Hi, welcome everybody. I'm gonna do a quick poll to see where you're from so that we can start breaking the ice. But welcome everybody. Good morning, good evening, good afternoon, whichever part of the world you're listening from. And I have, okay, about 30, 35 minutes, I can go over a bit after that. I have a hard stop at 11.15, which is my local time. And the way I want to spend the 30 minutes is start with a presentation that's 5, 10 minutes, like walk you through what does it mean for a PM jobs in ML and AI space. And then probably open up the floor for Q&A discussion if there are any questions. So I'm gonna start sharing my screen. All right. So I can see my own screen that I'm sharing. So just want to make sure that if others are also can see the screen if, okay, cool. All right. So as I mentioned, I want to like quickly cover a few slides and just kind of open up for the Q&A or discussions and anything that I can answer from my side. So obviously, I'm going to talk about PM jobs in ML and AI space. A quick thing about myself. So my name is Kapil pronouns he is, I'm based in Seattle. And if you would like to connect with me on LinkedIn, my LinkedIn handle is Kapil 528. Again, it's Kapil 528. And then I'll be happy to, you know, like get connected, stay connected, and kind of like help whichever areas I can. And the, I have pretty simple mantra to kind of like spend most of my workdays including also weekends is products like I'm very passionate about how product products work, like how they make our life better or how they solve our pain points. And they're not necessarily software products, they could be, you know, like hardware products, services, tangible, intangible, all of that, right? Like that's how it helps me to stay more product management, product management oriented in my career as well as life. I'm super motivated by people. So one of the pictures from my team outing. So always, you know, like keep learning from people and keep giving back to the community. And one of my hobbies is travel. So I've been all over the places and my favorite city to visit is Montreal, Canada. And my favorite place to visit in nature is a crater lake Oregon. And just a quick call out that all the material I'm presenting, it's solely my personal point of view, general knowledge, and does not represent any company or my current employer. All right. So I'm going to connect three dots for us is like, what is the ML and AI space? And what are the PM jobs in ML and how to, you know, like go about it, right? So there are many challenges adopting ML in many, many companies, and I'm not going to go through the entire list. But you know, like just to throw some keywords, right? On the left side, we have things those are part of the job and the job description rule and things that are a product manager in ML space or not only product manager, but anybody in ML and AI space data science space, they have to deal with or it's part of their daily job. One of the big call out I want to do is the implementations, right? So the data science and ML is applications are way different than traditional software engineer, primarily because the data signed expertise is needed. You have to know what is the object function you're going after and how do you achieve the model accuracy? How do you remove the bias? How much bias is still okay? And all those things, you need like a special expertise in that area. So that, you know, like that way adopting ML is not just, you know, like adopting an open source from the internet. And another quick call out is the value, right? So it is very difficult to maximize the value delivered because, you know, like it's such a complex lifecycle in terms of maintaining the ML models. And there, we will cover in the subsequent slide what are these life cycle and what are these aspects. And all of that needs product thinking. Like you cannot say that, oh, this is very data science heavy. This is very technical. This is very ML science. This is very data engineering heavy. You still need product thinking because you need to achieve that speed of delivery. You need to achieve that quality of product. And for any product you talk there are usually three piece people, product and processes. And that's why, you know, like the product management role in AI and ML space definitely is a valuable one. Another one is that like, you know, like if we take the previous slide and kind of like create a word, a cloud from it, it will look something like this, right? There are challenges when, you know, like you start solving a problem with ML, but how do you scale it? How do you put it into the production and put it into production or test it out in a wild life traffic? It takes a village because, you know, like, you need to consider all of these things as a product manager, right? Or even like somebody's working on a ML or AI based product or a solution. And the highlighted keywords are essentially, you know, like represent some of the common themes across the many industries, many different the solutions, like stability of your life cycle, stability of your model, cost has been very, very important, right? Okay, so I think that's here. Like I see Felix is asking, you're kind of covering a bit now, but how about the domain knowledge? So that's, I think, yes. So the idea is here that the ML space and how it's related to the product manager. So now the, let's talk about the domain knowledge for the APNs, right? So, you know, like if you search sample job listing from like internet, these are the four different job listings that I was able to find. And if you see that like what they are asking for, what are their highlight key points? So, like, if you take this and convert into the main key points, like how do you go about building the domain knowledge as a PM in the ML space? So this diagram here represents like how a product manager can be somebody who is very generic product manager to a domain expert, right? So in PM basics, like obviously, like you understand the product, you understand the domain, you understand the jobs to be done. So when it's, when I say domain, it's the industry, it's the customer you are serving for, whether it's travel industry, insurance, or the, let's say streaming industry, or even, you know, let's say, education, right? So you have to understand the domain, you have to understand like what is your user base, what problem you're trying to solve, and then jobs to be done. Like what is your job on that particular team and what, what you're trying to solve. And then, you know, like as you go from top to bottom and left to right, that's where, you know, like you will have to find like how you can become that go from level of, you know, like the basic to confident to advance to master in terms of product management. So I want to quickly focus two main things, the ML and AI space in terms of product managers. So as a product manager, you always try to achieve like how you can become