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How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jeff Musk on Wed Nov 27 2019
Well, it's a long story, so I'll try to give you the short version. Let's see. Like I actually got my degree in electrical engineering but had a hard time finding a job in that field when I graduated. So I ended up actually getting a job doing videogame development as a software engineer when I graduated college. So I moved out to California, worked in some bold adventure games. If you're old enough, you might remember like King's Quest and Leisure Suit Larry and stuff like that. So that's where I worked. Sierra Online and I stay in the video game industry for a few years between there and a place called Looking Glass Studios and in Massachusetts. After that moved down to Florida, where I started getting involved in dot com startup that was doing some three-D virtual avatar stuff and ultimately into the military training industry. So in that space, I was working on virtual reality training simulators for the Army and Navy and Air Force and various military branches, so that kind of got me Maur into some more professional aspects of three-D rendering technology and things like that And after a few years of that I decided it was time for something new throughout my resume, and ended up at amazon.com, which is kind of high point of my career, I suppose. And that was going back almost 12 years or so when I started there, so moved out to Seattle. I started there as a senior software engineer, and I was working on what they called recommend her system. So now we're starting to get into the world of data science in machine learning. So from there I work my way up into a senior manager position over the course of about nine years. By the time I left, I was actually running the engineering department of IMDb.com, which is one of their subsidiaries. So spent most of my career in Amazon doing machine learning related stuff. Recommended systems in particular. And eventually, you know, again, it was time for something new. So we packed up and moved to Florida, and I became self employed. So today I run Sun Dogs software, which consists of both software that I wrote individual simulation arena and also education, online education in the field of data science, machine learning. So that's actually my main focus right now, that online education piece. And today I have over 300,000 students across the world online that are learning big data in machine learning stuff from me, so that's been very rewarding.

What process do you follow for creating and updating courses? How do you ensure the relevance of topics and exercises covered?

Based on experience at: Founder, Sundog Education
Summarized By: Jeff Musk on Wed Nov 27 2019
I guess I'll take that in reverse order. So, you know, before you start to create a course, it's very important to, you know, figure out what you're gonna create a course in, right. So, there's a lot of tools out there to help you figure out what students are looking for so on, you know, me in particular, they have a very good, tool for, like, measuring, interest-based on search terms on the enemy platform. And it can also let you see if there's actually existing courses in that topic space or not. So that's a good way to figure out. Is there an unmet need for a new topic? For example, to this day, that's not a whole lot of courses on Apache Spark for some reason, so that's kind of one needs that I feel there, and it's first entering the relevance. The thing is, these technologies change very, very quickly, right? So it's almost a full-time job, just keeping these courses updated as new versions of the technology come out. So that's how we keep it relevant. Going forward and the exercises, in particular, change a lot. You know, for example, we have some courses on Amazon. Web service is certification preparation, and we have a lot of hands-on activities to show you how to use the service is. But Amazon is always changing how it all works and changing what the dashboards look like with the consul looks like. So we're always going back and updating those hands-on exercises. For the first part of the question, that process we follow typically, you know, once I've identified a topic for a new course, I'll spend about a month just researching that top again depth because no matter how much of an expert I might claim to be, nobody's really an expert in technology because things change so quickly. You know, you really have to be constantly educating yourself. So that's the first step. Educate myself as to the current state of the art. The current developments in whatever topic I'm going to teach at that. After that, I'll start to put together the course materials, and you should they'll start with the hands-on activities. The exercises spend a few weeks on that spend another couple of weeks putting together the slides for the course. And then comes the easy part, actually, recording the course itself. So just going through all those activities and slides and actually recording the actual video content, and that that can also take two or three weeks because these courses tend to be about 10 hours or so in length.

What are the various student profiles who take your courses? What kind of career growth and jobs your students could get afterward?

