It's more than a week and I've not uploaded any video and I know most of you Have really missed your boy Mr. Ghana baby some of you I've been Sending me messages on Instagram telling me what happened to you. We need new videos I posted on Instagram and still people are commenting that hey No new video. I know you all missed me, but The main reason why I'm not uploading any video is because I want to rebrand the Africa to the world. I Want to bring quality instead of quantity because I started my videos in China and Coming to Africa to do the Africa to the world It was not something that I was expecting to be this big So I got overwhelmed seeing the number of people supporting the movement like I got more than 100,000 subscribers in five months of which it took me like 4 years to get 100,000 subscribers when I was in China so which means that people really love what I'm doing and I decided that it's time to upgrade the quality Because when I went to Ghana I Met old people. I mean people in their 50s watching my videos. I've met people in their 60s watching my videos I've met people in their 70s watching my video. I want to visit somebody in Ghana and he was like You have no idea what you're doing because all my life. I wanted to see Africa, but I couldn't get a chance to travel So I am seeing Africa through your lens When he told me that I felt like it's time to upgrade the quality of the Africa to the world movement videos So what I did was I Have a camera guy in China He's African. He's Tanzania. I had to let him come back to Africa And also have this guy that I met in Kenya. I mean the first time I went to Kenya. I met this guy He was like you motivated me to learn Chinese and stuff I think I did a video with him. This is the video that I did with him So and when I went to Kenya, he also helped me in shooting some of my videos. I know he's good So I decided to bring these people together to form a team as the Africa to the world video Editor so like they're gonna be the videographers for the entire Africa to the world, but I'm in a hurry Because this is not what I want to do for the rest of my life. I just want to do this for one year Finish the Africa to the world project and just enjoy my own personal life You know, I have to go to school again because I told you guys that I'm a renautical engineer I need to go get my license so next September is When I need to get my license so my next September I have to leave so I Want to make sure that from now So next year August We'll finish the entire continent and we'll bring you quality videos from the continent So that even if I'm away There are still videos that most of you can enjoy there are videos that people can see and say that this is Africa That is why I got this team, but you know, I've been in Senegal for like two days and My entire team including Babe doesn't know I was here You guys were saying that I Did a video with Quintili blah blah blah you say a lot of things like you guys are funny, you know This is my job man and don't think that any girl that I see I Just have to sleep with the girl or something. Hey, I'm not that kind of guy. I like talking. I like Expressing myself in my videos, but this is not who I am.  Hey, I Have my own life, you know and What do you see on camera? Might not be the real me. It's just sometimes for the video and This is how I actually Surprised the team when they came to Senegal Maya Well, I'm here for 3 days man you guys I'm telling you How many days since Monday? So now, I've been here since Monday. Waiting for you guys Why would you like to be? Yeah, this is Ghana. Is not part of Africa Oh Is that looks like my mom The day I was talking to you, I said I was at the airport. Like I'm filming a video at the airport Oh my god I Good I'm full of surprises man. I can surprise anyone any time any day They all didn't know I was here, but I was communicating with them every single day But anyway, they are all here Then as you can see, I'm not lying to you. We are gonna work as a team and from today Make sure you subscribe to the channel turn on the notification button because You're gonna be watching video because I see We are watching what movies We'll be uploading at least Three videos. I mean at least a day So be ready for us. We are here to work for you Because we want you to see Africa through us tell your friend to subscribe to this YouTube channel Tell your mother to subscribe to this channel tell your entire family To subscribe and turn on the notification button I'm planning to go to all these countries that I've been to before Because I went there alone but this time around I want to go with the team to finish work I couldn't do when I was there But if I can do all these things I need you It's time to support the movement You know, you cannot do this without money and we are all young people Trying to bring something good out of the continent So you need to support us You got it. You have to support us. It's not supporting us with just money But in case we we tell you guys that we are coming to your country We need a place to sleep. We need a car to move around You can give it to us maybe for a week or two weeks You know so we leave But the most important thing is like Our flight tickets traveling in Africa is so expensive So if you can help us pay our flight tickets It will be very grateful These are our donation platforms We have GoFundMe, we have PayPal, we have Patreon Just go any of the platform you can also use Wave You know in PESA we have in PESA we have mobile money go anywhere you want Just support the movement Me I would say one dollar it's even enough Because little drops of water makes a mighty ocean We are ready Get ready And come support the movement I don't even know what to say again But I want to say thank you so much for supporting the movement and also for Patreon subscribers We'll be uploading videos Specifically for Patreon donors So if you are not a Patreon donor Go to the Patreon www.patreon.waramaya Go there, go subscribe And support the movement over there We are here in Senegal I expect awesome and beautiful videos from Senegal To boy Mr. Ghana baby And on behalf of the entire crew we say thank you


Okay. Well, everyone, thank you once again for joining. This is Indra and I have with me Venkata from Scribble Data. As before, we are curating a series of conversations with data science folks, broadly called Making Data Science Work. This is about putting data science and its various avatars into production, where there are serious people asking serious questions about when do I get my money back on all this money that I've spent on this initiative and that. And today we're very glad to have two friends of Scribble Data, Ivan Shiklain and Dimitri Petrov, who are both founders of... Is he impressed? I got that. Who are both founders of... I remember us. Yeah, yeah. They're both founders of dbc.org. And now iterative.ai. dbc, I think many of you are already familiar. It's an open source version control system for data for machine learning projects. And in fact, I expect that some of you who are joining here today are actually dbc enthusiasts joining us from different parts of not just India, but potentially other parts of the world as well. And you guys are much looked up to. So Ivan, what was that? dbc has crossed 5,000 stars on github. That's incredible. Thank you. Yeah, super excited to have all the dbc enthusiasts here from India. And happy to answer any questions. What? Do they hear or later please contact us? Yeah, we'll provide them. We have time, right? Yeah, we answered different questions about dbc through 5 probably different channels. And today we got one more, right? Yes, we have... I believe we started with Discord initially, but now we have a bunch of forums, github. It's like a lot of different channels. Good stuff. I wanted to kick today's conversation off with just a little bit about the basics. While some of the people that are listening to you might know what you've done with dbc and what you're doing with iterative. I would love to ask first maybe Ivan and then Dimitri a little bit about your own personal journeys that has... Why are we speaking to you today? What got you into dbc and what got you into iterative? A little bit about your interest in data and your own personal journeys. We'll keep it short, but we'll be very curious to hear. Short, like 5 minutes short or 10 minutes short? Like 5 minutes short. Okay, so in 5 minutes I will cover my story and Dimitri's story and we will make something else. Go ahead. Thank you first of all. Thank you for inviting us. I'm really excited to have this deep conversation with data science folks, people who built ML tools. My personal story, I'm not even a data scientist I would say. What happened, I built tools for data scientists and I've learned recently a lot about data science, of course. But by trade, I'm software engineer. So my journey, I started doing system programming, like building databases back in Russia, Moscow, like 15 years ago. And I have done some startups not related to data science, web services, backends distributed systems, a lot of different stuff. And I found like the good question is how I got involved into data science at all, right? Why is this problem? Why, how, how it happened I connected with Dimitri and how it happens that we have been building data science tool for the last two years. I found myself wondering at some point, found myself rebuilding job title classifier. It's a very simple problem to have, like you have job title and you need to classify it to seniority level or type of job title, is it marketing or is it software development or something. And I was very curious, why don't we have some libraries for this? Why don't we have some ML models rebuilt for that? Why don't we have GitHub for data science, I would say. I later realized that it's a holy grail of data science tools to build GitHub for data scientists, I would say, right? But I found myself wondering why don't we have all of that? Like when I first started doing data science, being software engineer, I believe like a lot of software engineers do data science these days. I was wondering like how, why don't we share all these results? Why don't we have data sets? Why don't we have ML models? And even before I met Dmitry, I don't remember if he started building DVC at that point or not. Even before I realized he's building DVC, I remember we had that conversation. So I believe initially I came from a little bit different perspective to this area. And then when I met Dmitry, of course, it clicked because he will tell his story, right? But he comes to this problem from software engineering perspective as well in a way that resonates most with me and it makes more sense with me. So that's how we met and started building DVC and other tools. Dmitry, if you want to just add a little bit to this, your own journey and then when you went, Ivan? Sure. So first of all, I did some resorted academia, right?  