basic to expert. And that's why I wanted to cover these earlier slides, like how you became the expert in your domain, right? Then understand the ML models and lifecycle, like what are these, like how do you develop, how do you train, how do you test, how do you deploy to production, how do you measure the output, the outcome of your model into the production. Then what are the feature sets, ML concepts are needed to make your model better and keep iterating it. And while you're doing that, there is a cost associated with it. There is a opportunity cost you need, as I mentioned in my previous slide, you need experts. The experts are very expensive, right? In terms of like their time, like how much they cost to any company. So I think in that sense, you have to know how to go about observing, you know, like your metrics, your KPIs and cost and the data, data drives enterprise, no ML science, no data science project will ever begin will ever be complete with data, right? So you have to understand what type of data you need, where is that data situated in your company or even outside the world, like for example, Kaggle, like some public sources, and how do you channel that data, like there's data governance, there's data compliance, then you have to understand the difference between PCI, PII, like data categories. And then as a PM in ML, maybe it will be your direct responsibility to, you know, like come up with data products or knowledge graphs, or you will work with peer products, peer teams or sister teams that builds this data products that builds this knowledge graph. So you have to understand like how that knowledge graph fits into your requirement and how that can be fit to the model. And because, you know, like there are many, many teams responsible for like data model development, model deployment, you have to understand the dependencies, what are the SLS between dependencies, like how do you extract, transform and load your data, and channel it to your team.  But when we say PM in ML space, you have to understand like how data solves your problem and how that leads to the second part, which is analysis and BI business intelligence. Again, you may not be directly responsible for doing analysis and BI, but you will be working with these folks to understand any test and learn you're trying to do, what is the power analysis output, what are the impact, what are the metrics you're going after. So that's, you know, like how the product management and the ML space are intertwined with these are. The last one I wanted to cover is like how to go about preparing, you know, like if you are in the process of going through a job in PM in ML space or you are trying to switch the domain, you're trying to switch the job, then this is where, you know, like just like a quick guide, do your research, like about the job description, job description tells a lot about these slides we've covered, right. So for example, these job description, like what is the expected out of you as a PM, whether it's a domain expert or a generalized product management, like, and all those things, right. So understand that from your job description. Do some research from social networks, such as, you know, like LinkedIn and other ones, so that, you know, like you understand, like what is the social space about that problem, who are these people, like, can you try to see their profile, like where they come from and all those things. If you can find an internal connection working in the company, that's, you know, like amazing resource to find out more information about the company, again, the people company, and then tune your resume based on what you find. It's not about like hide and show certain things, but just basically how do you, you know, like strengthen your resume in the direction of what you find about the job description. Second one is the hiring team, like leverage the hiring team, a lot of hiring team gives you a lot of information, you know, like preparation material and all those things. So use that. Use the screening call with the hiring manager as the medium to find more about the job, like why, what you will be doing and many, many times, hiring manager are super, super helpful because they genuinely want to help you. So, you know, like use that connection and use that screening call gaining more information about the role. The next one is the interview prep, pretty generic, you know, like practice your intro, practice the envelope questions, and be ready to share your work within permitted criteria. Like if you have built something amazing, and then if you can show it to the world, be ready to share that during the interviews, during the hiring process, so that, you know, like you can, the picture is worth 1000 words, right? So that's the idea behind it and ask questions like many, many questions, more questions you ask, better you get an understanding about the role. And just want to do a quick cap here with my presentation is the cards in your sleep. I usually tell people there are two cards in your sleep. Like one is that if your previous or current role is a great overlap, then you speak to like how the learning code will be smaller and how you can make the impact right away and vice versa. Like if you see a gap in your skills and your experience from the job descriptions, speak like how you will address those. So that's, you know, like I had in my presentation, I'm gonna, I'll try to answer the question as much as possible, but let me see if I can open up this for, you know, like a forum and then we can talk, take some of these questions. Can you guys, can one of you speak and see if I can? I'm not able to hear anybody. I think I'm live. Hi Kapil. Yep. Thank you. Yeah, I can hear you. Thank you for the presentation. It was very valuable, especially in the field of machine learning, which is so specific. My question is about working with data scientists, as you just mentioned, they're very valuable, expensive resources, but at the same time, like a good proper model deserves a lot of data cleaning and data cleaning can take like really long or so how do you define like this deadline to say, guys, enough is enough, we gotta deliver, let's run the model as it is, or it's better to take longer to sharpen and have a better precision, accuracy as a response to the predictions. Yeah. That's a great question. Juliana, am I saying your name right? Juliana actually. Okay, cool. Yeah, Juliana. Yeah, that's a great question. I think the answer to some extent depends. So for example, as a product manager, you wanna test as early as possible so that you can find out whether you are on the right path or not, so that you can course correct very, very quickly. At the same time, you might, the stakeholder and your user or you yourself might get discouraged from a very mediocre output of the model, which is paid on smaller data and course start problem. So you have to find what is your story, like how are you gonna