Like migrating caribou, you tend to follow the trends of what clients are doing, which dictates what you work on as a consultant.
Today, we’re talking to Lynn Langit, an independent Cloud architect. She is an AWS Community Hero, Google Cloud developer expert, and former Microsoft MVP. Lynn is a lifelong learner, and she has worked broad and deep across all three large providers. These days, she works mostly with Google Cloud and AWS, rather than Azure, because that’s what her clients are using.
Some of the highlights of the show include:
Differences between the West Coast and global use of Cloud
Education is key; Lynn is th co-founder of Teachingkidsprogramming.org
Lynn helped create curriculum and resources for school-age children; even her young daughter taught classes on how to code
Training for teachers was also needed, so TKP Labs was formed to offer fee-based teacher and developer training
Lynn started with classroom training, but has transitioned to online learning
Lynn is focusing on Big Data projects and using tools to solve real-world problems
Pre-processing and batching data, but not streaming it
AWS, Azure, and Google Cloud are all coming out with Big Data-oriented tools
Companies need to understand when the market is ready to accept a new paradigm; in the data world, change is more slow than in the programming world
If you touch a database and get burned, you are not willing to use it again; or you may have never tried to archive your data; hire a consultant to help you
Machine learning APIs give customers value quickly; review them before building custom models
Migrating data can be a costly project and restricts where the data lives
As Cloud proliferates, how will that impact technical education? Lynn’s Cloud for College Students to the rescue!
Shift from interactive to unidirectional, one-to-many learning styles; the Cloud is ready for serverless, but education is not ready for teacherless
Road that many of us walked to get to technical skills no longer exists; how to become a modern technologist
Ageism: By age 40, you are considered a manager or useless; don’t be afraid to learn something new
Lynn Langit on Lynda.com
Full Episode Transcript:
Corey: Hello and welcome to Screaming In The Cloud with your host, cloud economist Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming In The Cloud.
This week’s episode of Screaming In The Cloud is generously sponsored by DigitalOcean. I’m going to argue that every cloud platform out there biases for different things. Some bias for having every feature you could possibly want offered as an added service at varying degrees of maturity. Others bias for, “Hey, there’s some money to be made in the cloud space. Can you give us some of it?”
DigitalOcean biases for neither. To me, they optimize for simplicity. I told some friends of mine who are avid DigitalOcean supporters about why they’re using it for various things, and they all said more or less the same thing. Other offerings have a bunch of shenanigans, root access, and IP addresses. DigitalOcean makes it all simple. “In 60 seconds, you have root access to a Linux box with an IP.” That’s a direct quote albeit with profanity about other providers taken out.
DigitalOcean also offers fixed-price offerings. You always know what you’re going to wind up paying this month, so you don’t wind up having a minor heart issue when the bill comes in. Their services are also understandable, without spending three months going to cloud school. You don’t have to worry about going very deep to understand what you’re doing. Its click button or making API call, and you receive a cloud resource.
They also include very understandable monitoring and alerting. Lastly, they’re not exactly what I would call small-time. Over 150,000 businesses are using them today. Go ahead and give them a try. Visit do.co/screaming and they’ll give you a free $100 credit to try that. That’s do.co/screaming. Thanks again to DigitalOcean for their support in Screaming In The Cloud.
Welcome to Screaming In The Cloud, I am Corey Quinn. Joining me today is Lynn Langit who is an independent cloud architect. Among other things, holds the dubious honor of being an AWS community hero, a Google Cloud developer expert, and formally a Microsoft MVP, I believe all at the same time. Welcome to the show.
Lynn: Thanks for having me. I’m excited to talk with you.
Corey: It’s fascinating to sit down and talk to someone who is broad and deep across all three of the large public cloud providers. Usually, someone specializes in one or at most two. Personally, I’ve gone very deep with AWS historically but I cannot speak authoritatively, I'm too much past that. What is that like?