Based on experience at: Founder, Sundog Education
Summarized By: Jeff Musk on Wed Nov 27 2019
You know it's hard to say. I mean it's tough to generalize. I mean, I see from you to me that there's about a 50 50 split between students from the United States and students that are in India. So there's a huge demand for these topics in India. Rightfully so. You know, these are very lucrative skills to have, right? You know, if you understand machine learning and big data, you know, you can pretty much write your ticket. So as far as career growth in jobs, I mean, I don't want to give the impression that just taking an online course is enough to get a job. It's not, You know, you need to actually built to demonstrate that you can apply what you've learned to real-world situations. But for people who have taken the next step in, like done some freelance work or maybe want some cattle challenges or things like that, Yeah. I mean, you can get your foot in the door doing you know, either a data scientist role Or, you know, sometimes these roles are more described a software engineering positions, because at the end of the day, you know you're writing python code. You're writing scallop code, and employers want people that can, you know, learn new skills and apply new technologies in general, you know, they're looking more for general skills in this field as opposed to a very specific. Do you understand how to use this version of this specific technology, right?

What kind of support is provided if in case students get stuck or have some queries?

Based on experience at: Founder, Sundog Education
Summarized By: Jeff Musk on Wed Nov 27 2019
The only saving grace is that a lot of these technologies are actually pretty much the same across different platforms. So they're all just one open source package that you know, is more or less the same. They tend to be right on top of a job or something that's portable. So the platform issue is less of a nightmare than you might think. But there's a lot of questions that come in, You know, these are difficult, heavy topics. I mean understanding, machine learning algorithms you know, things like principal component analysis or recommend her systems or singular value decomposition, these are, difficult topics, right? And you need to have a pretty solid mathematical background to wrap your head around them. So we get a lot of questions, you know, especially with 300,000 students. You know, you can imagine we're getting hundreds of questions every single day. But you know, I can't do it all myself. Obviously, I'll pitch in when I can for, the more difficult questions that require my attention. But we do have sort of a first line of support with a full time teaching assistant that monitors at Q and A that's coming in. So if you take one, of course, it is on you. To me, for example, they have a Q and A feature where on any given individual lecture, you can post a question and you can actually see if other students before you have asked the same question. I got an answer already, and usually, that's the case. When you have this kind of a scale of students, that's the kind of support we provide, generally between 24 to 48 hours to get an answerback.

What other courses or skills would you recommend to students who sign up for your class?

Based on experience at: Founder, Sundog Education
Summarized By: Jeff Musk on Wed Nov 27 2019
we're talking about, like prerequisites before you actually try to get into the world of machine learning. It's definitely useful to have at least a background in linear algebra. You know, that's gonna be really helpful because understanding our major cities work and just, you know, algebra in general is kind of fundamental to the whole field. And also it's really helpful to have a background in python programming. Python has kind of become the language for for machine learning and data science lately, so it would be beneficial to go and take an introductory python course before diving into one of my courses. We tend to include sort of a crash course in python with my courses. But you know, if you're new to programming a relatively new to programming, you'll benefit from a deeper, more intense course on python first

What were the responsibilities and decisions that you handled at work? Discuss weekly hours you spent in the office, for work travel, and working from home.

Based on experience at: Sr. Manager, Technology, IMDb.com
Summarized By: Jeff Musk on Wed Nov 27 2019
That was a fun place. IMDb. As I said, it's a subsidiary of amazon.com. So we actually had our own floor on one of the buildings of the amazon.com campus in Seattle there. And my responsibility was I was in charge of the entire engineering department in Seattle. So those were the people that were in charge of the IMDB website itself. The IMDb mobile app, and other things, like their integration with fire TV and things like that. The sorts of decisions I had to make where you know what direction we're going to take as a platform. You know what platform migrations were going to take on and why. You know what new APS we're going to build what new features we're going to create. And you know, I was also part of the general leadership team at IMDB as well. So, you know, even beyond the scope of just technology and engineering, at least had a voice in some of them or strategic decisions for IMDb is a hole as well. A SZ faras hours saw as bad as people make it out to be an Amazon, you know? I mean, it varies from group to group, obviously, but, you know, I was a family man, so, you know, I did kind of like, have to enforce them. Work, life, balance there. A typical day is a pretty long commute. But yours again to the office by, you know, nine little bit before nine o'clock. Most days, I'd be out of there by six. And you know, you don't want to leave Seattle at five PM anyway, because traffic is so terrible. So it was really more about avoiding rush hour than you know, being chained to my desk or anything like that. Travel is pretty minimal. You know, once in a while I go out to do recruiting trips. So at any big technology company like this, they have an insatiable need for talented engineers. And a big part of the job is just hiring people in recruiting people. In fact, that's a big part of what I did from a day to day basis as well. That's what they called a bar Reaser, an Amazon. So if you ever interviewed Amazon. There's gonna be one person in your interview, Luke, who kind of has a veto decision. That's not from the group that you're interviewing with, and this is the person that's sort of in charge of maintaining a standard across hiring across the entire organization. So I was that guy. So I spent a lot of time doing interviews and, you know, having meetings about whether or not we're going to hire somebody as well. Um, working from home as possible. Just get the last bit of your question there. You know, obviously, sometimes things crop up outside of business hours. That's an emergency, and you have to deal with it. So we had a VPN device that allowed us to securely connect to Amazon from home if need be. But again, I tried not to do that too much.