break that story to your leadership, your team, and your stakeholder saying that, hey, like, this is the north star, this is where you're trying to go. And if you walk backward, step one, phase one, limited data, it just to try out and find the signal. And if that story sticks, then you can go to try things out. If not, then you have to push, usually there is time, resources, and scope. So you have to always balance that equation. So come up with your story, find out what are you after. And if you know there is already signal, you're on right track, and you need more time, then you have to work on your story to push back and get that data. And that's one of the things I like, PM in ML and AI space, that it's such an amazing space problem to solve. But as a product manager, how do we tell story? How do we connect stakeholders who are less technical? How do we interpret the model output? I think that's the key. And you have to choose what is your circumstances. And based on that, either you roll forward with limited data, or you push back with your story saying that these are the reasons we're going to push back. Great response. Really, I forgot to consider the stakeholder is the definition of how accurate you want. It's really going to depend on the problem. Super thanks. I appreciate it. Thank you. Any, anybody else, comments, questions, ideas? Yeah, I guess I'll go. One of the things that I think I've been struggling with is that I kind of had some limited exposure in some past product roles into ML applications. But I think, to me, the biggest gap I've seen in maybe maturity of the company was there is knowing more about deployment, model deployment, and lifecycle management. I feel like that's an area I don't have a lot of experience with, understanding precision recall and model performance that I understand that I've found resources on. But where is a good place to get resources and kind of increase my adaptive expertise when it comes to model deployment and management? So I am prepared to have those conversations when I'm interviewing. Yeah, I think thanks, Kenneth. That's a good question. So in terms of how do you get expert, you have to constantly keep learning. You have to keep trying. And as a product manager in the ML and AI space, I think you are hitting a very good point here that how you can become hands-on is the key. So it's like, oh, I just don't care about the model is being trained. Okay, that's fine. And what's next? So if you become that expert, you understand, okay, you can ask a better question. For example, if your data scientists say, I'm a training model, you're going to ask questions like, do you have enough data? What volume are you going to train? How long your training exercise is going to happen? And all those things. Same thing with model deployment. Like, is it proud, non-proud? Like, are we deploying on the like new version, new model? Are we A-B testing? Are we running multiple models? All those questions, you will build muscle memory, you know, like more and more hands-on you do it. I'm happy to connect with you, like on LinkedIn, or like my email address is like me at my first name, lastname.com, which is me at CapriGangana.com.  It will be tough to, you know, like give you very specific focus answer within like a few seconds. So happy to chat more and like how I can, you know, like get you more involved into learning these things. Thanks, I really appreciate that. Hey Kapil, thanks for your presentation there. MLAI is kind of a, I don't have experience in it. I've been product manager of B2B e-commerce and web kind of projects for a while, including, you know, several credit card journeys and those kind of things. So I bumped into this session and I found it very interesting. So being a non-technical guy, I pride myself actually more on the UX and the business side of things and business side of product management, really getting the stakeholders and analysis and understand the problem and then coming up with the solution and working with developers to solve that. So if I was curious enough for this MLAI and I'm kind of shying away from it, thinking it's way too technical and it's too much, do you think that's a fair thing on my part or should I really go into it because it's maybe the new thing or the evolving thing or already evolved? Like am I increasing my career scope by going into it? And if so, then how do I go about it? Like, you know, can I start at the junior level in that MLAI PM while I'm a senior PM in here, would the company consider me for those roles or I will be way too under qualified for that? Yeah. So I think that's a good question, Sundip. And I'll give you a quick, like, when, before I became product manager, I was machine learning engineer. Before I was machine learning engineer, I was data engineer. And before I was data engineer, I was traditional software engineer doing Java J2 stuff, right? So even like I went through a transition. So like, how do you progress in career? Like, I think it depends a lot on your context and the company and the people around you. And that's why in my first introduction slide, I mentioned, people are very important part of our career development, right? So like how these, so for example, if you're senior at your current level, like, can you, can you learn enough about ML and just be a lateral move and you're not starting from one level, like three levels down. And that will come from the people who will believe in you or who will not believe in you, right? So I think that's the thing, like, I'm happy to chat more about my program or personal now the ML space. In my opinion, there is no field that is not using technology. There is no field that's gonna not use ML and we are about a decade away from like true AI, even though we say AI now, because AI takes tremendous amount of machine learning ML over and over and over again, to get to AI space, right? So just for the ML space, I think there is no escape from ML, right? Whether you are directly involved or not involved, right? So even let's say in your current role, you might end up chatting with the peer products or some solution, those are ML based. So having like understanding those concept might help your current job better, you might able to ask better questions, better and all those things. So I think I would say that like you regardless whether you have interest to make the career progression or not, understanding how ML work at least like ML 101, 102 level will definitely help you in your career, like regardless of what problem trying to solve because everything is moving in that direction. A lot of companies are under pressure because everybody around them is doing ML and AI. So they tend to, you know, like try to do ML. So I think if you think from that angle, having that like understanding will definitely help you. Now about career progression, I think that's, you know, like something like your personal choice for their, you know, like