating a PDF scorecard that ensures that everyone is on the same page when deploying your AI systems. It's a single pane of glass experience which brings together a robust set of responsible AI tools including the already previewed model explanation and fairness metric. Okay, I think I spoke too fast but that brings me to the end of my slide. If you want to know more and these advancements that I talked about are some of it, there's much more that's coming up. So there's Microsoft Build May 23rd to 25th. You can know about new practices in RAI. You can learn more about what's happening with ACPT or the 10 labs. These are some of the sessions. Please do sign in. And thank you Pytorch and lovely to be part of this community. We'll do more together. Thank you.


Hi YouTube family, are you ready for this? We are gonna fly through these products, but I wanted to do this video because of the concept that Jen loves reviews, did. She did 20 products from 20 brands in under 20 minutes, and I thought I would tweak that just a little bit to 10 products from 10 brands under $10 in under 10 minutes. I have mostly drugstore and the drugstore stuff that I do keep I absolutely love, so I could probably do more of these 10, 10 under 10 things for you guys, so let me know if that's something you want. The first thing that I'm gonna talk about is one that my good friend, Sherry from Graceful Beauty, turned us all on to with her video, and that is the Revlon Color Stay Prep and Protect Primer, but it has a broad spectrum SPF of 30 for such a fantastic primer, you guys. It kind of can leave you looking a little bit white, but it seems to melt in quickly after that as it goes into your skincare, and you just let it sink in for a few minutes before you go ahead and put on your foundation. But I love that it's a little bit hydrating. I love that it has the protection in it, but more than anything, I love how it smooths out my face and how it just looks absolutely beautiful, so I would definitely recommend that to you. The next thing that I wanna talk to you about is something that has become a real staple for me just recently because of my mature skin and how dry my climate is, and it's the ColourPop Super Shock Blushes. You guys, they have so many colors in these, and they are so easy to work with.  You get it on your cheeks. I didn't do a dark enough swatch for you guys.  It lasts on me all day long. I don't have a problem with it at all, and I have so many colors of this, but this is my new favorite, and it's because it's a pink that just goes so good on my complexion because I lean neutral to cool, as you guys might already know. This is really good, and this is prenup from ColourPop in the Super Shock Blushes. This is a product that I've been talking about ever since it came out. I got it the minute it came out, and I have not stopped using it since. This is the Catrice Liquid Camouflage Under Eye Primer. It really is something that you might want to try if you suffer with a lot of crepiness underneath your eyes. I use this right before I put on my color corrector, and then I put on top of it my concealer, and you guys, it just helps give so much moisture to that area, and makes my under eyes look so much better than they do without it. So this is a product that I highly recommend, and I love that it was so inexpensive and could do such great things. Also, if you haven't tried the Milani Prep Set and Glow, this one is so comparable to the Hourglass Ambient Light powders that I think this is one that you should just pick up and give a try. I just use this as a finishing powder. I don't use it as a setting powder, although it says that you can. I find that if I use it as a setting powder, I get to looking a little bit too greasy, but wow, you guys, wow, wow, wow. The difference this makes on my skin and giving it life and glow and just a prettiness, kind of alternate between this one and the Hourglass, but I actually think I like this a little bit better just because of how soft it is compared to those. Those can be just a little bit stiff. This is not at all. So you might wanna just start with a light touch in the beginning, but I think you're really gonna enjoy that. Another powder, now this is a setting powder. The other one was a finishing powder, but to me, this Maybelline Fit Me powder is fantastic, besides the fact that you get a gargantuan amount in there. I love that they do give you different shades. I have fair to light. I really like the locking cap on this. I love the way that it looks kind of luxe. It's just, it's a nice product, and I'll tell you guys that I reach for this way more than I reach for my Laura Mercier. I love Laura Mercier, and I think it's a fantastic powder, but I just like this, it almost gives just a tiny bit more coverage and a little bit more blurring, and so this is just, for me, it's really, really good, and I really love it. And then from Cody, if you guys have been watching me and my channel for the past couple months, you know that I discovered this on a just spree that I was going on at Walmart, just walking up and down the make-up aisles like we probably all do, but I found this Cody Airspun highlighter, and there was like no