25-06-2021_mixdown.mp3-from OneDrive

[00:00:05] Oh, yeah, what's up, everybody? Welcome. Welcome to the artist of Data Science. Happy Hour. It is Friday, June 25th, 2021. Superexcited have all the guys here. Everybody welcome. Hope you got a chance to tune into the episode of Released today. Talk to the one and only the great and powerful Ken Gee. It's an episode I've been waiting to release for quite some time, so I was happy to finally get him onto the show. So for those of you guys that are tuning in on YouTube, on where else twitch on LinkedIn. You guys are more than welcome to join us right here in The Situation Room. The link is right there in the description. I'm excited for this weekend. It's going to be a amazing weekend I get to spend. Both weekend mornings with my friend who happens to be here in the I'm excited for Vince cause then look, man, tell us a little bit about this quartet you got going on, because I, for one, am super, super excited about this.

[00:01:10] Yeah, I love this class. So this is something that I've been teaching off and on for three years. But for the first time last month, it offered it out to see if anyone actually wanted to take it outside of corporate America. And yes, apparently it's fairly popular. And so this whole class is about strategy, but not business strategy, the way Grandpa Horder taught business strategy. This is business strategy specifically for do scientists and people in our field and talking about how artificial intelligence has changed strategy planning, its changed business models, its changed operating models, it has that word disruption gets thrown around a lot. But what does that mean? What does it mean to disrupt the strategy of competitors? What does it mean to enter markets laterally or [00:02:00] even kind of parachute into markets like Amazon has done in some cases? And Apple's done in some cases. And so the entire class is exploring all of the implications of A.I. and it's aimed at teaching Data scientists how you're basically at the middle of this. You're in the center of this strategic transformation that businesses either go through or they die. And so the whole class is aimed at teaching Data scientists, anyone in the new science machine learning field, how you can actually implement strategy and how artificial intelligence, machine learning, Data science, your role, how you are a strategic role. And then how do you then know that you're a strategic player? How do you implement due to science? How do you actually boots on the ground, get it to work, get it functioning in the organization and get excited? Because I know a lot of people that are in this class this weekend and this can be a good one.

[00:02:59] Yeah, man, I'm super pumped for this is going to be really, really, really beneficial, I think, for me at this point in my career. Is it targeted to people who are mid career people who are find themselves in leadership roles? Like who kind of the intended audience for this course I've got going on?

[00:03:16] Originally when I created it, it was strategy consultants. So these were folks that were in the business strategy world and their pure business strategy consultants and after did one about three and a half months ago. And someone approached me after the class and said, wouldn't this be better if we had some Data scientists in there? And so I pivoted the class a little bit. But it's still heavily focused on the strategy side of the House. But now it's speaking directly to Data scientists at mid to late stages in their career, as well as people that are in leadership. But it's really, like I said, it's that kind of trying to drag you into the strategy world.

[00:03:54] Yeah, man, I'm really, really excited for this. Hopefully I get a chance to get to [00:04:00] join, I don't know if the only spots left this weekend then, but go ahead and go ahead and drop a link right here in this chat and I'll make sure I spread it on the charts for LinkedIn YouTube and shit everybody else. Man Superexcited. Have you guys here again? Hopefully you guys got a chance to tune into the episode that I did with Kanji today. Next week I had an awesome episode releasing with Dr. Jordan Ellenberg. He wrote the book Shape. So this book right here shape. He is generous enough to give me two copies. So one copy. I'll be given away next week right here on this show. Interestingly enough, this is a book about geometry. And I was watching one of those videos the other day. He's talking about how important geometry is to today's science machine learning. So if you want your chance to win this book, make sure you join us. And, you know, I'll figure out some way to pick a randomly selected winner. I'm good at doing random things. All right, guys, super excited to have all you guys here, so. Man, I was thinking, like, you know, that I'm seeing all these people graduating from from high school around here, either doing their their grad pictures and the, you know, lawns and stuff to drive their grad party. It's been 20 years since I graduated high school. I holy shit, man, 20 fucking years since I graduated high school. And my career has not been like the rosiest career. And I think about like, you know, when I when I hear people like Vince talk or Joe talk and they talk about how they had such strong, like guidance and leadership in the form of good managers and mentors and stuff like that. So for those of you who have had just amazing bosses that or mentors or guys that have helped you up until this point to career, how different do you think your career would have turned out? I guess to start with a vendor, let's go to Mikiko.

[00:05:50] I think, you know, I talk about great mentors because I don't want to talk about all the terrible ones I had, I had some trash managers, like some straight up trash managers. And I really hope they're [00:06:00] not watching because at one point in my career, I was a trash man. I mean, a lot of us as leaders don't get the kind of training you need before you dropped into a technical leadership role. And I did. So, you know, it's not their fault. I'm not jumping all over them. But, yeah, I had leaders and they jaded me pretty heavily, but I had enough good mentors that were able to kind of pull me out of some of the bad situations that I was in and develop my talent. They saw something in me that was worth developing. So they put the time in to me. And I'll be honest, without the good ones, I wouldn't be where I am right now, but also without the bad ones, without the absolute trash that I had to work for, I wouldn't be where I am now either. I've had some managers that, through being horrible at their job, taught me how to be an amazing leader. I've had a lot of education from both sides. And so while they're toxic jobs, at the same time, while in the toxic situation, I was able to learn a whole lot and find people who are willing to mentor me, who are willing to also grab me and pull me out of that situation. So, I mean, that's for me. I've had both sides of my career. Wouldn't be the same if I didn't have good and bad.

[00:07:16] Yeah, I had an absolute love that because I was reflecting on my career and I have had to go a lot of this shit alone. Like I mean, I've had a couple of bosses at a couple of jobs, but for the most part they were not that great of bosses. And the times where I did, I just spent a lot of time with no manager having to leave myself. And I guess maybe that's one reason I'm just kind of disciplined at getting shit done. But like the importance of having good managers, good bosses, I think can't be understated. Mikiko, let's talk about you. What do you think it would be like if you hadn't had, you know, talk about the good mentors and the good, good bosses that we've had?