what curve on career you are, what risk you're willing to take and whatnot. But you know, like we can chat about that offline. But I think just to cap my answer, understanding ML and all the things I will present my understanding these keywords, like what does it mean like accuracy biases and all those things will definitely help you in longer. Very enough. Thank you so much. Thank you. Any Tim, anybody else I think that if not, I think I will be here if anybody has any questions. Payton. Sorry, my internet. But I might have missed some, hopefully I'm not repeating, but so I have experience where in my previous role, my company had created an AI ML based product for acquisition, advertising in the very specific niche I was in. And my role there was heading up our acquisition advertising practice. And so that was there, obviously my area of expertise. However, first, just out of curiosity, and then obviously because it very much pertained to my role and was essentially replacing some of the things we were doing, I got very, very involved constantly talking with our data scientists, engineers to the point where our chief data and product officer wanted to meet regularly to strategize on the project. So I have a very big interest in AI and ML, but I don't have an understanding of it in the technical sense in terms of coding it. I understand how it works. I understand the inputs and how models are being trained. But I guess I think in an interview it might be a little bit easier to get that across. But just looking at the first step, how if you're looking at LinkedIn or a resume, how can that be summarized in a way where you would really believe that? Yeah. So I think that's a good question, Edin. Am I saying your name right, Edin? Yeah. So I think in terms of learning, so my suggestion would be draw a pyramid and then understand like generic concept.  So I would suggest that draw a pyramid where you will say, I know everything about ML in terms of concept, taxonomy, definitions, life cycle. Then next level, okay, I'm not going to worry about data engineering, ETLs and whatnot. I'm not going to worry about how the model outputs are consumed. I'm going to focus on like how model thinks, right? Then that will be your layer.  I can like deliver. I know how to achieve that, right? So find your pyramid, bid your pyramid, pyramid, and then that will I think help you to, you know, like see, you know, like how to go about whether it's career switch job, like switching jobs or even working with different products. So I think that's the framework I started using like, because you cannot learn everything while your job entails to ask you something, right? So I think that would be my suggestions. And again, like happy to continue chatting either on LinkedIn or direct, you know, hey Kanukriya. So thank you so much for your time. So I'm looking for a product management opportunity.  Yeah, short talk is what I should ask. Yeah, I mean, like, there are companies hiring. So I saw people somebody posted an air table, what do you call a chart what companies are hiding, as far as like my current company, like I'm not on the hiring team, as well as I don't know anybody is, but you know, like, I'm more than happy to connect to you with the, the my social connection who I know are hiring. And then you're like, we can take it from there. Yeah, I see the exactly same word. Katya posted on the chart, like, that's the air table link I was mentioning. So can I have your email address? Yeah, it's me and e at Kapil Gangane.com. I'll type it in the chat. Okay, it's not chat box on my side. Oh, so it's, I can, I can spell it out. It's me and e at me, like, yeah, I can. M as in Mary, E as in Edward, at K. A. P. P as in Peter L. Gangane, G. A. N. G. A. N. E. Dot com. K. A. P. L. G. A. N. G. A. N. E. Dot com. Yeah. And also like, Katya posted my link in profile. So if you connect me there, I can also share some of the resources. Okay, so it was beyond the initial pages. Yeah. Thank you. It was nice listening to you today. Thank you so much for that. I'm actually not so much into MI or ML or AI, but I wanted to ask you a few things about just baking into product management.  So I would be as I'm applying for jobs starting Jan, 2023. And one question I want to ask is now I've read a lot of things and job descriptions where they required a lot of technical knowledge for product managers, but it's not possible to have all the knowledge. So it's really overwhelming for me in terms of preparing because I have had no prior experience like work experience. So I'm just feels like I'm trying to learn everything just because it's expected. But what actual technical skills are really required while working in that role? Yeah, I think I don't know at what point you joined the conversation. But my last slide, where I mentioned about the preparation guide, I can share the deck with you. But in I think the first square, when you do research about job, try to find the keywords from the job description. And usually those job descriptions, it's like I ask you a lot. And then try to create a heat map. Of those keywords, what is the primary role they are looking for? What will be your responsibility? And then if you see that, oh, do you check all of them or most of them boxes, that could be a way to go about it. And then like one of the cards in your sleeves could be like, you know, like whatever you've done so far, if it overlaps, try to exploit that saying that, hey, I can deliver this, I've done it, be confident about it.  But you can also do like the other learning sessions, you can also start doing something hands on, like build a product, right? They always say it. So that could be one way. And as I was mentioning to Eden that draw a pyramid, like of the skills that are needed. And then like the, what do you want to be generalized and how we go up to the specialized one, right? So as you mentioned that like maybe ML and AI space probably is not your first go to step, like you want to just break into the product management, right? So like getting those basics down, then step, step number two, like, like, how do you apply your PMB six to the job, like jobs to be done, right? So if I share my screen again, so that we can do this guy, right? So if you see this slide, this slide talks about the initial first one is PMB six, like it has nothing to do with ML and AI, it's about product management in a frictionless world, product managers don't have job. Our job and responsibility is to either eliminate or reduce that friction. And that's what covers in a basic PM, right? Understanding job, understanding domain jobs to be done.  