What tools (software programs, frameworks, models, algorithms, languages) did you use at work? Did you prefer certain tools more than the others? Why?

Based on experience at: Sr. Manager, Technology, IMDb.com
Summarized By: Jeff Musk on Wed Nov 27 2019
You know, I was that more of a managerial level, so I wasn't writing a whole lot of code myself. But the platforms that my team used was that time we're using job of platforms to migrate. The website over the Web site was originally written in Pearl, If you could believe it or not. So, you know, we were migrating off of that at the time. And as far as like, you know, we were starting to dabble in big data and who Duke wasn't really a thing at that point. We were sort of pioneering some of that stuff. We were starting to play with things like pig Apache pig to actually do some log analysis at scale. We definitely had big data. You know, when you have a top I think I am. Devi is like one of the top 20 websites on the Internet in terms of traffic, or it was at the time anyway, so you can imagine just the scale of our weblog data, You know that was massive in itself just answering simple questions like how many people looked at the website yesterday turned out to be this huge engineering challenge. So we had to distribute that processing across clusters. You know, fortunately, Amazon is was very good at handling distributed computing and, you know, scaling things that even before we had to dupe. So we had a lot of resources available to us in our data centers for Amazon that we could draw upon for doing this stuff.

What were the job titles of people you routinely worked with inside and outside of your organization? What approaches did you find to be effective in working with them?

Based on experience at: Sr. Manager, Technology, IMDb.com
Summarized By: Jeff Musk on Wed Nov 27 2019
I mean, all across the board. Really? You know, my direct reports would tend to be, software development managers. And they, in turn, would have software development engineers that report to them. A few engineers might report to me directly if they were more senior level as well. So you know, in terms of like, managerial duties, you know, those were the types of people that I was dealing with and responsible for. But from a day to day standpoint, I'd spent a lot of time talking to product managers, directors, things like that, the occasional VP. And, you know, sometimes they even have meetings with Jeff Bezos himself. So all around the entire gamut there, But I'd say the most interactions, probably the product managers. You know, it's very important to have a tight coupling between what the product managers are envisioning and what you're actually building, Right? So having a good read relationship there was, like, really important for creating good results. As for specific approaches, you know, it is very easy and a large company to have these sorts of clashes between departments where they have competing interests in competing priorities. What I found most helpful was just unifying things around the customer. And this is sort of a theme at Amazon in general. So if you ever have a disagreement like it can always be resolved or usually can be resolved by what would be the best experience for the customer in this case, right. So that tended to be good let's say, Let's step back from our own personal feelings about this or personal goals and like, let's just do the right thing for Amazon's customers here, right? So that tend to be a good way to defuse conflicts like that.

What major challenges did you face in your job and how did you handle them? Can you discuss a few accomplishments?

Based on experience at: Sr. Manager, Technology, IMDb.com
Summarized By: Jeff Musk on Wed Nov 27 2019
One major challenge was just hiring enough people. You know, I'd say that was actually the hardest part of the job. Just finding talented engineers to move out to Seattle and help us build-out. IMDb you know, that was really the toughest thing quite honestly. And as the people manager, obviously like sometimes you deal with difficult things you have to do, like, once in a while. You make a mistake in hiring or if you know somebody you know doesn't isn't the right fit for what you have them doing. So sometimes very difficult conversations you have to have, people have to be transferred or leko or whatever. That's never a fun thing. You know, if you're a human being, but that's not the thing you want to be doing. So that was kind of the biggest challenge to me personally. As far as accomplishments go. You know, we launched the IMDb app and its first formed under my watch. So, you know, that's big deal. I still use it to this day. If I'm wondering who's that guy in that movie. I think the main accomplishment is just that. We had a really positive working environment. I think people liked working at IMDb and engineering in general. And, you know, I'd like to think that in part, that's because they felt like they had a technologist that was in charge of them that would listen to their concerns and actually act on those concerns on their behalf.