And later I was data scientist at Microsoft. And at Microsoft, there's a huge difference between academia and industry company, right? In the industry, you have all these tools for machine learning called the beautiful AI platforms. Beautiful, probably not the right term, but at least you have the platform which solves the problem, right? You don't think about like how to get like this type of machine to run my code, right? A way to store data, how to like trigger data from pipelines and how to convert this to more production stuff, right? Dmitry, don't you use Excel spreadsheets to manage stuff? Yes, I did. It's a separate story. Let's not talk about it. So, and when I look at this kind of AI platforms and when I look to outside of like large companies, outside of Microsoft, I was thinking like, oh my God, like how people live outside? For example, when I start like playing, for example, Kaggle, like outside of job, right? Like, I realized that there are no tools. I start investigating like how startups solve this problem. There are no tools and they're kind of reinventing all these wheels again and again. And I like, and I start thinking like how those AI platforms should look like in five and 10 years. I don't believe that those like huge monolitical platform you can take from outside from like Microsoft, Uber, I don't know, Netflix and put it outside that they will work. I don't believe this will happen. I was thinking about kind of AI platform on top of open source stack. So it should be like open source tool. I believe we shouldn't have like one single platform. We should have like a bunch of tools which you can like chain together and they can work and sort of like a particular workflow because workflow for different in different teams. So, and then I start like playing with this, I start building something like how to get resources, how to visualize my plots, how to get this visuals to my machine, how to synchronize the stuff. And I found that I am kind of always making different kind of mistake. Like I got to run file and then like half an hour later, I just realized, oh my God, like I just waste like half an hour. And I need to do like the job again. And pretty soon I realized I cannot do this properly like resource orchestration visualization of my ML experiments because I don't have a good foundation. I don't have like a good version system data management system. And I got this idea. Okay, let's build this data version system somehow very quick. And then I will build like another blocks of this kind of platforms. AI platform. So this is how we stuck with DC. It's supposed to be like a couple months project. And today there are three years. We celebrate it like three years a month ago. This is a short story of DC. Thank you. And actually this leads me into the broad theme of what we want to talk about today, especially when Ivan was telling me about his primary trade being that of a software engineer. Right. So today's theme as the people that have logged in probably know is to discuss data science as a software engineering discipline. What can we take from software engineering principles and apply to data science to machine learning specifically. And I like that, you know, while you're not doing data science yourself, you are thinking about both from your training about what you were doing, as well as what do data scientists today who are talking about putting machine learning into production. What do they need and bridging these two. So that being the general theme of what we want to talk about. I'd love to ask, look, I don't want to ask you about. So what are software engineering principles? I think everybody has slightly different takes about it. And I think also that for the most part, our audience has already absorbed some of that. That tends to be the level of our audience. Happy to take questions from anybody in our audience. By the way, there's the question and answer tab on Zoom, or you can add comments to the YouTube video and we'll be able to get them to Ivan and Dimitri at some point during this conversation. If you have any questions, right. But maybe one of the first things I'd like to ask you is. Two software engineering principles apply to machine learning development and deployment. Anything that you want to talk about on this topic, maybe some learnings and approaches from software engineering. So go on. Sorry. Yes, what did we learn building these very complex systems over the past 30 years that is actually applicable to the new domain that is emerging? Dimitri? So before we jump to the principle, I think the more important question is like, why do we need those principles first and why now? And I believe why we start thinking about those principles. Why we start thinking about like engineering, like this supreme around data science is many companies today raise a point when they got finally, they got the first ML models is the first data result. And today they kind of their challenges have had moved from like how to get model, how to get data science folks for two production stations outside, right. Today they have this model and this model needs to work on I don't know cell driving car. It needs to work on some software. It needs to work to predict some stuff. Right. And this is the time when you start thinking about the principles because you cannot just take this model and put to like actual life system. Right. You need like a discipline around around those systems. Right.  Like why don't we learn from this and apply the same principles to our model management or kind of AI product life cycle. So this is I believe this is when it came from and today like many companies are struggling with with this particular problem, not like how to get model. Yeah, right. I would say that maybe a little bit controversial opinion. I don't believe like data science is unique, very unique or very, or, or like very, very different from software engineering. Right. Or very complex comparing to data science. It's just, we have early days of data science in software engineering. We have solved all this complexities. Quite, I think quite successfully still evolves, but we have gone from very monolithical like version control systems and waterfall approaches to a trial to version control systems like it to motherality in systems to open protocols to everything. And we have solved and improved all our processes significantly. I believe in data science. I mean, it will be happening something like this as well. So we will be taking best from software engineering applying it to data science process. And pretty soon we will have like an ecosystem of tools that will be solving all the complexities of all the processes. I can add to this. I can add to this. Basically, we need to kind of learn from like software engineering experience and transfer this to data science. And as Ivan said, a lot of like all this principle applies. However, we should know that there is a difference, right. And it will, it will be a big mistake to ignore the difference. And this is actually one of the reasons why many data science tools are not very successful. Because when you just take like one principle from software engineering, which makes total sense and they apply for data science. And the folks might be gets reluctant to use this. And so we need to understand this difference. We need to kind of think deeper about this kind of data scientist behavior, like what, what they need, what is important, what is not that kind of apply. So, Dimitri, I have a question about this, right. And I think we're getting into the difference between traditional software engineering and data science, especially machine learning, right. There is, I mean, beyond the fact that, you know, the word learning is built into the ML. Beyond that, there is also this notion of growing complexity of systems. When you're building a machine learning model, the idea you're already thinking about future versions of it, you're already thinking about the different ways, ways different than traditional software that this can break and that way you have to be responsible from a retraining perspective, from a debugging perspective. Can you sort of help me tie this into why you were thinking about DVC and how, you know, maybe a little bit about how the DVC solution sort of plugs into this. Yeah, so this is a good question and, and, and yeah, and we build DVC kind of to reduce part of this complexity. So the, I believe the complexity comes from one of the bigger source of the problem with the complexity is we have a two different stacks today. Every team has two different stacks. You have a set of tools for software engineering, right.  You have your CI systems like that, like dozens of those code quality system control, right. You have like agile process and all the stuff from one side. And on another side, you have the same set of tools, not the same set of tools, but a new set of tools for software engineering, and you need to connect them. And it basically creates a complexity, right, instead of like five different companies, which can connect it to each other, you have like, you got like five more companies, and they're like interacting with each other. And workload is not defined. It's not well defined. And people just get lost on this like complexity. And today we have like so many system, right, because some, some folks need like machine learning tools, like for modeling, the other people needs like data processing tools like for clusters for like HDFS or some other stuff. I mean, and a lot of problems can be solved, relatively easy, but not easy to put all the system together and kind of come up with a meaningful workflow around the system. So yes, what I think today we kind of all the complexity moved from like how to build model, how to solve this problem to how to organize the workflow how to make all the system work together. I think this is like one shift, which is happening today. Yeah, I, sorry, I'm going. Yeah, yeah, I would add to this coming back to your question, how do you see if it's right. Maybe we jump ahead a little bit. My perspective for data science, there are two differences, right, major differences data and science data means that you have now some artifacts to take care of. All models data sets and science processes different. It's like R&D like highly iterative process where result is not well defined. It's the metric driven and we go can go on and on, right. And that's what DVC is about. It's about collaboration that fits into this two differences. It's a collaboration tool to how do we kind of share our data sets, where do we put them. It's like fundamental problems. Every team faces first time first time they start doing any ML process or data project. How, how can I send you this ML model, how I can communicate with my team, how can I communicate with production systems. That's what DVC is about. It's a fundamental layer, collaboration layer for data science projects. Tell me this, would you say that DVC, whether the way you're building it or for some other format of it was inevitable in the evolution of products in the overall data tool chain? Well, good question. Yes, yes.  