Then like, how do you stay on the basic side, but understand basic. So this grid here is, you know, like starts from how, from a generalized, all the way to how you become specialized understanding ML and data. So I think that would be strategy I would apply, like understand the job, the role, do your homework, like make your help your connections, use your connection to help you understand more. And I think based on that, you know, like you will start breaking more into it. Again, happy to touch base offline and walk you through like the process that work for me. Okay, yeah. Yeah, that sounds good. Thank you so much for that. Anybody else? Hi. Can I ask a quick question? Hi. Thanks for taking the time to share your insights. I have a similar question to an earlier question from Sandeep. Basically, I am trying to transition into a PM role from a non technical background. And I'm curious from a hiring team perspective, whether I'll be seen as more competitive in applying to a associate product manager role or any entry level role versus trying to work on those pyramid and try to apply to a more senior level roles? What would be a better strategy, especially in terms of this recent layoffs from all these tech companies and the technical PMs coming in? Yeah. So I think that's a good question. I, so my, there is no easy or straightforward answer, but this is my personal point of view that the job interviews treat them as a two-way street, right? So it's like they are asking questions, but you are also, you know, like evaluating them, whether this is the right role for me, this is the right team for me, like whether this is the right setup for me. Like most of like, as I mentioned during my presentation, most of hiring manager are very, very nice. They're trying to help you out. They're trying to give you hints, right? So like, ask them direct questions like why, like, hey, you know, like, I've done this and I think this will be applicable to this job in this role. Do you agree? Do you disagree? Like, what do you see a limitation in my candidacy to this role, right?  And then like, you get a better at it. And like I said, treat it two-way direction and ask them questions like whether what is lagging, how you can fix it. And again, like, repeating the same message, card in your sleeves, how your previous experience you can apply in technical world, because end of day, like you are here to solve the problem, you are speaking on behalf of customer, you are speaking customer's language, you are data driven, you are data informed, all those things, technical, non-technical, they're common because those are fundamentals about how to go build a product or solve a customer problem or a user pain point, right? So I think like, draw those commonalities and use them as your strength and then add your bits like your personality, you know, like your energy, like all other things you can bring in from non-technical world, still applicable or add on to the technical side. And as I was saying earlier, like, you have to keep learning, whether even like technical field, you have to constantly keep learning, keep honing your skills. So I think that learning curve is always constant regardless whether you're technical or non-technical. So those are the key things, you know, like, again, like, happy to chat more, it just, you know, like difficult to give answer in specific, like time said, but like, just to reiterate and recap, treat interview as two way, to a street, ask questions, exploit what you have done, what overlaps with you, and ask them like how you can, like, what are their concerns in terms of gaps, and just kind of work it out and then like, be a good storyteller because as a product manager, that's what it, we need, right? And you can learn this technical stuff. You can learn storytelling, you can build the product management or muscle memory, and it's a learning curve. How do you tell your story to the hiring team is really important. Thank you. That's very helpful. Thanks. Um, actually, I have one more question. If you don't mind. Can we give Felix a quick opportunity? Sure, sure, sure. Yeah, yeah. So, first of all, thanks, Kapil. I also love that the slides seem to be made with AI. I'm kind of wondering there, do you have any sort of top tips for top technologies to look into that would be, you know, big in a few years for PMs or generally, like, is there any specific technologies you think we should be aware of? Um, specific technologies. So, I think a couple of stuff, like, I'm not sure I'm that expert in terms of, like, telling you like, hey, Felix, go and learn this. But look for the things that, for example, chat GPD, right? Like, everybody went crazy or like, chat GPD, like, oh, this is like AI bar that has a like, philosophical context and all those things, right? So, I would split into two parts. One, what's going viral and why it's going viral? Because a lot of times that virality has two aspects. One is the social aspect and one is the data loop because it's like, it's going viral because like, one thing is leading to other things and dominant effect. So, that would be my, one go to things I would say, right? And another would be like, just try to see, like, how these big companies launched the product. For example, Snowflake, Databricks, for example, or like other cloud based data warehouse.  Then also learn, like how, for example, one of my favorite products is Notion, like how they came about, like, adding these templates and they started, you know, like offering their products to the companies. And then what is the, like basically dismantle a product that will tell you the problem and the direction they're trying to go. And then once you have that, try to see what are other competition, then that will give you the direction of the companies are trying to go. And then they are likely will use the common set of technology, whether it's a set of cloud technology, such as AWS, Azure cloud, things like that, or, you know, like the tools like Databricks, Cubo, and whatnot, or, you know, like Spark and TensorFlow. So I think that you have to basically draw a Venn diagram and see what direction they are going and try to pick interest. I just try to be generalized in terms of what I learned just like everywhere, because that helps me at least break the ice and learn more. I don't have a specific vertical domain I try to, you know, like focus on, I just try to stay horizontal as much as possible. Thanks. Great answer. Back to you. And then I think we'll cap. Yeah. So, good question. While applying for associate also, because I've been applying like all the applicants that are applying alongside, and on the LinkedIn, you can actually find like senior, how many senior level applicants are there for this position. So I find you feel frustrating that even for associate level positions, a lot of senior level people are applying. So how is it fair to us who are just entering the market or, you know, for whom the position is actually being there. So one question for you would be, do they, so the hiring manager or recruiter, do they also take in consideration the senior level applicants, because they're applying or because the position is meant for associate level applicants, they will just focus on them. I won't be able to talk to, on behalf of like all the hiring teams and hiring manager, it's just that how they see the role. And they probably are just trying to, you know, they're trying to get the best candidate from a bigger sample pool. But this is like going back to my answer to, hey, treat this as a bi-directional, the interview or job role as a bi-directional street and ask them questions like, hey, this role says XYZ and I'm associate, do you think like, are there any gaps? Or it couldn't be other way around, like the role might be asking less and you might have more to offer, right? Then play that card saying that, oh, like, yeah, I'm very excited. I'm very confident, learning curve, I can deliver impact and all those things. So I think you have to, you know, like at the end of the day, it's a game.  