[00:07:53] Kirrily wouldn't exist, that's just like straight up the answer, because I think. So [00:08:00] something that one of my mentors actually and mentor of at least 10 plus years, right, because he initially started as my fencing coach in high school for a very poor public school, which God knows why we even had something so hoity toity as fencing. But he was like an assistant volunteer coach there and like temples and something that kind of like was struggling to explain to my parents, like my career moves because they are much older. And they came from a generation where you were the company man or woman. Actually, they came from generation of company man. There was no company woman. So that's how much older they are. And trying to explain to them, like, what is even machine learning when to be honest, even among us in a community, we can't always seem to agree or come to a consensus of what that is. And I try to explain it to my parents. And he was saying that part of the frustration of people who are outside the tech field when they look in is that there is language. There's this capital. That is used to describe just all the work that we do and a lot of times when people don't have access to the capital, it becomes very, very hard to break into tech. You could be the smartest person in the world. But if you do not have those connections, if you don't have the language, if you don't talk the talk, I mean, so many of my friends, right. They can tell you about how they have to code switch. It's a real thing. It's a real blocker. And so if I didn't have, like, my mentor to not only tell me about tech, but to help guide me in terms of how someone can kind of bootstrap their career, I would not have a career because my own family did not have the capital to help guide me in that.

[00:09:49] So it just super important in a lot of times. I think some people think mentors are like they have to give you the advice for your career. No, no, no, no. That's not what they're there for. Right. They're not always there to give you personalized. Like, how [00:10:00] do you interview for a normal job, a military job at Amazon? Well, they don't have an engineering like 20, 30 years ago necessarily in that specific form. But when my mentor gave me was, first off, a moral code, you know, what are the things that you do or do not do, like as a professional, how do you ensure that how you do your work is in line to your values? And the most important thing my mentor told me was that look you showed me was that you can live the life that others give you. Either you adopt it because of the stereotypes or the biases that society has about you. You can leave that life. You can kind of like go with the stream or you can have the Data Cohen is the musician or you can have the grit to come up with your own destiny. And so those are really important. If I didn't if I didn't have that mentor and two or three others and really good mentors, I can count on one hand. But they were amazing in terms of the impact. So, yeah, I wouldn't even have had a career without that mentor.

[00:11:06] So we love it. And this is a bit in contrasting, last week we opened up talking about our bad bosses and that was an interesting discussion. If anybody has questions in the chat here or on LinkedIn YouTube twitch, wherever you may be watching this. Let me know right there in the chat. I will add you to the queue. Yeah, I mean, Mark, have you had. I guess so. So the question that we that we were just, you know, kind of warming up here, talking about with and if anybody wants to chime in, please do. It's like, you know, I was reflecting back on my career. It's been 20 years since I graduated high school. I have had to go much of this journey alone. I've not had the few times that I actually did have bosses that I directly related to their horrible. And I just keep finding myself in positions where I don't have bosses and I'm just like doing shit on my own. But I'm wondering how different your career do you think your career would have turned out if you didn't have those awesome bosses, awesome managers, [00:12:00] mentors, things like that? I will go to a mark then, Greg. Then if anybody else wants to chime in, let me know also if you guys have questions right there in the chat, let me know as how you get at.

[00:12:10] Yeah, so I think for me, my my current manager's exceptional, she she's has both the technical background so I can talk kind of the technical concepts while also has a background in psychology and and workforce and how to be effective manager. And also, like our product at our company, is geared towards culture and building better work habits. And so it kind of got really lucky in that is just a whole system designed to have really effective managers. But more importantly, like where my career would be, I think it came at a critical moment because at my last role I had a really bad manager that really made me question whether or not I was good enough to be a data scientist, democracy kind of work. But like somebody put in the work and just not cut out for it, you know, it's like is it worth the effort to really get to that point by coming to this role? May realize, oh, actually, it wasn't me. It was a combination of forces and it didn't have a good manager to help to recommend our early states. And so now that I have this exceptional manager, you know, I'm able to think more critically. What does my career look like moving forward? As a data scientist, I'm able to have effective conversations of, all right, here's where I'm currently at.

[00:13:31] Here's what I'm trying to go here, the key steps and get to, I think, a key thing that that really direct a lot of my work. Recently, I had a conversation, my manager, she's like, look, you're doing great on the technical side. Like, you're probably one of the better ones on our team currently, but you should actually focus less on that. The key growth area for you is going to be on the business side of really connecting those technical skills to business. And I was like a quick I'm moment because I really haven't been able to have those conversations [00:14:00] with anyone beyond this amazing group, but more so tied directly to to my actual career at the moment. So just having an amazing manager that knows how to work with people and have the technical side has been a game changer for me. And it's really 10x my my current Data science career. And I feel like I didn't have that. I don't think I would be at the current level I'm at or nor have the the dreams I currently have where I want to go with Data scientists.

[00:14:30] Thank you very much. My shout out to the community members in the building. Russell, what's up? Actually, what's up Spencer? We got we got my entire bottom. This Matt, Matt Blazer, Matt Sharp, Matt Diamond and then dear George George, I know you sent me a message on LinkedIn. So happy to get your question after this. But let's hear from from Greg and then everybody else. Wherever you're watching this, if you have questions, let me know what you to the queue go for. Greg.

[00:14:58] Yeah. So I definitely enjoyed listening to pretty much everyone here about how great a mentor is. It's it's true. We all need that. We all need the the the role models in our lives to be inspired. And I've been lucky enough to have those folks coming out come in and out and help me from the first person would give me a chance to start my first job knowing that I was on a work permit and that I feel grateful for that. But at the same time, I was I saw my first manager gave me that first chance as a mentor. But at the same time, that same mentor told me something that ran me away from the company without knowing he was doing so. That actually helped me go beyond to where I am today. So I tried to change my mindset to a different thing, meaning a mentor could be anyone, anyone that's already doing [00:16:00] something that you want to do. It could be anyone that's younger than you. It could be older. You just have to set your mindset for pulling the right stuff that gets you to the next level from that person. A mentor doesn't have to know he or she's a mentor. And that's how I look at it when I get inspired by people out there. If I tell you, Barbi, for example, I saw someone who's in his 60s, who was in the sixties, early sixties.

[00:16:31] Like kill himself to master the tool, and I was like, look at me, I'm in the. Early stage of my career and look at this through here, I'm comfortable with Excel in this tool is coming to me. I need to learn it. If he can find so much energy to learn how to use this, to plug in multiple data sources into something that tells a story and he feels so passionate about it, that inspiration for me all of a sudden this guy became my mentor without him knowing it. So is the ability to me to distinguish or surfaced of folks that can help you push through the next stage of your life? To me, is is is what gets you to the next step and being able to appreciate that sometimes and going back to that person and saying thank you, this is what you did for me without you not even knowing it. So don't try not to see that. You know, having a mentor is a finite thing, it's a continuous thing, you could come tomorrow and find somebody new in your life that inspires you to go to the next direction. And I can say I've been grateful to to find those folks around me on a daily basis that got me to where I am today.