Where do they put their models? How do they name them? How do they put them to some machine to train, right? They come up with prefixes, suffixes, some locations or necessary or something like that. And it reminds me a lot of software engineering back in like 20 years ago, right, when people were sending archives to this software code to each other. And then merging them together in some peculiar way using ML. Not personally. Step back and the way, even the way you're framed, which is that data is the new dimension that has been added and the fact that there is science that is a second dimension. It's fundamentally a search process, right, where you're building the systems and these systems are continuously evolving, getting it closer and closer to our objective that we need to meet. Now, that seems like a fairly complex process because it involves a lot of understanding of what problem we are solving first and how are we solving and also actually implementing learning from it. And so on. If you look at the software engineering journey, we had almost 30, 35 years. The first versioning paper was written in what, 1970? And Git came about in 2000s and GitHub came around the same time. We had a long duration for us to learn, understand, debate, explore various options and so on. How are you seeing the repetition of this whole process in the data science discipline as it is happening in 2000 and how do you see it going forward? How do you see the evolution of this space? Yeah, so it was quite a journey, right, for software engineering. It took like 30 maybe 40 years, maybe even more for this kind of change, right, from like, hey, this is a version control system and this and today like every company, every software company uses, right. I believe in data science, all this evolution will happen like just way too fast, comparing to software engineering. First, I mean like five years, not 50 years. And I believe there are a few drivers for this. First, Internet, right? So like in 70s and 80s, it was of course not easy to innovate when you like send actual emails, not emails. Second, more folks are involved in this process in software engineering and data science. And what is even more important, we can learn from software engineering, we can learn from other disciplines. For example, today like they're not only software engineering, right, they are designers, they are some other professionals and we can learn how they do their work and get those ideas. And even more than that, we can take their tools and build out our tools on top of those, right. And this is the next driver. So, and this is actually our fundamental belief behind the company and behind the tools. We don't try to invent new systems. We try to build the systems on top of engineering one on top of existing one. And this is one of the major drivers which can kind of get us through this journey in five years, not in 50 years. Yeah, that's right. We can definitely benefit from the knowledge software engineering discipline acquired for sure and can build on top. And don't forget like software engineering is way more efficient right now. It's not even about Internet only, right. It's about all the tools software engineers have. And building tools for ML, ML Ops, ML engineering is engineering discipline. It's like in our company, I wish we had more data science, I would say. And we discuss and debate with Mitry a lot about our team that they even, we feel some disconnect even like we're building tools for data scientists. And it's software engineering discipline, but they don't sometimes we wish they had more involved into data science to understand the space. Makes sense. I have a question from Neeraj who has asked something on the QA panel. He says, how is DVC designed for experiments in development environment where we work on sample data which is relatively small versus DVC in staging or in production where the data is huge. And further goes on to ask how can we reproduce the pipeline in staging. Okay, so we go to details on DVC, right. This is where the audience wants you to go. Yeah, we can ask. We will answer, but before jumping to details of DVC, let me give like a bit like overview of DVC, right. Because for some folks who are not familiar, they might not understand DVC kind of from even from high level. Let's start from high level. So one of the DVC, we say DVC is version control system, right. It's kind of good for data. But if you look closer to DVC, it's not. This is like a kind of surprise. It's called a data version control, but actually DVC just codifies your data. It codifies your data, replace data by meta files, put your actual data to your cloud, which is like S3 like Azure, or maybe you have like separate like SSH server, for example. Like DVC supports all the major storages. And you do the actual versioning by Git, right, the meta file versioning. So in this way, we kind of just codifying data and we are not kind of reinventing version control system, right. This is exactly what I mean when I say we'd like to build system on top of engineering workflow. We don't want to reinvent version control. We'd like to argument version control by data files, by pipelines, by model versioning and all the other stuff. And metrics, yeah, metrics is like one of the crucial part of this, of the workflow. If you're like familiar with DevOps paradigm, you can think about DVC. It's kind of like, kind of a terraform, right, terraform, for example. A terraform codifies your infrastructure. DVC codifies your data. This is analogy, not the perfect analogy. Yeah, in the infrastructure world, there is even a term, a fancy term now, GitOps. And I would say, what DVC is, it's GitOps for data. Yeah, data ops, one more term. Data ops, one more term. Yeah, we can go one known with terms. Yeah, solid metric for interrupts. Yeah, go ahead. So I'm actually done with introduction of DVC. So the question that Neeraj had was a little bit deeper into DVC. Yes, right. And I know we'd like to have kept it later, but what Neeraj asked was, how is DVC designed for experiments in development where the sample data is relatively small versus in staging or production where the data is huge? Yeah. First of all, let's discuss experiment on DVC. So on DVC build on top of the Git system, right? We operate by commits, right? For DVC, like experiments, it's a commit. And if you are working with this concept like experiment as equal commit, you kind of are good to share your experiments among the team, right? And if experiments built on your focus machine with a small data set or maybe with like synthetic data set, and you need to kind of replace it by the production one, what is needed, you just need to replace the data sources, right? In DVC, we have like a concept of pipelines. And in the pipelines, you need to replace the data source to production one, and you run the pipeline, do DVC repro on top of another data set. So this is what people are doing sometimes, sometimes they do this in their kind of in a manual way. In some scenarios, they do this automatically. For example, if you have like CI-CD system and you do commit, commit your experiments which go through CI system, then CI system can do the replacement of data set, your data set to the production data set. And CI system can run training on the production data set. And in this scenario, the beauty of this scenario, user can be even separated from data, from the actual data. So as a user, you might have access only to your data set, only to test data, not test, but like development data set, not to production data set. So one thing I'd like to add here is that now DVC has a substantial community, you know, and every day they're putting out more tutorials and documentation on how to address all of these scenarios, they're very active. So Neeraj, I would recommend that you lock into the various forums that are there in DVC. I think this would be fairly, you know, they would be ready tutorials on this. Yeah, my perspective to add to this is for those who are familiar with Git, you can, it's close analogy to think in the same terms, like how would you put Git into production? How do you use Git in production? You use Git in production as a protocol to deliver stuff to production, like you do Git clone or Git pull or Git push to communicate with some production systems. Maybe in some automation scenarios, you do Git commits as well to capture some results, for example. The same with DVC, it plays very well as a kind of ledger of what's happening under protocol and project description and plays very well in the development phase when you collaborate with your team members, when you collaborate with production systems, but it doesn't try to replace air flow when we talk about pipelines or sophisticated production systems that run pipelines 24-7. The goal is to maybe deliver data sets there or maybe capture results there so they are visible so that they can be reproduced so that we can share them. That's the goal of DVC as a foundational level. It's a protocol. It's a way to organize your project. It's a protocol between systems. It's a way to capture your data artifacts. Wonderful. In addition to what DVC does, I liked how you talked about why you approached DVC as a problem in the first place. I think both Dimitri and Ivan were talking about this, your own individual approaches where you eventually seem to confluence. The fact that you were thinking about the data tool chain that itself was interesting. So I have a question that moves a little bit away from DVC. Maybe we'll come back to DVC in a bit. But the question that I have is, in your opinion, what are the next logical steps in the development of the data science engineering tool chains? Do you have any hypotheses, anything that, or maybe you've seen something, maybe you've seen some interesting projects that are happening, which you think, yeah, that makes sense. This is what needs to happen next. Yeah. Let me think. Screeble, right? I saw one interesting question. Thank you. To be serious, a lot of interesting stuff is happening, right? And what we see is definitely, like Dimitri mentioned, we would like to see eventually an ecosystem of different tools play well together. And what we see now, CI-CD system appear for ML, and Dimitri can talk about that more. And we see feature stops. Now at least people realize there is a term for this system, right? I just want to say to people, we are not being these guys. I have no idea why. That's what we have across the industry. It's not about Screeble or DVC Perse, right? It's not about only us. And we see deployment systems. And what I like most, we see open source deployment systems as well, right? That solves all these complexities. We see tight integration of software engineering tools with data science tools like Qflow, for example, runs stuff on Kubernetes. So, yeah, and I hope step by step we go into the direction Dimitri mentioned. Yeah, it's amazing. The amount of progress in the last two, three years, I mean, it is exponentially growing the tool ecosystem itself. Go ahead, Dimitri. Yeah, I just want to add, like, yeah, I want to describe this kind of in a really nice terms. And I believe the next big thing will be related to integration between those systems because today many companies, many systems are built in a way that we are kind of, we are end to end. But I believe in the future we will see, like, more integration between those systems when, for example, people can work with, for example, work with data through, like, DVC or some features tools. And they will be able to deploy the model and then we'll get the result back, like to close the loop, right? To see the difference between metrics, what you got on modeling stage versus what you got on the production stage. What is the equivalent of this in the traditional software engineering? Are we talking about network protocols, REST APIs and standards? What is the closest analogy that comes to your mind when it comes to integration? Yeah, integration. So I believe that a big portion of those integration is done by version control system on the development stage. Part of those came from API if you are talking about, like, deployment system and production systems. Yeah, and maybe, I mean, even it's not even about only version control, it's about version control integrates all these pieces together, right? But next level is that we have all these tools we integrate, like GitHub, with CICD tools, then with some production, yeah, Git ties all these together, but we have layers of tools. Oh, yeah, yeah, yeah, yeah. Just Git is not enough, of course. You need to kind of... Yeah, Dmitry, maybe remember when we initially met, the way probably, as far as I remember, Dmitry pitched me, DVC was not about DVC and it's the end goal of us solving some problems. I remember we had discussions about CICD and it was three years ago, CICD for ML. Maybe you can actually share that part of the story. Yeah, actually, CICD is one of the pieces which connects the things together, right? In software engineering, the same might happen in data science. When you have your code, you have your kind of developing phase artifacts and CICD connects those artifacts with computational resources, right? When you build some stuff and it reports the result, which is important, right? And in data science, this result is even more important because in software engineering, what you need to say, yes or no? Like, was the build successful or was it not? Test, pass, test, fail. In data science, accuracy went down like 0.5% and precision went up to 0.75%. Is it like good or not? I don't know. This is the big difference between data science and software engineering. So that's what I'm wondering, right? When