turning back for me, you guys, once I found this. This is called Snowmuch Eyes, and this is probably the most intense highlighter that I own. So on the days when I am just like really want something that's gonna really stand out and really be glowing and beautiful, that's the one. The other thing is that it is so micro fine like the powder that Cody has. It's not gonna show up texture, but be careful with it. Like I said, it has an intense amount of shine to it. It's definitely something that you could look up for under $10 and be so happy with. All right, I have from Essence some lip glosses, you guys, and I just think these lip glosses are so good. These are called the Plumping Nudes, and I have three different colors here, and I will go ahead and list them in the order that I'm gonna swatch them because I don't have my glasses right here with me at the moment, but these are a really nice formula in that they give you enough opaqueness that you're gonna be able to wear this and not have to wear a lipstick under it, but because it's not super heavy, not super sticky, you could wear it over top of a lipstick if you decided to. I think this is such a comfortable formula. It lasts quite a while. Now, as you guys know, I don't have to tell you this, it's hard to find a gloss that lasts a very long time, but these do the trick. And I think, I wanna say that these are only $4 and they have like six or seven colors. And you guys, they're just a beautiful formula, so I really enjoy the colors that they've put on there because they're very wearable colors as well. I am absolutely in love with the LA Girl Pro colors Foundation Mixing Mediums. They have four different colors of these. They have white, they have the blue, they have orange and they have yellow. Now, I got the blue because I was finding that so many of my foundations were on the orange side, even the very fair ones. They were too orange for me. My skin coloring leans towards the cool side, kind of neutral, so in between those two. So if I get orange on me, I'm gonna look really bad and if it oxidizes even a little bit, I'm gonna look even worse. So finding this and using it and playing with it, I do have a whole entire video on mix-ins into your foundation and how to find the perfect foundation for you, which I'll make sure that I do link below. But I am in love with this, you guys. It just does a wonderful job of mixing into any foundation and making it a cool foundation or a neutral foundation depending on how much you put in there and the other thing is it doesn't seem to change the consistency of your foundation at all.  Okay, you guys, so this is a bonehead move if you've ever seen one before. In the middle of this video, not only did some of it get corrupted, but I accidentally erased some of it. So the new me is going to be presenting you with the next two products, which the first one is the ELF contour palette. I freaking love this palette because you guys not only does it have a very cool tone contour, which I think is fantastic for doing any contour, you want to do a shadow color and I love that about it. It does a really good job. It also has a really nice brighten me up color almost, well, for me and my fair complexion, I should say, for doing bronzer. I really liked that one. I had that one on today too, which is so pretty for just brightening up my face just a little bit. But also you guys, these two products right here, this one looks like a creamy, like almost a banana one. And then this one, and then this one just looks like your basic white. But both of those are so creamy and so nice that if you have a hard time setting your under eyes, you could very, very lightly go into that, tap your brush off really well and just barely touch your under eyes and it's not going to make it look so dry like it sucks the life out of it. So this one right here, I do love this little palette and it's only eight bucks. You can't beat that. The last one that I have to share with you guys is something that I had never seen before. I've heard about this company before, Lottie London. But I did not know that they were making these little nine pan eyeshadow palettes. This one is called the Mobs. They have the rusts and I think the rose and I want to get them both. Now this is under $10 and these are a very, very nice formula. The mattes in here are creamy, buttery. They all just blend so well together. They're so nice. But then you get these pops of all of these shimmer colors and they are gorgeous you guys. And I definitely think for building up, I think the color payoff is there. So if this color palette doesn't really appeal to you, try one of the other ones. I don't think that the formula is going to change for them and I'm going to be picking up the other two colors because I think this is so pretty at such a great price. And yeah, that's another one that I really love and would recommend under 10 from the drugstore. Now back to the old me. That is it for the 10 products that are under $10 from 10 different brands. I hope that you guys did enjoy this video. Thank you so much for being with me today, you guys. Take care of yourselves. Love you much and I'll see you in my next video. Bye guys.