[00:17:47] So, yeah, I love it, Greg. Thank you so much. Also, hopefully you guys got a chance to tune in to the Data Community Content Creators Award. It was earlier this week on Tuesday. It's [00:18:00] also live on dedicated Web page. Greg was a speaker there. He did an awesome job. Also special shout out to a lot of our community members here. Mr. Ericson's Eric Sims is voted by the Data community as their favorite LinkedIn personality, which I can love. That is awesome. Congratulations, Eric. Eric, you put some awesome stuff here in the chat, so don't you go ahead and tell us a little bit about that. Also, everybody else, if you have questions, you got to let me know so I can add you, too. Q So go for Eric.

[00:18:34] Yeah, I was just going to say that I think it's also important to recognize what a good manager, a mentor looks like for you specifically. I took me a few jobs to kind of figure that out and I realized that I personally like hands off managers. Part of the reason I really like my current manager is, you know, they just give you space. And if there's a problem, like ask for help, otherwise you're going to just go for it. And I really like that because I like to work on my own and experiment and break things and fix them and that kind of thing. And then there are other people, like my wife, for example, she's really collaborative. And so if a manager is like here, I'll talk to you in a few days, it's just like I'm alone on an island and it just feels terrible. And a lot of people are collaborative learners versus independent learners. And so, you know, different strokes for different folks is kind of the thing that came to mind. And so I think it's important to realize that what works for me may not work for you. And that's fine.

[00:19:35] Excellent point, thank you so much, Eric. Also shout out to Eric Batonga in the building because one of two people who helped tally up the votes independently, we had Eric and Juraj Parmar from the community who helped me make sure that the counts were independently validated by two third parties there. George. My friend, you hit me up on LinkedIn, so [00:20:00] I'll go ahead and turn the floor over to you. Looks like we're having some audio issues there with their daughter. It sounds like there's some severe audio issues there, my friend. Just go ahead. Tipler right in the chat and there's a few questions. What's up, Greg? Matt Diamond. How you guys doing? Gapper? Greg, you got a question or

[00:20:21] I'll come up with my question later.

[00:20:23] Ok, cool. Yeah. So, um, Lee Anthony, how's it going? Clint, what's up? I see you guys here, so. Yeah, man, that was a good discussion so far. I mean, if you guys got any questions or any other topics of discussion, because I can just make stuff up top of my head that'll

[00:20:41] Sound like a small as like a really small daily day to day kind of thing. So I am currently trying to figure my way around, you know, five hundred tables or so and figure out, you know, how they are related and in meaningful ways. Right. And I have not so far been able to find like a like a good schema diagram. That doesn't mean it doesn't exist, just means I have to find it yet. But I was wondering if anybody knows of any kind of tool or resource or anything where otherwise I was just considering coding something up that will basically use what I know of the system to take the table name and the keys that I can see and then map me to different tables so that I can just like, you know, make something for myself as a reference. But if anybody knows of any sort of tool I can use or even the right words, I think it's like a schema diagram. But anything practical would be super helpful. Save me some time. Well, the help us out a little bit. Describe what it's going to look like when it's doing. The thing that I think about is like box of table, line to other table showing which keys connect to what other tables. And then from there I could add notes like this table tells you this and this table tells you this and that sort of thing. [00:22:00]

[00:22:00] Yardie entity relationship diagram

[00:22:03] Things more

[00:22:06] And more. Go for it. Oh sorry. Li Li or are you going to guys

[00:22:10] Is it more of a map kind of flow chart kind of a, b, c kind of thing. I mean it could be A to B to C it could just be you know, like this table is like a fact table and has like these dimension tables that like relate to it or something. And I just, you know, in my mind it's like easy PhD dotcom, you know, that kind of some kind of a tool or something. Well, if it was just a map, Google has a little mind map program. It's a template kind of thing. You can put like a little bubble and then it lets you draw an arrow to the next bubble and then to the next bubble. If that holds, that's just a quick, dirty tool to just give you a point of reference. It's built into Google sheets, so you can do that pretty easily.

[00:22:57] So just to clarify, are you looking for something where you just say, OK, here are all my tables, and then it'll just find the relationship for you between, like, primary keys weren't keys and spit out? That would be that would be awesome. I, I'd love that. But Mark, go for it.

[00:23:12] I just went through this process for the start up and I know exactly that. We do not have that resource. So I was the one to create it. And so what was the thing you're looking for is called the entity relationship that. But our brief was mentioning. And then also I think I think the right terminology is like there's different weights connected. There's like crow's feet notation as well, which provides information like that directionality and where the primary keys. And then also another key turns going to look up as primary key and then primary deposit key. So like for your table is essentially one specific value that that connects as a primary key or there's a primary deposit key words like multiple value. So like for me, you'll be like a user I.D. [00:24:00] or ID and then date someone making something up. And again, that's like the unique value will be like a unique set of values to tell you it's a distinct role. So those are the key things. I would look for a tool I used for this is chart, but you can essentially look up, look at yada yada. I think the big challenges, especially with your center working and company Data, is just getting it approved by security. So if you don't have one already, just ask security. Hey, is this a tool I can use for free? There's certain free tools. Hopefully you're not using query. I use big query and unfortunately they don't allow exporting your metadata. And so all the nested values I had to do manually and it was a layer of hell trying to figure that out. Did you do that? I have a link for you if you have a query to navigate that. But for the most part, many other tools are much nicer and can export that metadata that tells you where the table values and their primary and or composite keys and how they connect. That sounds pretty sweet. I actually just barely became even aware of the concept of a primary composite key recently. So that's definitely something to look into.

[00:25:16] Mikiko, go for it. And we also have Data engineers in our midst somewhere. Where is Matt Sharp? And I think that of data governance. But that might be helpful. Orbin But Mikiko, go for it.

[00:25:30] Yeah, I mean, I'm always a fan of the lazy way, so depending on what speculatory use Dvir and Raiser's people, I think both have an option to generate diagrams. A creative option, I guess, is you could I mean, Eric, you were doing some work with, like network graphing libraries, right? So that's another option to do it. But I don't know. I [00:26:00] mean, my two cents, I don't know. You would need to map all the tables or if you do, you still probably want to pass it by like Data engineering or Data governments or whoever is kind of responsible for that. The nice thing with some tools like DB or for example, if the engineers have like annotated the columns, you'll actually see the you'll see the comments like on the columns. And sometimes they describe like they actually define like what is this column. Right. But it's one of these weird things where you're working with the spaces. They're kind of like these weird living artifacts were probably like a bunch of the tables are just going to be used for like transactional purposes. So there isn't really any deeper meaning behind it. But some of the other tables, which are more like the aggregate, the tables, that's we're kind of the real sort of defining or conversations need to happen just because you can have like a couple of different ways to define metrics. But I would look at first, like out of the box stuff, like on a scale editor. And then I would consider like, how many tables? Well, essentially, what information would you actually need from this?