we think about CICD system, the one known unknown is the data. The data keeps changing, right? And in the case of traditional CICD systems, we had a specification and we could write tests for that specification. And then we could check in our CICD systems as to whether it is meeting the spec every single day. Now here, spec is unclear and it is changing every single day. So what are the, there should be a missing piece somewhere. The CICD systems have to be, the concept has to be reinterpreted in the data science space again. Yes and no. I mean, before we start to reinterpret this, let's first, I would say let's first do the first step and interpret it in a similar way as software engineering has. Let's first learn how to run tests for data, for example, and run tests for ML models, at least with some golden data set we have. And let's learn how to, yeah, it will be a good start. And what you mentioned that data is changing, yes, but it reminds me more about monitoring systems, I would say. It should be part probably of some monitoring systems and which I will win as well, right? For the close to the development phase, and I will win fast. When you want to detect some drifts in how model behave, right? And it's a little bit different problem to my mind, at least comparing to CICD, but who knows, I mean, maybe it will be part of CICD as well. Yeah, I think, I think important important part is here is the important part is the workload, how the workload is organized in software engineering. Yes, you kind of have this like kind of solid artifacts, right? Your code or maybe your data, you expect them to pass all the tests, you expect them to be like correct, like 100% if possible and then build something out of it. In data science, unfortunately, or maybe fortunately, I don't know, you don't have like, how to say, you cannot be sure. You work kind of in a different, a little bit different paradigm. It's okay if your data change. So you should expect this. It's okay if your model makes a different prediction. I think, but you still need to be consistent in your process, right? But the, all this complexity, it moves from the artifacts to a high level to the workload level, right? You need to be sure that your model is not drifting like too much, right? Whatever it means for you. And you should be okay if it drift like a little bit. You should be okay if, for example, precision decreased if accuracy increase, like something like this. So you don't need to be like 100% accurate as you wish in software engineering. And this is like a huge, like, I don't know, cultural difference, right? When we talk about the difference between software engineering and data science, because for software engineer, engineer, it might be not clear. His motivation pushed him to reduce all this uncertainty, right? In data science, you are okay with uncertainty. You just need to like a build a right workflow around how to mitigate these issues with uncertainty, how to deal with this uncertainty. So it's the worst nightmare for software engineer. Why can't we increase usually precision and recall simultaneously, right? 100% or something like that. Yes. Another question from Nidaj, which is, he says it's related to the same thing where Dimitri talked about changing the data source from sample data to production data. Nidaj says, as his production data is a collection of so many .dvc metadata files, how does one specify so many files which are increasing every day? Is this one best relegated to the forums or is this one that you can quickly answer? Yeah, I can answer from like high level. On the low level, yes, we can discuss on the forum or through other channels, right? On the high level, yes. In the dvc, we create meta files for like all the sources and some amount of files increases and sometimes it bothers people, of course. But we know about this problem and I believe we solve this problem and we will be releasing dvc next week, dvc 1.0. And in dvc 1.0, we kind of moved away from separate dvc files, separate meta files into a single one. Also, we separated the part that user work with and what system is generating, like with hash sum and all this stuff. So, you kind of have one specification, human readable specification and you have a special file with technical details, with check sums, with all those internals. Yeah, and I would add to this that dvc itself handles them, right? It's kind of, dvc is built to be a little bit more explicit than git because we deal with data. We want to be more explicit. We don't want, for example, to clone all the data artifacts you had and the whole history of your experiments at once. So, we made it a little bit more explicit intentionally. So, you see these meta files in your project, for example. But dvc handles them.  So, dvc helps you in this way. The only file that Dmitry mentioned is kind of make files we have for ml. Those, again, like dvc first of all can help you edit them and second in dvc 1.0 it will be a single file. Wonderful. So, coming back to this question of discipline, right? The end-to-end scientific process and essentially the way to understand what is happening today is that today the data science, the modeling development process, we are starting to impose certain checks and balances through the entire process. It was anyway required that if it was going to be a science to begin with, reproducibility and all of these should not be a strange word. But the question that I have in mind is that the end-to-end discipline applies across all layers of the stack through the entire journey of the data. Whether it is starting from the data collection itself or even at the process level. For example, when you are iterating on the models itself, that also needs discipline. I'm just curious about what are, do you see any significant gaps? For example, there are no standards for data collection today. Our client just asked for a recommendation on standardizing that process and when we looked at it, we couldn't find one. So I was just curious about what other big pieces needs to become a lot more controlled to achieve end-to-end discipline or confidence in the outcomes. Yeah, so it's never ending story. I don't believe we can achieve 100% confidence or 100% reproducibility in this sense because data pipelines are hard. We have so many moving pieces. We have data ingestion, some big data systems, some databases on top of that. Then we fetch some files from these systems to do actual ML modeling or something. And we can capture, I mean, the question is do we actually, in some industries, I believe we do need to capture the whole pipeline and version it. But in majority cases, I would say the really good first step would be to capture at least starting at some point. At least get some snapshot of some intermediate artifacts and data that you deal with today or your model is trained with. So that we can at least have some responsibility and accountability moving forward that something happens to see what data was used. I mean, you have to break up the problem at some level and each subsystem can have its own definition of its discipline and the tools to encourage discipline and so on. But I was given that any guarantees that you have to give for the model as far as correctness or the explainability and things like that. Somehow there needs to be coordination across all the regimes of discipline at every step in the life cycle. Right, I mean, I don't believe there will be one single tool that will cover all the kind of responsibility problems across the whole data stack. It's just what are so different like deaf world when you do get an ML modeling versus some big data clusters and some data ingestion like Kafka. Try to version Kafka with DVC. Probably it won't work and it doesn't make sense. If it's needed, there will be some tools which will be taken care of there. I have a question that a little bit tangential to this just to add a different flavor. You guys are building a product. You're building a tool and in that sense, you guys are entrepreneurs, right? I don't want to lose sight of the fact that it's a business that is marketing, there's scaling. There are choices that you're making all the time. And I would love for the entrepreneurs that are there in our audience, not just about the data. If you could tell us a little bit about when you went down a wrong road or when you've had a significant disagreement about your roadmap. I'd love to hear a little bit about how you, what your mechanism was to either identify when it is that you're going down the wrong road or to come to an agreement that this bit of the roadmap, you know, even though we're two sides are pulling in different directions, how do you finally come to an agreement? So either of these, if you can just talk about a little bit. And the journey of DVC itself and how the community is going. I think that might be a little broad, but if we can just focus on that. Go ahead. Yeah, so regarding the roadmap, this is, first of all, we can, we have kind of like a big roadmap, right? What kind of product we need to build? What kind of problem we need to solve? And this is, it's about big steps. And what I see, we haven't changed this roadmap much. So as Ivan said, like three years ago, we were talking about CICD for machine learning, right? And on the last year, this year, we see like a clear signal from the market like we need this, right? People start talking about CICD for machine learning. So we know that versioning part, data transferring part was crucial. And we see this today and we are building DVC. Like some new directions like how to make, how to version, not data sets, not file, how to version, how to version object like, how to build like feature stores, some stuff. We haven't had like a big plan to move to this direction, but we were thinking about this before and we see today like more and more movement happens. So I believe if you don't have like a big disagreement on this level, however, when you start building like a product, when you start prioritizing tasks, right? And decide like what we need to focus next, not year but month. So this is when we have like all the disagreement and all the like discussions around like what we should do. Yeah, and it's fine to have disagreements, right? You just need to come up with, you disagree and commit and move forward. Yeah, they have to have disagreements. Yeah, and also I would say it's important to test hypothesis fast. I mean, it's fine to fail. It's just you test hypothesis, you move to something else and you do something else after that. It's just how do you test as many of those as fast as possible? And also I would mention that with DVC, our community helps a lot. Don't forget we have a lot of users from day zero. I remember like we had, initially we had like one guy or someone came and asked some question and we're happy. Yeah, and we were kind of trying to solve the problem and learn as much as possible. Now we have like three, four, five, ten issues a day sometimes and some discussions on Discord. It helps to drive road map a lot. At least short term road map for DVC is very clear in the sense and it helps to prioritize tasks like Dmitry mentioned. Should it be CI or should it be deployment or should it be something else? Community helps a lot. So thank you everyone who participate there. Maybe to your actively going beyond the folks who approach you, you have a significant outreach program as well that you started recently. Can you tell us a little bit about that? I think the few folks in the DVC community in India that would be interested. Yeah, so our community as Ivan mentioned, right, was growing like from like one request per week when we got like two years ago when DVC was just launched to like five, three, ten requests per day. So this is like, we got this active community of the users and the natural next step for the community to build kind of more systematic way of working with community. And