So you have to know what is the fair game from your standpoint and how do you go and play about it. Unfortunately, there is no easy or simple answer like how, you know, like how people apply and whatnot, but things that you can control is basically like these, right? Like do the research interview prep, leverage the hiring team, ask them more hints, sample questions. There are many resources that tell you for which company what questions they ask.  So some of the things are out of our control. What we can control is our skills, keep learning, keep trying and not lose hope.  And you will like definitely keep excellent. Thank you so much. So I think I have a hard stop now because of my daytime job. So if you have a question, do you mind posting it on chat so that I can take it offline? I'm sorry, I have requested you on LinkedIn. And I wanted to know pricing strategy or any books or courses for pricing strategies for products in AI and ML space. So whenever you get a chance, you can let me know. Absolutely. Yeah, I have a very excellent resource on the topic.  It talks exactly same thing like AI products and pricing. So I'll definitely, you know, like, send that resource to you. Can you message over LinkedIn? This chat is not accessible to me. Okay, sounds good. Thank you.


Welcome to BMC Video Solutions. In this video, we will introduce the Control-D Report Approval feature. The Report Approval feature enables the implementation of an approval and workflow process for reports in the Control-D repository. The Report Approval feature is useful for preventing reports from being published before they are approved. It is also useful for facilitating a workflow process. Typically, reports are distributed to recipients specified in the declaration mission. These recipients can view the reports through Control-D Web Access. If an approval workflow is required, specify the approval option in the declaration mission. When the approval option is activated, end user recipients can only see the report in Web Access after the report has been approved by the Approver recipient. Now let's see how a report is approved. Open Web Access in your browser. The report list displays all your reports. In the Filter Report List dialog, choose Reports for Approval. The report list is now called Approvals, displaying only the reports that need approval. Open the report you want to review. If the report is OK, choose Approve from the Report menu and enter a comment in the message box. The report can now be viewed by other users. Now let's see how the Report Approval feature is implemented. To implement the Report Approval feature, the Control-D Administrator must first define Approval Profiles. Each profile includes a group of one or more Web Access users that are responsible for approving reports. When a report requires an approval workflow, the Administrator must assign the report to a specific Approval Profile. The Approval Profile is defined in the Approval Definition panel. Let's go to the T-Screen to see this panel. From the IOA Primary Option menu, enter T. The IOA Recipient Approval Tree entry panel opens. To open a tree, type in a tree member name. The Control-D Recipient Approval Tree panel opens showing the contents of the tree member. Each tree member can contain one or more Approval Group Profile definitions. In this example, there are three definitions. Let's open an Approval Definition. We can identify an Approval Profile by the name in the Approval field. We can create a group of approvers by entering their Web Access user IDs in the Authorize fields. We can determine the relative position of this Approval Profile in the Approval Hierarchy by specifying the appropriate number in the Approval Level field. The Parent Approver name and level is also indicated. The Parent Approver must approve the report before the current approver can view their report. Lines of description can also be added, including email addresses for notification that the report needs approval. Let's go back a second. Note that when creating a new Approval Tree member, the Administrator must use the keyword CD-APPROV as the first or root parent Approval Profile Definition name. Using any other name for the root parent profile definition, we'll open a recipient definition panel instead of an Approval Definition panel. And this is not what we want now. Instead, we need an Approval Profile. Once we have created an Approval Profile, we are ready for the next step. When a report requires an Approval Process, the Administrator opens the report's Decoration Mission to assign the report to an Approval Profile using the Do Approve parameter. Let's go to the R screen and open a Decoration Mission to see an example of a Do Approve parameter. The first field is the Approval Name. This is the name of the Approval Profile definition that we defined in the Approval Tree member. When Y is entered in the Hidden field, the report is hidden from all users except the Approvers specified in the Approval Profile definition. These are the Web Access users specified in the Authorize fields. When S is entered in the Multifield, the Approval is relevant to more than one report. The Approval is defined for a specific report, but applies to all reports related to the same SIS out. When Y is entered for the Store field, the Approval Process is activated only for the report created by the Print Mission with Store equals Y. With the default N, the Approval Process is activated only on the Decolated report. Finally, to enforce a Service Level Agreement, the number of days after the report is generated that the report must be approved is entered in the SLA field. When we run the report's Decolation Mission, the Approval Process will take effect for this report according to how we defined the Do Approve parameter. Let's summarize the Report Approval Process. First, the Report Approval Workflow must be implemented by defining an Approval Profile definition in an Approval Tree and by assigning the Report to the Approval Profile using the Do Approve parameter in the Decolation Mission. Next, we must run the Decolation Mission. Finally, the Report can be approved in Web Access.