[00:27:10] You're totally right.

[00:27:13] It depends like I think it's I think it's really super useful, especially for me, like I'm going through that right now, where, like being new to the job, I have to kind of map all the different, like Data flows and scale transformations and all that. So it's a really good way to understand what is under the hood to get like, I think the real real definition. It's always good to talk to that. The engineering and the business teams.

[00:27:41] Well, that's that's good.

[00:27:43] Speaking of Data engineers, we can get some insight here from one. Match up. Go for it.

[00:27:52] I mean, I don't really have anything much more to say than what's already been said, so awesome.

[00:27:59] Well, that [00:28:00] the some good resources there and some good, you know, somebody in LinkedIn says BCO as well, if you want to draw that out. Thank you, Jim. On on LinkedIn. Is that some satisfactory results for you there?

[00:28:15] Yeah, I think it's helpful, like, you know, like Mikiko saying, I think that 80 percent of the work account for 20 percent of tables. And so for me, I can probably do it with 20 or 30 tables or so. I don't necessarily need all of them. But it's like finding also then what's in each of those tables that's going to be meaningful. It's like, oh, my gosh, that column was there the whole time. I didn't even realize, you know, like that. Definitely had a few of those moments in the past week or so.

[00:28:42] When I started my current job, it was kind of like that, like there's a bunch of tables with really uninformative names. And I had to go around and I think I talk like six or seven people to finally figure out what tables it was that I actually needed and then what each individual caller meant and then how everything was connected. It took a lot of groundwork. And I think I mean, that that kind of just speaks to maturity, Data maturity of a company which Mark was talking about in a post earlier today. But Mark, also you have your hand up to go for it.

[00:29:11] There's one more thing is, like many, many times, these these diagrams are static. And so they may have changed since the time they were made. So I think one of the more important things you can do is identify who generated the Data or is this something that your company has generated or somewhere you're going from somewhere else, because those would be the key stakeholders to confirm thing, especially when you use it in school and you find some funny business.

[00:29:40] Thank you very much, Mark. Yes, I mean, this kind of was the Data maturity, like a tour company to put them on blast on LinkedIn for everyone to hear.

[00:29:50] Yeah, well, I mean, from my perspective, it's great. Like I have like I think I trust the things that I see, because usually when something is wrong, it's because I did [00:30:00] it wrong. And so and I can usually find the correct number by triangulating elsewhere and things like that. And and to the credit of whoever had the idea to set our tables up, like the way that they have named things, there's a lot of really good consistency, both across table names as well as like it become. It's pretty intuitive to find the keys. Like once you if you see a column in a table, you can easily figure out what table it's related to. And so I think I probably have it better than many people in that respect. But I'm just trying to figure out how do I, you know, flatten the learning curve for me is to find finding those, like, key tables and then also make it easy for the next person who comes in to be like, these are the one or two dozen tables I need to pay attention to first and then can grow out from there. So unfortunately, I don't think I'm I don't think I'm at the place of where, you know, Mark sitting of having to do everything from the ground up, basically, but happy to learn from anybody's experience. So thank you.

[00:31:04] Awesome. I thank you have Marcus posted a great paper here on Data maturity that's interesting. Never got this topic of Data maturity because like I'm doing this, I work currently. I'm doing the current state assessment. And, um. Like, it's like, oh, my God, it's like, am I doing this right, what am I what framework? Supposedly some using the that I think is called Data Dam, a dam. Adamah, using their framework levels, know to level five. I mean, at the time being spent on it's kind of hard to kind of communicate the value or ahli of doing this current state assessment, so maybe then maybe that's something you can help us understand. What are some words we can put into presentations specifically about why it's important I should be spending time with the current state assessment.

[00:31:51] Well, let's give you a general answer, and it's actually something that you'll hear we babble about a lot tomorrow and on Sunday, but it's complexity. One of the biggest things [00:32:00] you want to do just at any stage is reduce the complexity of the business. And so any time that you have a data set that doesn't make any sense to somebody who's coming in onboarding, that's complexity. So onboarding now takes longer. It takes longer to scale the business. Any time you have worst practices used in in your Data just Data governance in general, again, you have all sorts of complexity. Anybody that needs to use that Data, especially if it's for model development, you don't know if you can trust it. You don't even know if you're using the right Data. The other columns are labeled nicely, but they do actually can do what they claim to. And that's that's why I do what I'm talking to senior leadership and I'm trying to get some cash and get a couple of people hired and have their full time job. Just be clean up one of many different types of train wrecks that I can run into. That's usually what I'll say is, look, this is adding complexity and complexity is slowing down the scale that your expansion desires have. So we're slowing that down. We could be going faster than you're always worried as an executive. Are you going fast enough? And you're also looking at something that's going to decrease the quality of anything downstream. So now you have scale slowdowns, you have quality issues, and those are the kinds of things that you can talk about, just anything that they're scared of and skill and quality or to just easy ones that you can always use.

[00:33:26] Awesome, thank you very much, Ben. My pleasure. If you're around, I'd like to ask you a question about Data governance map and know a thing or two about Data governance. I'm wondering, like, how do we because we talk about Data governance. People like just that word. Governance, I think gives people, you know, bad taste in the mouth. Like we talk about Data definitely we want Data freedom. How do you communicate the value of Data governance to people who are, you know, maybe have Seneschal [00:34:00] feeling like I'm talking about,

[00:34:02] Hey, I kind of got an emergency going on,

[00:34:05] Right? Hey, we'll handle handle it. And whether I need you on the podcast, talk about that,

[00:34:11] That's for sure. Some time I won't be able to join.