we launched like the ambassador program, DVC ambassador program. So what we try to do, we establish connection with active team members all around the globe. And we try to help them to kind of work with DVC, to write about DVC, to talk about DVC, to organize meetups about DVC. We introduce them to each other. So they have like a lot of common interest and common projects. So they help each other. So and today we kind of reached this level when the community kind of went to the next level, to the ambassador level from kind of random folks to more, I don't know, it's systematic, right, work with community. We just launched this community like less than a month ago, I believe we published the first blog post and we got like five ambassadors with like some of those already had a good experience with DVC with created meetup about DVC meetups about DVC or writing blog post. The other don't, but they want to. And now, yeah, we are very excited about this move, kind of a new thing for us, very new thing, kind of additional team, if you wish. Yeah, good channel for them for people who are interested to reach out and in some way start becoming a part of this. Yeah, of course, first of all, on the website on DVC website, you can find this community page and there is some information about the ambassador program. And recently, one of the ambassador published a blog post on our website, you can find the blog post and read more about the ambassador program. So how it works if you're interested. Yeah, please welcome. Wonderful. So, I'm thinking that that's a great note for us to be able to wrap this up on. But Ivan and Dimitri, I know that we will usually get questions that come once people see the video they posted under the video on Hasgeeks own page or comments come later on on YouTube after people have seen the video. We'd love to be able to circulate them back to you and get some of your answers. And I think you guys have fairly public profiles and DVC.org is fairly public as well. So for anybody that has any questions, you have all of these different avenues, the various fora that DVC participates in. The Hasgeek page where the stock is where your registration for the stock was where the video will show up, as well as the YouTube link where this video is currently being streamed live and where it will be available afterwards. So with that, I mean, thank you so much for making yourself available. I say this with a lot of warmth because like I said, it's 6am and I haven't seen 6am in a long time. And the second thing is the energy that you brought to this was again amazing. So thank you so much for being here. And we will continue this conversation in some format or the other. As I promised you, if you find stickers that would like to ask some more questions in your time zone, we'd be very happy to do this again for you at that point. Thank you. Any closing thoughts? Yes. First of all, thank you Ivan and Dimitri. We've been having conversations but it is great for you to talk to our community as well. Scribble, just like how iterative is Friends of Scribble, we are good friends of DVC as well. We will be more than happy both as users and facilitators to connect you back to the DVC folks. We are a strong believer in the project itself and users of the project. So, you know, looking forward to rapid growth in the Indian community, DVC community. Thank you Ivan. Thank you Dimitri. Thank you Dimitri. This was a great discussion. See you around. Talk to you soon. Take care everyone. Bye bye.


Welcome back everyone. In this video, we're going to talk some more about finding the inverse of a function algebraically And we're going to look at some examples involving square root functions So let's find algebraically the inverse of the function f of x equals the square root of x minus 2 We're also going to find the domain and range of this function here And so I do want to mention that real quick if we mentioned the domain and range of f The graph of the function is going to look something like the following This will be this function right here is our standard square root function But it's been shifted to the left by 2 because of the x minus 2 inside of the square root and as such our graph would look something like this again It looks like the standard square root function, but shifted to the right by 2 therefore the domain of f is going to equal 2 to infinity and Then the range of f because we didn't do any vertical transformations the range will just be 0 to infinity So we can look at the domain and range of this function very quickly through our graph transformations What we can already do is we can already predict what the graph of f inverse is going to look like if we draw the diagonal line Y equals x we know that the graph of f inverse will be the reflection of This fun of the original function f across this diagonal line So we're going to get something like the following F inverse right there. That's what this graph would look like Remember the white graph here was f So let's consider the algebraic function for f inverse, right? So it seemed previously that the function f is given by the relationship y equals the square root of x minus 2 Where we replaced f of x with y right here in order to go to the bizarro realm and get f inverse We're going to switch the roles of x and y right here y becomes an x and x becomes a y and now we have to solve for the Inverse function have to solve for y in this case by performing the inverse operations We're trying to get y all by itself on the That's on the right-hand side of the equation. Well to get rid of the square root function We apply its inverse x we square both sides This gives us that y minus 2 is equal to x squared and then we'll add 2 to both sides We add 2 to cancel the plus 2 that's on the left hand side. This gives us y equals x squared plus 2 But when we'd say y here, we're really going to put in the name of the inverse function f inverse So we're very specific. So here's the formula for inverse function F inverse of x is x squared plus 2 But we do have to be careful about domain and range because of the domain convention Unless we specify otherwise one when they see the formula y equals x squared plus 2 You would assume that the domain is maximal. That is it be all real numbers But because of the domain and range of the original function in order to make the inverse function one to one We actually don't get the left hand side of this parabola Instead we're going to switch the domain and range of f to give us the domain and range of f inverse The the domain of f inverse is going to be the range of f Which means it's going to be zero to infinity Which we see right here you get any x value to the right of the y-axis there And the range of f inverse it's going to be the domain of f Which is 2 to infinity and looking at the graph here You're only going to get those y coordinates which are greater than Or equal to 2 And so the range and domain of f inverse are just going to be the opposite of the range and domain of f The domain of f becomes the range of f inverse and the range of f becomes the domain of f inverse Let's look at another example quite similar in nature We have a function f of x which equals 2 plus the square root of x minus 4 So the formula that defines f is going to be y equals 2 plus the square root of x minus 4 Switching to the inverse function the equation that will define f inverse will be created by swapping the roles of x and y The y is going to become an x and the Let's see the x will become a y and so now we just proceed to solve this by doing all the inverse operations So track 2 from both sides so the two cancels on the left hand side I like to put the y on the left hand side, so I'm going to switch the order here We get the square root of y minus 4. This is equal to x minus 2 Next I want to square both sides make sure that you're squaring the entire expression x minus 2 Like so this would then give us y minus 4 on the left is equal to x minus 2 squared For which if we want to we can we could multiply that I'm going to leave it factored For reasons that will become clear in just a second and then minus I guess we want a plus 4 to both sides and this is going to give us that y Equals x minus 2 squared plus 4 But then again, I'm going to get rid of the y in the final stage And come over here and say f inverse of x equals that So it's very clear that this formula is giving us the formula of the inverse function If we were to compose this function with f their composition would just be x these operations will cancel each other out In terms of graphing these things We could we could sketch a graph of this very very quickly Some things to note here. I'm going to put a little bit of tick marks on the On the axes there Something like that. So the original function right here, you can see that this is the standard square root function It's been shifted to the right by four. It's been shifted up by one Um, and so if we count that out, you're going to get 1 2 3 4 to the right and then 1 2 up your graph will look something like this In particular there's this point 4 comma 2 Whoops 4 comma 2 and this is the graph for f Now we'll draw the diagonal line in there to emphasize the symmetry that's going to happen If we graph the function for f inverse This function right here is going to be the standard parabola It's been shifted to the left by 2 and it's been shifted up by 4 so we go 1 2 to the left or to the right excuse me and then we go 1 2 3 4 Upward and then it's going to look like the left side of a parabola And so this would be the graph of f inverse like so it'll emanate from this point to comma 4 They're going to be reflections of each other And so we can very quickly see that the domain of the square root function We can find that out by solving the inequality x minus 4 is greatly equal to 0 Or we can see this from the graph the standard graph has been shifted to the right by four units So it's the domain of f would be 4 to infinity And in the range this one's a little bit harder to do algebraically But we can see geometrically that you took the standard square root and you moved it up by 2 That changes the range to be 2 to infinity And so then considering the domain and range of f inverse the domain of f inverse is going to equal The range of f which is 2 to infinity as you can see here We get all x coordinates to the right of 2 And then the range of f inverse This is going to equal the domain of f Which is 4 to infinity and you can see we get everything above 4 on the y-axis So this is how we can compute the formula for functions inverse algebraically Just take the original equations swap x and y and then solve for y We can find the domain and range of those functions by computing the domain and range of the original function Probably going to do that using transformations And then by switching the rules of domain and range you get the domain and range of f inverse One thing I want to mention here is the original graph was shifted to the right by 4 and up to On the other hand, this function was shifted to the right by 2 And shifted up by 4 The horizontal shift of the original graph becomes a vertical shift in the inverse because When you take the inverse function the horizontal becomes the vertical On the other hand the original vertical shift of 2 Turns into a horizontal shift of 2 when you switch to the inverse function Because the vertical will become horizontal when you switch to the inverse function Because the inverse function by its nature it swaps the roles of x and y Everything horizontal becomes vertical and vertical for horizontal The inverse function is just the original function stating on its head so to speak


Ladies and gentlemen, welcome to Oscar's Wacky Taxi. Please keep your hands and arms inside the car at all times. Remain in your seats, secure all those articles, and enjoy your ride. Yeah, but don't enjoy it too much. We won't get a scram as soon as it's over.