What's up everybody Jason here for Jasper's case breaks dot-com We just sold out twenty twenty three bones best university football delight to box break random first letter first name Number two number of dose again guys everybody gets around a lift from the list below just remember That's your example there for letter M anything Michael Panix would go to you All right, that's some combo letters Click the dice roll it'll be a one of four or five times one two three four five Five times five times Nick down to Peter one two three four five See down to J Nick letter C Nathan letter D Adam with SV Eric with T Omar with our Adam with N LN sorry Nick with OP Kyle with W Adam with K Adam coverman sorry with K Alan Murdoch with M Mike tower with FG Copperman you have the E all the letters lot listed Nathan with B Steven with a and then Peter with J So again, I'll leave the trade window before a little bit, but I don't know how many people are really gonna trade I think everybody likes to just stick to the letters plate safe Be for J. I don't know Omar. Did you get our again? I didn't realize you got it in the first half I mean ours are not a bad letter. This is a lot of ours but I Know it probably sucks. They had the same letter again Because you want to see if you can get something different Probably one of the bigger ours. I would say is like Ramon dunes day.  Oh, that's right. Yeah. Have you got a letter already got that Keefe, right? All right, I'm just gonna close the guys Okey-dokey Okey-dokey artichoke All right, so Again stacks of two's one two three four I roll five or six. We'll just redo it again two one two I'm gonna go these two boxes here, and then these four or six I should say We'll leave them there for another break And I reposted it already just want to guys Love to pull some bone X Any of my Oregon boys Troy Troy Franklin as well. He might actually end up being a first-round pick, too All right, how Perkins letter H with that one Which is actually Where's H? I don't see H So that'll be going to the combo all of the letters and then Javon Ballard to 50 letter J All right, there's that are Riley Leonard quarterback out of Duke Jackson dart letter J To 150 J's going to Peter ain't there's Blake Horam That's a two out of five for letter B going to Nathan. That's a sick one right there Johnny Wilson a 100th letter J And actually I'm just gonna actually just grab them from over there. Yes, can really kind of really peek up from over there Play could link Letters C to 150 going to Nick. Here we go Drew Payne out of 15 letter D Going to Nathan and we got for my ducks Aaron Whittington's nice when they're out of 50 or Noah Whittington. Sorry letter N going to cover them with the LN combo. I Think Noah actually is not declaring for the draft. I think he's gonna come back and then we have a Javonte Barnes Letter J going to Peter. Oh, Blake Horam out of five Not too shabby first box Wanted Javon Belard and out of ten Emeka Igbucca out of ten right there letter E Which is gonna go to cuperman tonight's little red two Keon Keely letter K well 98 Somebody wants that Blake Horam Chris Vizina To 99 letter C going to Nick Jackson Arnold To one 15. Oh, this this kid supposed to be really good, too Another one out of ten red drew Allard Letter D going to Nathan out of Penn State and about a little JJ JJ McCarthy out of 25 10 out of 25 and Well, why don't we just pull his autograph, right? Elements of the game to 75 JJ McCarthy going to Peter Aang and letter J Very nice. Very nice Yeah, I would agree so Nick I don't I don't really even know if you worthy of even being like a top pick And I'm speaking about JJ to be honest. I Think JJ is pretty overrated Jackson Jason Marshall out of 15 I'm not saying he's not a Michigan legend and fan favorite, but and then last one. Well patch autograph Xavier worthy Letter X which would be going to the E all other letters not listed. It looks like right I'm not saying he's not I just I don't know I mean just to be honest make you think you even transition to be a really good quarterback then if I just don't see it But hey, I mean, I'm never I'm not 100% right all the time either, right? I just feel like I don't know he's great though. I mean he has underrated speed. He's a pretty fast guy He's very mobile, but I don't know. I just feel like that Michigan team was just overall just great, you know I mean, I feel like if we had to put it in JJ's hands to win this game when a national championship I don't know if you guys win You know solid defense great running backs great offensive line some good weapons outside. I mean, I don't ask just me though But I'm not hating on him though All right guys, there you go. No randomized is actually to go up there, but nice big corn there Pretty nice. There you go. Appreciate it guys. I guess the next one's in the store. Jasper's case breaks calm