[00:34:17] Have fun, man. Awesome. Everybody else got questions on anything. Please go ahead. Let me know. I can actually see the Q check in on LinkedIn and YouTube. Don't see anything there. So there has a question he he actually typed out here so I'll read it. Data signs the machine learning is relatively new in my country. I really want to be one of the go to persons in my nation and highly sought after. I want to push myself there as an authority. Can you advise which route I could take? Also, what service or assistance can I give to organizations to help them get value even if it's not being paid for yet? It's a good question. One thing to do is to start start talking about it right. Start making yourself available for presentations in groups like that and activities like that. Sorry, but yeah, I'd like to, I'd like to hear anybody else here thinks, um, let's say let's go to Vienna because OGE in this space and I've become and become a leader in this,

[00:35:22] It's hard to create a category. That's what you're doing. Yeah. It's been created in other countries. So you have precedent. There's interest in other countries. So there's definitely things leverage. But to become an expert to create the category and then put yourself at the top of that space, that's that's a really big challenge. And you'll need you need some sort of a source of authority and you'll need some sort of large project. Those are my to look, if you can find a company that's highly credible and in your country, specifically a company as well respected and that has some sort of [00:36:00] connection where you can say, hey, here's a use case where I can make you a ton of money with Data science, you can get them body and that's really going to make connections. You have to go into the company and its your senior leader by senior leader pitching in and putting yourself at the center of the solution to their problem and putting Data science machine learning methodologies at the center of the solution to their problem. And especially if this is something that just isn't being done by a lot of companies in your country. That's hard. That's a lot of work. And then once you get that done, once you've walked your way through, done all the selling, you have to make sure you don't get edged out of that use case because a lot of times you open the door, you create the opportunity, and then Deloitte shows or Accenture shows up and they just kind of push it aside like, oh, hey, thanks. You have to make sure that you don't end up getting kind of keened off the stage. And then from there, you have to maintain enough credit for a project that you did at a company who wants to take all the credit themselves. So you have to maintain some level of credit and that's your source of authority. That's your credibility. And then you have to advertise that like crazy. So it's hard, but it's doable.

[00:37:14] I like that. So it seems like the first step would be, like you said, find like the biggest company in the country and then find out what industry that company is in and just look for case studies or use cases, white papers, whatever, that you can find that deal specifically with that company's industry. But obviously, look for machine learning, science type of use cases and research them and just show them like, hey, this is something that you guys can be doing as well as that kind of like how that would go.

[00:37:46] It's a lot of networking. It's really any time you do a consulting relationship, it's it's relationships building or any sort of consulting agreement or consulting engagement. It's always, always, always relationships. And so [00:38:00] you're going to be building relationships in that company. Getting hired into one is actually easier. I mean, it's almost easier to create your own position in that company. You get hired into that position and then as the company gets more mature and gets to be known, was in your country as an industry leader in Data science, you kind of by association, get your name in there. And that's another way to do it. Sometimes you have to just create. Job create the need in their mind and then you ask, would you mind hiring me?

[00:38:31] Mm hmm. So do you think a good idea would also be maybe starting just like a Meetup group for Data Science Machine Learning in your company or something? In a country where people can kind of get together who have the same interest and you organize meetings, set the agenda, get people involved and use that as an opportunity to network and also build that authority and maybe even, you know, help people who are breaking into the field, help them with projects and things like that, lift them up. I think that's probably a good way to to build authority and your brand name in the space as well. Anybody else have thoughts on this? I'd love to, uh.

[00:39:07] I have some thoughts. Yeah, please. What then was saying hundred percent agree. I think there's. You know, there's always other ways to get to the top, and I guess that depends on what you mean by top. I mean. Now, part of that is maybe a popularity contest, and I mean, it wasn't that long ago where there were a lot of Data charlatans, the same people that had a really large followings but didn't necessarily know that much technically, but they were able to get the hype coming and they were really sought after because they were very popular. I mean, the other aspect is, I mean, if you really want to be released on a sought after [00:40:00] the Data space, you can you can go very technical now. So you can write a lot of white papers. You can get a lot of patents, you can do these things, you know, write books, do these things that distinguish you as a very technical leader. And that will always make you start after one. And then I guess probably the last solution for this is go become an expert in another country where it's already established, where it might be easier to grow and learn from mentors. And then when you have established yourself in another country, you can move back and you already have a lot of this experience and a lot of this. I guess cloud from maybe working somewhere else. But having already established yourself as an expert so I can recommend to go about it.

[00:41:02] Thank you very much, Mikiko. Go for it.

[00:41:06] Yeah, I guess the traditional comment so. There's there's producing value as a practitioner and then there is. Producing content, sharing it with the community. So sometimes those can cross over, but sometimes they are totally unrelated. Right. And I think these are some of the quote unquote charlatans. They do a lot of circumstantial stuff, but they actually don't provide enough. They don't really provide a lot of value. And they also sometimes don't know how to even do the jobs that they talk about. Right. So there is there is there is overlap. There is overlap. Right. And I think something that, you know, if you look at, for example, people on this call, Harpreet, then Eric, Mark, Greg, you know, the things that they have done really well, number one is they have acquired, you [00:42:00] know, what a novel called Special Knowledge. So it is knowledge that is it's not in this niche, but is well defined and it's also very valuable and they also share it. So that can be an incredibly from what I've seen so far from how their careers are developing and all that, that can be a very, very powerful method. The other thing I would sort of make comment on is so the funny thing is that I think a lot of times, like in the US, we kind of position ourselves as like a superpower and all that, which is not to say whether we are not a technology superpower. But what I observe, for example, is that in certain markets, for example, like India, South Africa, South Korea, there are certain solutions which, to be frank, American entrepreneurs would not have come up with because they are not in those markets and they would not have thought of those solutions.

[00:43:01] So, for example, mobile transportation is pretty big, not necessarily in the US, but it's really pretty big in other markets. So it's possible that you can come up with solutions or ways to provide value in your specific market that are very individual to that individual, to that market, using some of the stories or techniques that you would develop either as a Data practitioner anyway. So that's something that I think you will sort of have an edge on versus compared to like, for example, Americans. Right. So there's always that kind of opportunity or that white space for you as a practitioner in your country. But definitely sharing the developing specialized knowledge is really important, but also sharing it is also really important because what my manager used to say is she's like, you know, you got to use that. Well, she's like, you got some options. You could either be someone who doesn't know what they're talking about, but you have a big audience, which is really bad. You could be someone [00:44:00] who's an expert and you have not no audience, which it means you can still get jobs. The interview just fine, but you're not really expanding your opportunities to capitalize on it. But the best the best place to come in for is to be pretty good at your job or pretty good as a practitioner with a decent audience. And you can always build up from there on, either in terms of expertize or in terms of AIs and connections like that.

[00:44:25] Specific knowledge feels like play to me looks like work to others. Market go for it.

[00:44:29] Yeah. So I have to speaking from someone who has not figured it out yet, but is trying to figure it out and try and be like an industry expert. I don't know if I can say industry expert of my country, but it's pretty intense. And also I asked earlier if your current practitioner working towards it, I finally found your picture and it was super effective. And tell me who you are. Really cool for the little little icon. It seems like you're currently a practitioner and so similar to Let Me Go set in my current game plan of what I'm implementing right now, I'm putting in action was like the first couple of years is really gaining expertize of of Data signs and domain knowledge to get myself into a role. And then the next step was specifically seeking jobs that will push my technical skills and my ability to drive value and specifically looking for roles that allow me to put things in production that I can point to. I built that product and I can show others. Also another reason why I'm moving away from health care, because a lot of my health care work had to be behind closed doors because of security reasons. And now I'm in more public facing product company blogs where I can say I helped build that piece. The next component is my LinkedIn. I'm constantly engaged on LinkedIn. And in the past month, I've really taken it full throttle to really build up a presence and like setting specific goals to build up their presence.