My background is, it's a bit of an odd one perhaps, my undergraduate degree is in German literature and I had almost a second major in biology, I just chose not to do a second thesis. In German literature there's a long tradition of exploring the relationship between science and technology and society. I would say I benefited from the opportunity to think first in a structured way about the role of science and technology in society in German literature courses and through fiction. And then when I graduated from my undergraduate degree my first real-world job was working in a medical genetics research lab where we're doing gene therapy research for an inborn error metabolism that affects kids and it's a disease for which there are currently treatments. It does not make life easy for those kids who have this disease but it is certainly a condition that many people are living successfully with and the technology that I was working on developing was one that creates all sorts of really complex ethical questions. If we were successful in developing gene therapy technologies that introduces a lot of difficult societal questions, for example, should there be limits on the types of modifications we would seek in our children? How do you define disease? I started really thinking deeply about science and society and the complexity of that interface from building on the undergraduate degree but then applying it to this real-world situation. I try to introduce my students to the insight that we have environmental dilemmas not because people who don't agree with us don't understand the facts of the world but we have environmental dilemmas in part because we as a society we disagree with one another on key issues along the lines of what form of government is appropriate in our democracy, what constitutes government overreach, our markets, how we solve problems or markets what cause environmental problems. If we don't agree with each other on some of those basic levels we're never going to agree on how to understand and how to address real-world environmental or social problems. My goal is to help students understand that offering facts to someone who doesn't already agree with you rarely is going to persuade them to change their opinion or their political preferences. What we instead need is the skill set that would allow us to have meaningful, deliberative exchange of ideas that also is about values and not just about facts, facts, facts.


The challenge of the Yukon. Honking, hon, you huskies! The Wonder Dog King, swiftest and strongest of Eskimo lead dogs, blazes the trail through storm and snow for Sergeant Preston, as he meets the challenge of the Yukon. Sergeant Preston was typical of the small band of Northwest Monty Police, who preserved law and order in the new Northwest country, where the greed for wealth and power led to frequent violence and bloodshed. But in spite of the odds against them, Sergeant Preston and his Wonder Dog King met that challenge and justice ruled triumphant. Sergeant Preston of the Northwest Monty Police stood at the bar of the Gold Nugget Cafe in Dawson, talking to Bill, the bartender. Well, Bill, something new since I was here last. Where'd the piano come from? Oh, that belongs to Shrimp Togan.  Almost starved himself to death, doing it. Oh, that belongs to Shrimp Togan. Poor little guy, he finally saved enough money to almost starve himself to death, doing it. Well, what was the idea? He really likes to play and figured if I let him keep it in here, the customers would tip him enough to make a living. Does he still work at salmon fishing? Yeah, in the daytime. He thinks he'll be able to quit it soon, or the piano playing business keeps on the way it has. Oh. You going to be in town long, Sergeant? I guess so, Bill. Say, did you find out anything new about the first deal and deal? Not much. I thought you might have some information for me. Nope. I've watched Bull Jordan. He's been spending a lot of money. Nobody knows where he gets it. But I ain't heard nothing suspicious. You know, Bill, I'm sure he's had other the gang of thieves doing it. I can't get a thing on him. They could just find out where they're hiding the first after they're stolen, but they're clever, Bill. Hello there! Hey, there's Bull now. He's been drinking. I'll take King and go in the back room, Bill. Maybe he'll talk out of turn if he's had a few drinks. Try sounding him out. Hey, I'm getting a drink. Yeah, oh, Bull. Hey, set him up, Bill. Hey, where'd you get the piano? Longster shrimp token. You mean that fishy smell, little guy? Where'd he learn to play? I don't know. Here you are. What you been doing lately, Bull? Nothing much. I don't like that piece he's playing. You shrimp. I don't like that piece. Oh, hello, Bull. Do you like my piano? It looks like a battered-up old tub. And it sounds like a lot of tin canes banging together. Boy, it does not. It's beautiful. Beautiful? It's a heap of junk. I bet I could tip it over with two fingers. Bull, let's jump alone. We want to hear him play. You play what I tell him to. Hey, Bull, quit it. You have too much to drink. You keep your nose out of this.  Do as I say. What do you want me to play, Bull? Play. She's my Daisy. Go on. But, Bull, I don't know it. Did you hear me? I said play it. But, Bull, honest, I never heard it. Don't get smart with me. You play it. Let go of me, Bull, please. If I knew it, I'd play it, Bull. Let it go. I'll show you.  Here you. Give me that axe. Hey, get that back. Give me my axe. I'll give it to you. Right over the head of you. Come a step closer. Bull, please. Please don't break my piano. There should be fun, huh? You think the law. I said put down that axe. Sure. Put it down. Can't get in the bad graces of the law. You're under arrest. I see you're pressing. I was just having a little fun. Oh, my piano. It's ruined. Pay him for it, Sergeant. No hard feelings. It was all in fun. Fun for you, perhaps, but not for shrimp. Come on.  What you can't get? Just going, I said. It was three days later, an old jake who had charged at the jail handed Bull his dinner through the bars. Here you are, Bull. Some good steaming stew.  Hey, jeek. Have you thought about the proposition I made you? I ain't interested, Bull.  They ain't got any right to keep me here. Well, they said you was to stay in jail until you got another pie added to replace the one you smashed. That'll take months. I don't know how to get one ever. I guess maybe you should have thought of that before you decided to break up shrimps. Yes, I'll frame up that's all it is. They want to keep me in jail for some reason. I don't know nothing about it. All I got to do is to see that you stay here. Okay. Well, Mark, what are you doing here? Is it all right if I talk to Bull? Well, sure, but first put your gun on that table. He's liable to grab it. Why, sure. Ain't got another one on here by any chance? Nope. Just got to search at them's orders. All right. All right. Go on, you can talk to him. Hello, Mark. How are you, Bull? I won't go back there until I'm sure I ain't fallen. Listen, we got to get them purrs out before the river praises. You ain't touching them without me. What they say you're being held here for a long time? I'll bust out of here some way.  We won't. I just wanted to know what to do, that's all. You shouldn't have come near me. Now, get out of here. Hey, what's all the whispering about? He owes me some money. We were playing poker the night he got thrown in here. Well, I guess I'll have to wait until he's out. I guess you will, Mark. You better start adding up the ingress on it, because he's going to be here for quite a spell. Well, thanks for letting me talk to him, anyway. Don't forget your gun, Mark. I got it. You friend of yours, Bull? No. Just got in a poker game with him the other night. Jeet, who'd you change your mind about? I tell you, it's no use. I don't. There you go, Shrem. It's on top of everything else. There's another prisoner for you, Jake. Hey, it's Shrem. Just let me go this once. I'll promise I'll never do anything crooked again.  I can't change the law, Shrem. Well, I wanted another piano so much I would have returned it to you, and I swear I would. Open the cell, Jake. Hooray, Sergeant. Here's company for you, Bull. Well, look who's here.  Why'd you have to break my piano? If you're going to room with me, you'd better watch your tongue. No rough stuff, Bill. See you later, Jake. I'm in a hurry. Hooray, Sergeant. Have you had any selfish room? No, I... I don't think I can eat. I better get you by to something. I'll be right back. What did they throw you in here for? I wanted another piano. I couldn't wait until I made enough money again, so I... I stole it. You?  I got it all back again. Now they say I'll be in jail for six months. It isn't fair.  Well, I sure hope you get out.  When Jake comes back, I'm making him open a window. I shouldn't have tried to take that money. I'll die if I have to stay cooped up in here for six months. There's... You and Jake are old friends, ain't you? Yes. That's why he's so worried about me eating tonight. But Jake's too honest to let me out.  I'd leave the country if he would. I'd never come back again. But tell him that. I'll keep at him. You never can tell. Shrimp, you better get a few things straight around here. What do you mean, Bull? For the last few days, you and Jake have been whispering to each other when you thought I was asleep. No, we haven't, Bull. Don't lie to me. I saw him give you something just before he went out. It wouldn't happen to be a key to the cell, would it? A key?  I just have to see these old handcuffs. They were hanging on the wall. They're just something to fool with. I get tired of sitting around all day. You and Jake are up to something. Listen, Shrimp. If you think you're going to get out of here without me, you're crazy, see? He gave you something besides them handcuffs. No, Bull, no, it didn't. You're probably just waiting for me to go to sleep. Jake's gone for the night, and you're planning to sneak off without me.  