Welcome to ITU. The United Nations Specialised Agency for Information and Communication Technologies, ICTs. ITU promotes collaboration between governments, regulators, academia and the private sector. ITU is the premier global forum in which members shape and define a wide range of issues affecting the future of the ICT industry. ITU and academia members work together to produce forward-thinking and innovative ideas, shaping the world of ICTs. ITU offers academia the opportunity to increase their technical knowledge, share their expertise and access training and specialised seminars, contribute to international standards and best practices, engage in global and regional debates, become an editor of ITU recommendations, launch innovative public-private partnerships, raise the profile of their institution, get access to world-leading ICT statistics and studies, and secure internship for their students. ITU has more than 80 academia members from all over the world, including universities and research centres, such as the University of Tokyo, Japan, Georgia Institute of Technology, USA, Nanjing University of Posts and Telecommunications, China, Universidad Tecnologica Nacional, Argentina, Tor Vergarta, Italy, CPQD, Brazil, St. Petersburg State University of Telecommunications, Russian Federation, EPFL, Switzerland, École supérieure africaine des TIC, Côte d'Ivoire, and many more. Our collaboration with ITU aims at fostering productive relationships with industry, academia and other organisations. Georgia Tech Institute, USA. We're proud to partner with ITU in the areas of information technology and telecommunication policy where ITU is the uncontested international leader, EPFL, Switzerland. The work of ITU allows us to expand our horizons, not only from a technical and academic perspective, but also for networking and international relations. Universidad Icesi, Colombia. Many thanks to ITU for the guidance, for acting as a bridge to get the members together for better communications, and for acting as a platform for better cooperation. Nanjing University of Posts and Telecommunications, China. Don't miss out. Join the leading researchers and experts shaping the world of information and communication technologies. Find out more. Join ITU today.


The study identifies RNF as a key enzyme responsible for oxidizing ferrodoxin and reducing NAD during the succinate pathway into abundant bacteria of the rumin, prevetella brevis and prevetella ruminicula. This enzyme is important to propionate formation in these bacteria and may be used by many other bacteria from the environment. The study provides fundamental knowledge for manipulating fermentative propionate production. This article was authored by Bo Zhong, Christopher Linger, Hannah DeGruitt and others.


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headline news update. Good afternoon folks. Steve Rhodes coming July from the shores of Delray Beach, Florida. This is your 2 p.m. update because we've got all the U.S. indices that we track trading to the downside. The Dow's off 231 S&P 29, NASDAQ 164, Russell 19. Summai's down 32. Trendy's off 207. They're the big leader percentage wise to the downside. Spot follow totics is well above its 50-day exponential moving average. She's trading at 1851. That always is bearish, directly speaking for the S&P 500. Gold's up $4 while Silver's back 21 pennies. Like we crude up about $2, trade at $83.92. Natural gas off 45 cents. That's nearly 8.25% to the downside. Let's go spend a little time as we do during this segment by taking a look at our indices charts out here. So give me a moment. We'll switch over to that page and we'll begin by taking a look at the Dow that's in the upper left hand corner. You'll see that today should become bar number nine of a TD9 count. In order for that to happen, we need to see price close above $36158. $36158 is what we'll call it. If price close above that, you'll have a valid TD9 count. That suggests to move back to the oscillator and change line, not much further below $36131. The S&P 500 confirmed a TD9 count top yesterday and price should go target its oscillator and change line of $4656. The NDX100 confirmed a TD9 count as well. Its price target is $16087. The Russell 2000 confirmed a sell the D point yesterday with its bearer shooting star candle. That puts its price target down at $2385. The semiconductor index is forming a TD9 count today. Now you can still get a higher high tomorrow and that pattern would still be a confirmed TD9 count. Regardless of whether that happens or not, this is suggesting that price could pull back to $3625 level. That's its oscillator and change line. In the transports, she too very likely targeting the 16612 level. It's oscillator and change line. I know you're getting tired of me saying that, but that is where price is likely headed to. That's not the same thing that we have inside the XAU. The XAU is above the oscillator and change line 13309 and that remains bullish and suggests that it should move to higher ground out here. The New York Stock Exchange. So this is kind of an interesting chart out here. Why is it interesting? Well, there is no topping signal out here. Yeah, it's got a rogment and indicator signal. But right now so far, this is just an inside day. And an inside day for the New York Stock Exchange says that the trend that's in place would continue. So that's kind of an interesting message out there for the wider swath of the market. That's not the only wider swath of the market message that we have. There's one more. I'll try to get to it here momentarily. And that's taking a look at its advanced decline oscillator, which is still above zero. So the New York Stock Exchange is bullish. Folks, stay tuned for the power trading hour. I'll see you tomorrow on Wednesday.