[00:45:58] The reason being is that once I build [00:46:00] up a presence, my next goal is to transfer that to my own platform. Whether so like Harp has his podcast and his newsletter and all those things. Right. Just some kind of Internet right now. He's amazing, but he's able to transfer that. So he owns that is not tied to LinkedIn. And so for me, that's my next step, as I'm trying to own that on that piece. And the reason being is that by having that collection, people seem expert. People share share my resources. More importantly, the other stuff is networking. So I've been networking for a while. And by networking, I don't mean like Chinoise made people go like try to gain as many meaningful connections, because I have this whole mentality of like I'm not competing with people. I'm trying to expand the pie with people and the people I know and more to connect and expand the pie with the more opportunities that come my way. And so by building that network, I can have more opportunities to expand the pie and have access to those big piece of projects that I've been talking about where it can put you, quote unquote, on the map. And so I think to summarize, I said much better to me earlier, but get domain expertize, share that knowledge and then leverage that knowledge and and networking built to turn into opportunities. And I'm currently trying to do that. And I will get back to you in the future if I figure it out.

[00:47:30] I love him and positive some games are the really don't only the best games. I would say the only games worth playing, but they are the best games positive. Some games are the best games. Marketocracy. I actually also had a question as well. So I'll go straight into your question, by the way, everybody else listening. If you have questions, let me know in the chat. Raise your hand, whatever. I can add you to the queue. If you're listening on LinkedIn YouTube or twitch a question, let me know. Shout [00:48:00] out to Marena, who made her way into the room from the LinkedIn comments. Glad you made it here. Dave Mengel, what's up? What's up, Toschi? Haven't seen in a long time, friend. Good to see you again, Kelly. How's it going? Klint. Everybody else good to see you guys here. Go for it, Mark.

[00:48:17] Yeah, I'm super happy we have a Data engineer in the call. No pressure mat, essentially. I've been thinking a lot about Data pipelines, my job, but more so on the downstream side of our we have our database. How does it get into our data warehouse? So that part I talk to Joe quite a bit. I think I have a good enough grasp to do the job, but not be expert on the other side that our engineers are more still working on now. What do you think about our infrastructure, like our original data sources? How's that ingested into our database? And that's a whole new area that's just beyond me. And like I tried talking to them and I think they're just figuring it out, too. And we don't have a Data engineer company. And so we're all just kind of talking. And one specific question I have is, when you're doing data ingestion, is there any specific key terms to talk about making changes to a user over time and keeping track of those changes for Data ingestion? That's a very specific question, but I think it leads to a lot of downstream impacts of our Data quality. And I had some weight, some key terms to look up further and research and get back to our team. That would be amazing

[00:49:35] If we go for your

[00:49:37] Stats in

[00:49:38] Recovering data. Scientist from Salt Lake City today in place of Childress. So go for it.

[00:49:45] Yeah, I mean, I'm trying to understand exactly where you're getting after. I mean, the and so there's kind of two aspects. I think one of the things you're looking for is probably in event sourcing architecture. So [00:50:00] essentially. We're looking more at screens because you seem to be carrying a lot more about how users are changing over time. And so kind of the traditional long story of how Data has always been stored is we throw it in a database and something change with the user and we're going to update it. And so the database just kind of storing the information of the state, which is OK, but you're kind of throwing away a lot of that information and Data how things change over time. And so kind of the way the modern way to do that is to use dreams and to use what's called an event sourcing architecture, where essentially every single event that ever happens gets put into this stream and it gets it's immutable and it's logged for essentially for as long as you need it. Sometimes that's whole time. Sometimes it's not. And so then if you ever need to figure out what the state of the system is, you would just go and you would aggregate all of this event information in the source. And hopefully this is kind of giving you some some keywords to look at. But I wasn't even aware of our sourcing architecture, so that is already a big plus for me. So, I mean, this is I mean, this is why people would move to things like Kafka and why it's pretty powerful is because of then sourcing really sells a lot of problems that you see in traditional architectures.

[00:51:45] But but other than that. So it kind of depends on where your data is coming in from. And a lot of that is actually that comes from your software engineers as they're building your platform and they're building [00:52:00] an object oriented programing usually to kind of figure out, hey, this is how people can interact their website, and these are things that are coming in. And then if you want to know how things are changing and those platforms, you start adding logs and you start. Logging, how users interact with your website to kind of clarify, give you a good example, like we work with H.R., H.R. status of H.R. platform management training as someone is icy by then, later on it became a manager. But then like, I hate being a manager one year later to go back to being I see like those has huge implications that our whole products and being able to track those changes really well in a way that just doesn't have to involve jumping through hoops to figure out that would just make our lives so much easier. Yeah, no, I mean, it's a complicated problem when there's lots of different ways to go about it, but that you can kind of look at some of those things and just mentioned

[00:53:02] That sounds like a like a master Data type of issue. Right. I'm just at a high level thinking about that. Then any insight, any suggestions here to to help Mark out or Mikiko or any other engineers in the building?

[00:53:17] I'm just going to say, no, that would I would be where I would start. You can, but this is a rabbit hole. Just don't go too deep down this rabbit hole. Just do enough to get you by right now. And basically what you've been told is to go through that and stuff just to get to where you can really overengineer this. So don't do that.

[00:53:39] Thank you very much, Mark. Was that helpful or Mikiko, any insight or tidbits here for Marc?

[00:53:46] I don't think this will help. But one of my coworkers, I think a staff engineer, which Seymour, who wrote a book called Gently Down the River, that is a children's book about Kafka involving otters. It's very [00:54:00] cute. I would highly recommend reading it, not not because it will help you with this problem, but it will help you understand, which is the wibbly, the most widely used tools in the areas. Also. It may very well be the

[00:54:17] Link is right there. It is gently down the stream. I love that. I absolutely love that. Um, right. On questions. Comments from anybody else. Go ahead. Let me know right there in the chat or comment box, wherever it is that you are consuming this.