I ain't planning any such thing. Well, I'll fix that. Give me them handcuffs. But what are you doing? We're handcuffed together. We'll pull our cots side by side. Then just trying to ski without taking me with you. But, Bull, we can't sleep handcuffed together like this. That's right. We can. Ain't you got any better ideas? Why should I help you escape? You're the reason I'm in here. If it hadn't been for you, I wouldn't have stolen that money. You ain't leaving it without me. That'd take your choice. Well, I guess I might as well tell you. Jake did give me a key. It's here, hidden in my pocket. Why, you little... I could have taken that away from you. I knew that, but now you can't go without me. Boy, you... We've got about four hours of darkness. We've got to get someplace where we can get these handcuffs out of us. That could be that key we're starting. Please don't go so fast. I keep stumbling in the yard.  For what, for you, I could use a canoe. I can't paddle with you handcuffed to me. If I can only get loose. Where are we going? We've come so far. Turn left to those trees. Not so fast, Bull. Hey, well, there's a cabin hidden away back here. I told you, I knew where I was going. If I'd open up, it's Bull. It's Shrimp. I had to bring him. We were handcuffed together to get a file and cut me loose. Bull, where are we? He's sleeping down in the boat. There are the lanterns. They cut me loose first. Then we're getting the furs on the boat and shoving off. Are you going to take Shrimp with us? No. I'll tend to him. I hear the file is loose. We can get about six hours' start before they miss us at the jail. They'll have all the trailers. I couldn't use the canoe. I had to walk along the bank of the river. Bull, please don't kill me. It was on economy. You escaped, remember? You wouldn't do it?  As soon as I'm loose, I'll take care of you and your fishy smell. I've been locked up with this little mackerel for three days. Now hurry, Mark. It's almost five. What's the matter? Sergeant Preston, I'm glad to see you. Nice job, Shrimp. You mean this little runt was in on it? Take it easy, Bull. My dog's watching you. Are there any more of them, Shrimp? They said something about Pete. Down in the boat, sleeping. Oh, Pete, eh? Jake.  I'll handcuff these two and then join you. Will you get him, Sergeant? I was afraid you couldn't trail us in the dark. We couldn't have done it without King, could we, fella? You and your fishy smell, that's what did it. Well, that was part of the plan, Bull. Mark, you can finish filing those handcuffs now and let Shrimp loose. I'll put another pair on you and Bull. All right.  I should have bashed your head instead of handcuffing myself to you. We planned it the other way around, Bull. When you took the key away from me, I was supposed to snap the handcuffs on, but you saved me the trouble. Maybe next time, Bull, you'll think twice before you smash a piano. There's a nice reward for these stolen furs. It'll be yours, Shrimp. Oh, it ought to be yours and King's. You thought of the plan and King found us. No, Shrimp. You take that money and buy yourself a new piano. Our reward will be seeing these cooks behind bars These copyrighted dramas originate in the studios of WXY Z Detroit and all characters, names, places and incidents used are fictitious. They are sent to you each week at the same time. View holders speaking, this is the Michigan Radio Network.


What's going on guys welcome to a new video so today then I wanted to discuss with you the kind of products You should be selling when you're drop shipping or more specifically then the kind of price range You should be selling your products in so I've been talking to a lot of you guys now through the one-to-one calls I've been giving away a call on every single video for quite a while now So if you do want to enter the one on this video by the way Make sure you like the video and leave a comment down below and if you commented on yesterday's video Then just make sure you stay tuned to the end where the winner will be announced So I've been talking to a lot of you guys then through the one-to-one calls and everybody seems to be selling the same Kind of products in the same kind of price bracket or at least products in the same price bracket And what and the kind of price bracket I'm talking about then is under 20 pounds Now in my opinion The products can work, but I think you're really going to struggle to to sell them profitably For a sustainable amount of time So when I first start dropship and then I sold really cheap products they were in kind of like the five ten fifteen pound range and Within six months then I soon became it soon became really difficult to sell those same products And make a profit essentially and it wasn't until I started selling products a bit more expensive than that That I was able to actually build something that was sustainable and profitable So in this video then I just want to take through kind of like the pros and cons of selling cheap products Versus products a bit more expensive. So When I say low-ticket items then I typically think of products kind of less than 30 pounds And then high-ticket for this video. It's just going to be anything above that. So 30 pounds plus So that's the top of the video then we're just going to be going through the pros and cons And I'm just going to be showing you some products examples as well Just so I don't leave you kind of high and dry It's one thing to tell you to sell more expensive items But unless you know the kind of items I'm talking about then it's not really going to help you much So I'm going to be showing you some products examples as well. So that being said then guys, that's the top of the video Thank you for tuning in. I hope you enjoy it. Let's get into it So I've got my phone in front of me then we're just going to be working through some notes And one quick point I want to mention then is that drop shipping has evolved a lot since I started three years ago So I've kind of had firsthand experience at how things have changed or at least not drop shipping Actually when I say drop shipping I really mean Facebook ads of Facebook ads have just got a lot more competitive And therefore a lot more expensive and the market changes so people react to different kind of ads different kind of products And unless you adapt to those changes, then you're just going to be left behind So everything I talk about in this video then is current for now in comparison to what it used to be so high-ticket versus light-ticket then I'm going to start with high-ticket because In my opinion, it's a lot easier to make money But there are still some cons that you need to be aware of and if you're brand new to marketing Then it may not be the easiest thing for you, which again, I'm going to go through So absolutely the biggest thing then with high-ticket items is their bigger profit margins So if you sell products such as drones, you can buy a drone a pretty decent one now on that express for about 20 30 pounds and you could probably sell that on Facebook through Facebook ads for double that if not triple that So you're looking at maybe 60 pounds profit margin per Per sale minus your Facebook marketing costs and if you just compare that to a low-ticket item So the other day I was looking at somebody's store and they were selling like animal jewelry And they would leave in kind of like a range of anywhere up to about seven pound per purchase If they were to make a profit and that didn't take into account anything else like the Shopify subscription plan or Any app costs or anything like that. So at any time you can increase that margin For your marketing cost, then it's only going to pay dividends in the future because a good business is a profitable business So the more profit you can make then the more healthy a business is going to be So second thing as well is that you don't have to sell as many items to make as much money So if you're selling a high-ticket item with 60 pound profit margin You'd have to sell say 10 low-ticket items to make the same amount of money if you're making six pound per item and that's going to be a good thing as well for your business because Number one, there's going to be less amount of returns It's going to be less problems to deal with less customers to deal with and less orders to fulfill So it ultimately it's just going to be a more slick and efficient business to run Which is again is always a good thing and a good sign of a healthy business But then kind of on the flip side of that as well Because I don't want to make you aware of the pros and cons Is that if you do get a return then it's going to have a bigger impact on your business? Especially if you're trying to do it with a lower budget So if your budget is say 300 pounds and you're selling an item that is a cost to you a 30 pounds Then obviously if somebody wants to return that and they want their money back then that's a lot You have to refund the customer if you're selling it for a hundred pounds plus that's kind of like a 30 pound Cost that you may have to absorb if your supplier isn't willing to absorb it themselves And that kind of leads me on to the next point, which is If you're selling more expensive items, then making you sure you have a good supplier is key because Any any return is a significant impact on your business So this is where key a good supply will pay off because items aren't going to go missing in the post Any damages the a decent supplier will deal with with returns, etc. Those sorts of things So basically when it comes to selling high-ticket items, you can't just go with any old supplier You have to vet them out and the longer they've been in business the better reviews The more the quicker they return Messages things like that. Just make sure you go with a really good supplier And then the final point as well is that when you're selling