What was the hiring process like for your job? What were the roles of people who interviewed you? What questions were asked and how did you answer them?

Based on experience at: Sr. Manager, Technology, IMDb.com
Summarized By: Jeff Musk on Wed Nov 27 2019
Well, I guess there's two answers to that. So when I actually went to IMDB from Amazon, that was more of an internal transfer. So I was a senior manager at amazon dot com. At the time, I was in charge of Amazon's what they called personalization platform and also their front and systems that optimize their home page, which is, you know, a huge responsibility. Um, but, you know, I was ready for something new. So at that point, I just, you know, through my name in the half, for I am D because it looked like a very interesting space. There was, you know, an internal hiring loop at that point where I would talk to the CEO of IMDb. Call Needham. Great guy. Um, you know, and we just talked about it. It's basically ah was mostly based on my past experience in history and Amazon. So I had a pretty good track record as a manager there, so that was a pretty easy process to go through. A lot of it was just, you know, are you enough of a movie not fit in here or not? And I was able to say I built my own home theater at home so that that got me. And I think, um, if you want to go away about one actually got the job at Amazon itself as an engineer, you know, that's that's much earlier. Um, the process was they did, and they still follow the same process today. For the most part, they usually be to telephone interviews that where they'll, you know, throw some technical questions. I use some design questions, talk about your history, dig into your past accomplishments and what you've done and, you know, a few higher level business things. This, well, they're looking for. Are you able to take new problems and wrap your head around them and offer concrete technical solutions to them? And, you know, are you thinking about the customer experience in that process as well? Um, after getting through those two phone interviews, one being with the director that I would eventually be working for their they flew me out to Seattle for a round of in house interviews. That's an all day thing. You know, five or six interviews during the day. They're very exhausting. Process, especially for jet lag. Um, rolls would usually be two or three software engineers you know to evaluate your technical competence. Usually the measure that you'll be reporting to who would be going into higher level things And also, like I said, the bar razor So they'll have someone from outside the organization to provide sort of, ah impartial view as to whether or not you meet the bar for hiring an Amazon. In general, uh, questions like I said, range from the highly technical. You know, here's a problem. Go reverse the words in a string and white Write some code for that on a white board to design problems. You know, like designed this distributed system, too. Uh, calculate the top sellers on Amazon and massive scale, for example, was one that we used back then. And, um, you know, there would also be some just digging into your past accomplishments, making sure that you actually did the stuff that you said you did on your resume and asking you to go to a lot of detail about what you did there. And, you know, frankly, see if you're excited about it or not, right, So if you like, start to get jazzed about the stuff that you've done in the past. That's a good sign. It means you like what you do right, and you'll probably be successful is an engineer.

How did the program prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: BS, Electrical Engineering, University of Massachusetts Dartmouth
Summarized By: Jeff Musk on Wed Nov 27 2019
How did the program prepare me for my career? Like I said, the degree was in electrical engineering, and I never did electrical engineering as a job, but it was still beneficial. Part of electrical engineering at the time did involve computer science and computer engineering and some, you know, low-level programming, even assembly level programming. So at the time, you know, we're talking like 15 plus years ago here, computer science degrees were still a new thing. So a lot of these computer science jobs would actually accept an engineering degree in place of the computer science degree, So, it did help me get my foot in the door for my first jobs, for sure. You know, having that piece of paper and that line in my resume was sort of the table stakes if it were to actually get some jobs in this field. You know, I was actually one thing I regret about my time in college was that I didn't really take advantage of, you know, the social networks and support that come with that sort of an environment. I was a commuting student So I just drove to school every day, took my classes, drove to work and then drove back home at night. So I didn't really spend a lot of time networking or, you know, talking to alumni or, you know, really talking to my faculty outside of my courses. And I think that was a mistake. Had I done that, I think it might have led to more opportunities in the field that I was actually getting my degree in.