[00:54:36] Well, if you want to call me, I would make. So there's a reason why, like I will work with our Data engineering team and why I really respect the hell out of them is because they're solving problems like this a lot. And it really is complex because it's not it is a tootling question. But it it also it is also like proccess question, too. And there's also concerns about like resiliency and scalability and all that. So I would say, you know, my recognition would definitely be taught to some good engineers like Harp. Anyone else you kind of know because it is a Harp those are hard problems that even at MailChimp we have like a team that solves those kinds of problems, like I an ideology. I don't even touch that because it's just so complicated. So if you feel a little stressed out, I would say, no, it's OK. It's it's a hard problem solved. It's it's not because you're not solving it, because it is a really hard problem to solve, though. All times takes people who are like working and thinking about these kinds of problems, like for their jobs, like full time jobs. So that's just the only thing I would put out there.

[00:55:43] And also I definitely I read the book also completely recognizes and same problem to work. I was not expecting a full blown like here's the answer to it. Just more so like you just reach out and the data science team, you're like, hey, this is a proposed solution. Please check it out, have any feedback. I just want [00:56:00] to give at some type of feedback that will make a decision that makes me regret life later.

[00:56:05] Oftentimes when I come across these hard problems and I'm stuck usually because I don't have the vocabulary to go look for the answer, it is that I'm looking for. And like sessions like this, I feel like very, very helpful because somebody else might know vocabulary term that you can then go in research and see, OK, is this applicable to what it is that I'm doing? And then in researching that vocabulary term, you might come across another couple ones and then research that. And eventually, you know, within a few steps you get get to something that might work for you. But, um, that's definitely a I love solving our problems. They're fun. I love researching and going down rabbit holes and doing things. I wouldn't have it any other way. Vigne says no matter what solution they implement, you will hate your life. And then everybody started laughing. So now I know. So I mean, speaking of resourcefulness, what are some tips you can share with anybody and anybody hear the call? What are some tips you could share with with. You know, with whoever's listening, if you come across a problem and you're like shit man, like I to, I don't have any idea how to solve this, what do I do? What are some things that you do when you come across a problem like that? Let's go to let's go to Eric and then we'll move to Marine after that.

[00:57:25] I just have to take off. But hopefully I miss Eric and not Eric Gitonga, otherwise I'll just hand it off to him. But I have one minute. So you say resourcefulness for when you get stuck that essentially.

[00:57:38] Yeah. What do you do when you're stuck and don't know what to do and what kind of problem. What's your what's your first go to move.

[00:57:43] The first go to move is probably keep trying it a little bit of a different way than Google, then stack overflow and then somebody in my master's program talked about did they called it like the rule of three. So I was like, before you go ask a professor, ask [00:58:00] three other people. And that actually works really well for making connections with people who are not your professor, too, because that way I'm reaching out to this coworker and then some other coworker, and sometimes those two people will give me different answers, but maybe both of them work or whatever. And then and then before. And if they can't solve it or whatever, then it's like, OK, now I'm going to go now I'm going to go ask a manager or somebody who I could, you know, bug to get the answer. But I think it's just I think we underestimate the value of just asking, just asking normal people and asking our friends and the connection that comes from it, because we realize that we have like mutual questions or mutual suffering or whatever, and we learn together. Yeah, Eric's exactly right. I mean, when it comes down to the rule of threes, if. Well, that's cool. But no, if you can't explain to somebody else and it's still stuck in your head, you haven't really kind of gone to the real problem. So you kind of have to draw that down. And by the second time, you kind of went through that kind of rehearsed a little bit. By the third time, the charm, you know, the three legs on the table kind of thing. Yeah, you're right on track. That's that's exactly right. Eric, if you can if you can explain it to somebody else and a clear and simple, straightforward way, it's it becomes easier to solve. Until then, it's just a complex. Let's go to Mars.

[00:59:23] Yeah, I love it. What else? How about that, let's hear from I forgot who said Marina, yes, Marina.

[00:59:35] I think it's kind of like the same. It's just trying to like chunking things, right, to try to make this the problem smaller. And can you try to make it smaller? Can you ask enough questions that you make the whole thing get smaller and and then, like, talk to people? Right. So, like, the whole thing about the role of three or three hundred, whatever, you need to get there to get [01:00:00] your questions answered, but also because then you realize that maybe they were not the right questions, that you were not understanding the problem, which is, you know, many times the case or somebody come with something or a different idea. And then there are some like pops up and then you are like, oh, OK, then I can do something else. Right. And then you just also like chunks. Then you just Google it and see what else comes up. And then if you country and that I like to put that kind of like also a timeframe for things, because at some point you have to decide, OK, maybe I cannot do it, you know, like I don't know enough. And then I would have to really like, sit down with somebody. Right. Otherwise you can be in that cycle for too much time. So that's that's the other thing. So basically, you know, tanking and then asking, you know, I try to ask questions and then try to explain or to talk with somebody, because that's normally helps to clarify your thoughts. Right. Or even solve the problem. And if not, at some point you just cry for help.

[01:01:10] I love I love that that tip about chunking because I'm super stubborn and at all except my time affair. I'm like, I'm going to find the right words, like two days later and I'm still looking for the answer. Let's hear from others here from Russell and then from then on this. And just for context for everybody is tuning in. And also, Greg, Greg, we're talking about what you do when you when you're just stuck on a problem. Like you don't have the right vocabulary to even begin to understand how to go and then search for this and you don't know what to do. How do you start working on a problem when you're at that stage? It's to then wrestle and then Greg. And then if anybody else has questions, whether you are out there in LinkedIn or Twitter or YouTube or even here, let me know.

[01:01:59] The interesting [01:02:00] thing about Data science is at some point somebody is going to ask you to solve a problem that no one's ever solved before. And that's one of the cool things about our field, is you're going to get that where you Google, you go to Stack, you've done these progressions are perfect and you get to the point where you go, oh, my God, no one has done this before. And so you're basically like building stack overflow before Stack exists. I don't know how they did it, but somehow they managed to build that website without their website. And so you have to do is and this is a lot back to chunking, but you or you're breaking up the problem, you break up the problem into these are problems that have been solved before. And this is the real novel thing that I have to solve. And typically what you find is ninety five percent of this thing that you thought was crazy has never been done before. Actually, when you break the solution down, like 95 percent of it's been done. And so you can pull that solution as your starting base and then you figure out what really has never been done before. And you start with, well, why hasn't anyone done this before? Because, again, the majority of the time you realize the reason why no one's ever done this before is because you really shouldn't do it. You shouldn't do this. This isn't something that you want to do.