high-ticket items, then you need a decent website You need decent social media profiles and you need a decent following as well because I don't know about you guys, but I certainly wouldn't be Willing to spend like a hundred pound plus with somebody who had zero followers on social media or zero reviews Or if they had spelling mistakes on their store or if the layout wasn't kind of all in line Or if they had pixelated images just anything like that Basically the more expensive your items are then the more professional your website has to be and you only have to go out and do a bit of research online to see that so If you go and look at Say Rolex's website and then compare that to a cheaper brand like Casio's then the Rolex website is just so much more professional and Just kind of it looks more expensive and so it should because obviously Rolex watches are a lot more expensive And it's the same reason the same kind of principle If you're selling more expensive items, then you need a more professional Slick and decent looking website so moving on to low-ticket item sensor typically anything under about 23 pound Now there's a lot to be said for these because As a new marketer somebody going into facebook ads and advertising products and low-ticket is definitely easier because it's There's a lot There's less checkboxes you have to tick to get somebody to spend Say 15 pounds then you do to get them to spend a hundred pounds So when it comes to things like designing your store putting a store to get our product images creating facebook ads and social media profiles Then it's a lot easier and quicker to do When selling low-ticket items so the number one pro then obviously is there easier to sell so for a newbie Then it can be definitely a more favorable way But however at the end of this video, then I am going to give my strategy and or at least my advice to what I would Suggest a newbie do and what kind of what kind of approach they would have would they sell high ticket? Or should they sell low ticket so moving on then the pros and cons are kind of like pretty much the opposite to high ticket So returns aren't as important because they're not as expensive you're talking maybe one two three anywhere up to kind of five pounds per cost for the product And then on the flip side against high ticket there's going to be more customers to deal with More potential problems if you're shipping out a hundred items versus 10 items Then the chances are there's going to be more problems to deal with with those hundred items And then kind of what I've already mentioned is that it's an easier route for less experienced marketers And also you can get away with having a less professional looking store So they're kind of like the pros and cons of high ticket low ticket in terms of My strategy or advice to new marketers people new to Shopify new to facebook I would suggest then that you start in the price range of anywhere between kind of 30 and 40 pounds Maybe even 25 so let's say 25 to 40 pounds because What this is going to do then is is you can find when it comes to selling products Then it's all about perceived value and what I mean by that is that You can find pretty decent items on aliexpress that are a cost to use going to be about five pounds but then the perceived value is Probably well within that price range so anywhere between 25 and 40 pounds and that's going to leave you plenty of room so anywhere between kind of like 20 and 35 pounds cost per purchase so even if you do Achieve a cost per purchase of those kind of high numbers Then it's still going to leave room for you to at least get your money back So you can continue advertising and continue putting more money into your business So one thing I always say to people on a call is that When going into any business a business is a marathon not sprint So don't be in a rush and be in a hurry but never be in a rush because if you try and rush things too quickly Then you're going to make mistakes you're going to cut corners and you're going to be too eager and you're going to overlook things So just don't be in a rush. So to finish off my strategy then or advice towards beginners Choose products that you can sell anywhere between kind of 25 and 40 pounds and make sure you choose a quality product so What I mean by this then is it comes back to that whole perceived value so I'll put some pop-ups on the screen now of Like low-ticket kind of cheap items that you wouldn't be able to sell for anything more than 20 pounds just because people look at them and they know or what to expect from it and they know Now have a figure in the head of what they would pay for something like this And you've only got to look at the items and people aren't stupid Even if they love an item if they feel like they're being ripped off Then all they're going to do is go somewhere else To buy it. So you may have heard me talk about impulse buy products On this channel before and 25 to 40 pound definitely falls within that category And as long as you make sure you tick all the right boxes for your customer They're not going to go elsewhere and try and find it somewhere else So as long as you have a decent store And the product is priced correctly and there's decent images to make sure it looks like It should match the price you're selling it for then you'll be good to go So in terms of cheap low ticket items then You've just seen the ones that I would would recommend you stay away from just because people know If somebody can price it by looking at it then You can get you can get away less with how expensive you price it if that makes sense So for example, then these cat earrings now I'll put them pop up on the screen Somebody could look at that and they know their plastic They know they're cheap and cheerful So you try and sell those for anything more than 20 pound probably 15 pound people are going to know they're going to be ripped off And it's the kind of products because it's so easily identifiable They could just go on google and search cat earrings and the chances are It'll be one of the first few pop-ups that they see So choose some more quality products then and what I mean by quality products is Products like these ones that are on the screen now So ones that people know that a lot of thought goes into them a lot of design They make an impact on somebody's life So they make somebody's life a lot easier or if it saves their money Things that people are going to be willing to pay a decent amount of money for and in return It's just going to be easier to market because you're going to have that room For that extra marketing cost. So another thing then I recommend to people when I talk to them is that Try and find something that people try and find a product that people know is really expensive. So 4k action cameras and everybody knows of gopro and everybody knows you're looking at probably like a few hundred quid for a gopro camera Whereas you can go and aliexpress find something that will do more or less the exact same thing for a fraction of the price so They can see something that they've always wanted that's hundreds of pounds But then if they see your ad that's essentially doing the same thing as a gopro But they can get it for a tenth of the price so you can find pretty decent action cameras now on aliexpress for like 40 pounds Then that's going to be a huge thing in the customer's eyes and that's going to That's going to be really attractive to them because they know it's an expensive product So they expect to pay Quite a lot for it But because it's so much less than what they actually thought so in comparison to a gopro Then they'd still be willing to pay say up to a hundred pounds if that makes sense Any questions at all on any of this by the way, and then feel free to leave a comment down below And then kind of like with one final point then to finish off this video So 25 to 40 pounds is the range I would suggest starting in Choose a quality product like the ones we've just discussed And then once you get a bit more experience behind you once you've started dabbling with facebook ads And you start to understand them you start to gauge And just grasp an understanding of how essentially people will react to different products Then start moving into the more expensive ones once you spend a bit more money under your store may be paid for a decent theme And you've got something really professional and good going then start moving into the more expensive products. So That anything it's kind of 60 70 80 even a hundred pounds plus And that being said then guys that covers all the points I wanted to go through on my phone if you're still watching Thank you very much Hopefully you guys enjoyed the video if you did all I asked then is please do leave a like And if you want it to be entered into the raffle to win the one-to-one call Then just make sure you leave a comment down below as well And that being said then guys let's get into announcing the winner of yesterday's video here we are then guys I'm a previous video. So if you do want to learn a strategy I use for retag to ads And then just make sure you go back and check it out So i'm just going to take the url then here at the top head over to the random comment picker url in there get youtube comments And let's start the raffle then see who the win is going to be this time So the winner of the previous video then is steven These are the type of videos we love facebook is the number one thing we most struggle with So I hear you loads of people tell me that same thing. So I've got loads of content coming on that So make sure you reach out then instagram is definitely the best place send me a dm And we can get that call arranged So that being said guys that's the end of the video If you'd rather just book a call and get straight to it then there is a link down below in the video description And that being said then guys, thanks for tuning in. I hope you enjoyed it and I'll see you all in the next one