Do you have any parting advice for students and professionals starting out in your field? What three mistakes they should avoid? What three things would help them the most?

Based on experience at: Founder, Sundog Education
Summarized By: Jeff Musk on Wed Nov 27 2019
Three mistakes. Let's see. I think one mistake would be, trying to go too fast. So a lot of people they'll be attracted to the money, right? And like primarily their motivation is I hear machine learning people make lots of money. I'm gonna go learn Machine Learning and be rich. You're not gonna be successful thinking that way. Like you need to get into machine learning because you're excited by it. Let you that you're you know, you enjoy the challenge of it. You enjoy seeing the results. You're fascinated by the idea of, like simulating intelligence within a machine, right? That's the right reason to do it. It's You shouldn't just go at it primarily for the money. And that's also a big mistake. Maybe this is a mistake Number two trying to get into this field without the necessary background. So I see a lot of students getting really, really frustrated because they try to jump in and learn, like, really complicated machine learning algorithms before they've learned algebra before. They've learned programming, and they're just going too fast at the end of the day. So, you know, take the time to learn the fundamentals you need before you take on these more advanced fields. number two, I suppose. And, number three, I'd say it's just not really understanding the importance of experience. A lot of people think they can just take a course, and then they're going to get hired and make you know, six-figure salaries, and that's just not how it works. In the real world, you have to demonstrate you can apply what you've learned. So it's on you to go and take that knowledge and build something with it and show employers that, yeah, I can make things that can actually help your business, right? Employers don't care about what you know. They care about what you can do, what you can build for them. So focus on that, you know, find ways to apply what you've learned work on open source projects, do cattle competitions, do some freelance work, whatever it takes right, that's gonna be way more important than saying I took this class. Um and I suppose that's also the first thing I would say for what would help from the most, you know, just getting some real world experience so you can point to and say, Hey, I accomplish this. I'm proud of it. Go look at this wonderful thing I built. You know, that's gonna be a great story to tell during an interview and also. I guess number two is don't underestimate the power of networking. You know who you know plays a big role in what kind of jobs you can get. So maybe that's another mistake. You can frame the law, these mistakes for her, things that can help you just depending on how you frame it. But a lot of people think you could just, like, throw your resume out there or send your resume to Amazon and Google and expect to get a job. It doesn't work that way. Amazon and Google get so many resumes that they basically ignore the ones that are sent to them directly. You want them to find you not the other way around. So make sure they can find you. You know, make sure you have a profile on linked in. Make sure you have a Github depository. Make sure you're active on cattle on winning some challenges that people can look at that's going to try the attention of these employers and make them reach out to you. You said the other way around. I think I owe you one thing that would help them. And I guess it would just be practicing, you know, coding at the white board. I guess the biggest mistake I made when I was getting started was really not being prepared for that. Ah, in house interview experience, it could be very intimidating to say, Has something in front of you say, Here's a problem, right? Code on the whiteboard to do it right now. It took me a while. Yeah, just make sure you get some practice on like coding on-demand at a whiteboard. If you're used to writing code, you know, in isolation while you're in the zone and tuning out the rest of the world, you know, in your little office or room. That's a very different experience from being in front of somebody watching everything you do while you try to write code on a white board where you can actually edit things or try things out. So practice that ahead of time. I mean, I bombed a couple of my early interviews at different companies because I just wasn't good at that. So it's definitely worth your effort to make sure that you can write code at the white board. You can handle an interview situation. You know the pressure of the situation, do some dry runs with somebody you know, get some practice and maybe even, you know, take some interviews that a company that you don't really care if you get a job there or not, you know, maybe just do the interview for practice. But, you know, I guess another way to spend that would be Don't go straight for your dream job. You know, um, don't make your first interview ever with Amazon or Google or Facebook or whoever it is you want to work for. Get some practice at a smaller company first and oftentimes you'll find that getting a job at a smaller company is probably a better way to get into that bigger company later on. Because you can build up experience at that small company and that'll make you more attractive to the larger company down the road. So just don't expect to go too quickly, you know? I mean, it takes years to build up a career in this field, and it's not gonna happen overnight.