[01:03:14] It should probably be doing it a different way. And so that, again, eliminates a whole bunch of those old I've just been asked to do something that's never been done before. And then when you finally get one of those problems were for real, not only is it never been done before, but you really should try this thing. There's there's actually something here you have to and this is the interviewing piece. You have to start interviewing people who have tried and failed to solve this problem. That's hard. It's hard to find people who are willing to admit, I tried this. I went down this rabbit hole and I failed miserably. And they'll tell you some of the mistakes they've made, if you're lucky. And then you can avoid all those mistakes now and you're hearing how long of a process this is. And so when you finally do get a project where [01:04:00] it's like, yeah, no. One, this hasn't been done before. Number two, it should be done. You have to then go back to the people asked you to do it and say, look, this has never been done before. It's going to take a long time and cost a lot of money. Are you sure? And that's one of the interesting things about problem solving, is when you do find one of these novel problems half the time, you don't even get the opportunity to solve it. You get halfway down that road and then somebody goes, sorry,

[01:04:23] Yeah, you absolutely love that. A recently finished book, How to Solve It by George or Greg Portilla Poly or something like that. But he talks about solving problems in four different stages. So there's a question here from YouTube getting stuck constantly with the vast majority of things to cover in Data size and lack of learning structure doesn't help either. How should I practice the skills that I learned on real world projects? OK, so how could I practice the skills I learned on real world projects? I just really like this framework that set out in how to solve it. I think the keigo, you've also got that book recently, but it starts with first understanding the problem and then asking yourself a series of questions. What's the unknown? What's the Data? What are my conditions? Is it possible to satisfy the conditions or in this case, assumptions? We're thinking about machine learning algorithms, statistical techniques. Is the are the assumptions sufficient to determine the unknown, then need devise a plan, so find the connection between the Data and the unknown. Um, you know, have you seen this type of problem before or in this case, have you not seen it, which Mark wants to have an example from for the real world which will get to or have you seen the same problem in a slightly different form, you know, related problem detail, another technique that might be helpful here. Then you carry out your plan. So carry out the plan of the solution. Checking each step I. Can then each step correct, am I doing this right? Um, and then you look back, examine [01:06:00] the solution that you got. Um, so I like that book, uh, how to solve it. It was a excellent read. I highly recommend it. Um, so hopefully I'm in charge there on YouTube. That answered your question. Um, there is a question here from from, uh, Mark about a specific example from your career then. Do you think you share.

[01:06:27] Well, I missed about the last minute. I'm sorry. I was just taking up a message. No, no.

[01:06:31] So, uh, so, uh, Afkham here. Can you provide a specific example from your career, you know, problem you have worked on that hasn't been done before or something that you might be able to share just to kind of give us a example that if you're not under an NDA.

[01:06:48] Yeah. That's going to say, oh, my best work is under NDA, but try to think if there's something I could share. I can't, like, live now. I probably shouldn't, even if I could.

[01:07:02] So I've got another question here coming in from LinkedIn from my good friend Revathy, about the hazard. How's it going? Good to see you there. Just join in on the chat. What are you doing in the comments there? She wants to know, is event driven architecture an alias for LAMDA Architecture? Chagai. No clue. Um. Anybody want to take this one on Mikiko then? Anyone is event driven architecture, architecture, an alias for Lamba.

[01:07:36] I know they're not the same. I think LAMDA is one of them is a type of the other or it's a way to implement the other. But this is why we really need that Sharpstown. I'm shaming him so he can see this on video posted.

[01:07:57] Newmark's asking lamda any of or lamda in Python. [01:08:00] Are there more than one couple lamda? And of those two, but like there are more than those types of lambdas, um.

[01:08:11] Yeah, so there there is and I think I think this is a challenge because some terms like. Endangering the mission or injuring or actually overloaded because Lamphere. So so the the key phrase is posted and managed to everything, so basically what Amazon, Google and I think Microsoft does is they will typically take existing open source architecture or libraries and then they'll essentially just bring it sort of like Hosten managed it within their ecosystem. Right. So, for example, like, is Google managed kubernetes? Right. A cloud function is Google's version of like lambda functions. So it's like it's one of those things where you have the software Landow but you can also allow that's based off of like specific architectural sort or designs and specifically with like. With like LAMDA and eight of us, it's essentially based off of, like calling three functions, right, which are used to trigger other things, but that's different from the lamda, that is relevant to the event based streaming architecture that mousepox about. And this is also why all the Data engineering team, because I don't know any of this. I just I only interact with it after the Data engineers do such a great job of getting it into a place.

[01:09:45] And we think about I know about Data engineering is like I don't know if that's even like I communicated Data engineering. It's like, oh, so there's so much freakin more to it that I did not know of until I started working with a, you know, software and stuff like [01:10:00] that. LTE is also a hot phrase. Yes, it is. That's true. That's when you can build a Data like the little Data Lake house and build a Data canoe to take it to your Data marts and yell to you there. Any other questions? Let me know. If not, we'll begin to wind it down. Shout out to everybody that we did not hear from Kelly. What's up? Matt Damon, what's up? Good to see you again. Actually, I haven't. I didn't even get to hear from you in.

[01:10:33] I've been good, I started working at Shopify this month and having everything that my friends really fried after the trainings, but it's been amazing. I'll have a lot more to share in the coming weeks. And the good news is I'm getting Fridays off for the next two months. So he's going for the happy hour. So maybe on top of a mountain as long as I have networks.

[01:10:53] I love that man. I love that. Yeah. Congratulations on I. I was listening in. It must've been like a week or two ago. I was listening into one of the sessions and it was the one where you and Greg were talking about like the regulatory type of compliance type of stuff. And you're interviewing I think that was the last interview we had with with Shopify. So congratulations on landing that. That is awesome. Um. Guys, make sure you tune into the episode of Released just a couple of days ago with Kanji and a couple days ago today, this morning with Kanji as a great, great episode if you're listening to this live or if you're watching the replay on YouTube. It's still not too late to join in on VINs course this weekend, when can you give me a link so I could share it across all of these different socials?

[01:11:42] I'm looking at all the way back at the top.

[01:11:44] Ok, it's all the way back to the top. I'll go ahead and post that, guys. Definitely join in on that. And I'm excited to add to. To learn from Ben this weekend, I know he doesn't have a good time if you join as well. Um, Stefan, [01:12:00] check that out next week. Episode with. Uh, Jordan Ellenberg, I'll give away a copy of this book, this very copy, he was generous enough to send me two copies. So we've got one to give away of, you know, to men. You got to join here in the zoo. We will randomly select somebody to to give the book to. Um, George Furkan on LinkedIn is saying, congrats, Akshay. So congrats to you, my friend from George Furkan. Um. And no other questions from any other platforms, my friend. Thank you so much for taking time out of schedule to join me today and hang out. Um. Be sure to tune in to the podcast. Be sure to reach out to a family friend. No, I'm just rambling now at this point. And it doesn't say, guys, remember, you've got one life on this planet. Why not try to do some big shows of.