Lynn: I’ve been independent for seven years. Apart of that, I actually was an employee of Microsoft for five years and before that I was their partner. This past seven years have been a sea change for me, it’s been really interesting. The beginning of it, I did a lot of work on Azure because I came out of the Microsoft ecosystem but I did a lot of work on Amazon because they were the dominant cloud provider.
As the years go by, I am finding myself doing more work on Google Cloud and Amazon and less on Azure, nothing negative on Azure, it’s just what my customers are looking for. It’s interesting to see the way my consultancy has moved over time.
Corey: It always seems that, as a consultant myself, that following the trends of what clients are doing dictates what I find myself working on. I can sit and say that a particular service or product that a company is offering terrific, that is the thing I want to focus on. If people aren’t using it and aren’t reaching out and asking for help around that particular thing, I find that I don’t have much of a business. I definitely understand what you’re saying with respect to, I guess, following the trends, it's almost like they’re migrating caribou.
Lynn: I live in Southern California and I do a lot of my work on the West Coast of the US which is a strange island in the global world of cloud. As a contrast to that, I also do work globally. I either do work on the West Coast or somewhere else on Earth. I find that these two partitions are different. West Coast tends to embrace the new new and tends to be first up. Of course we have wonderful connectivity, that helps. I’ve worked in parts of the world where connectivity is not a given and that really changes the game.
Corey: One of the interesting things that I find that you’ve been focusing on has been the idea of education. You’re one of the cofounders of teachingkidsprogramming.org. You focus on getting people further along the path of technical mastery than they already are; which is incredibly valuable and incredibly important and is often an area that seems to get short shrift. How did you get involved in that?
Lynn: I’ve been working with educating children around coding for 10 years, it really started when I joined Microsoft in 2007. I joined the team of 62 people, I was a US national technical presenter and there were only two women on the team. As part of accepting that role at Microsoft, I actually negotiated and I said that I would like to spend 25% of my time working on improving the pipeline, getting more people of color, women in technology.
That landed as me writing a program called DigiGirlz in Southern California, which were events for high school girls. There wasn’t any national curriculum at the time. I worked with my then eight-year old daughter and developed some curriculum. Because there was a vacuum, that curriculum was used globally and got a lot of iterations and I learned quickly. Also from my job, I travelled globally so I did the two for all the time.
I would go to the technical conference and speak about the shiny new Microsoft technology and then I would hold the DigiGirlz event, we got a lot of learning. When I left Microsoft for five years in, I discovered from working with my various communities that, especially in the US, there was this situation, we have AP Java in high school but there was no curriculum available to move the kids from visual learning like a scratch or a squeak to Java. There’s nothing in the middle school.
We took our work that we had done in small basic and we ported it to a middle school kid version of Java. We also worked on the curriculum and the deliverables around the curriculum, videos on how to use it, lesson plans, and worked with some middle school teachers. In fact, by then my daughter was in middle school. Kind of a funny story, she taught a class of seventh graders when she was a seventh grade after school. She also taught her history teacher how to code. Now, for many years, subsequently she’s gone back and co-taught with him in her middle school. It’s a cool story.
Corey: It’s fantastic watching people go from student to teacher. Something that I’ve always appreciated about conducting trainings is even when I take something that I thought I knew intimately and I teach it to someone else, I learned that, I have no idea how this really works. It’s almost like going back to basics in a way that surprises me.
Lynn: The organization is actually split because it was a labor of love on my part. I self-funded most of the Java developers that I paid because I’m not a Java developer, ironically. I paid Java developers to build off the curriculum with me, there are 80 lessons now and it could be at full year. It’s being used by Google and Linux in 16 states and 10 countries, it’s pretty exciting.
But what was also needed was training for teachers because the model that was really being adopted with the open source and free scenario that I had setup was teachers who were super achievers, who were teaching themselves to code, and were using the curriculum. There’s a whole bunch of teachers that are really are wanting more training, they want to use their teacher and service days.
We spawned a child non-profit called TKP Labs that does for a fee, teacher-based training. We’ve had some really good success with that too. In fact, we're working with our first entire school district in Santa Barbara, California. It’s an interesting model, it’s being sponsored by a local business and they’re doing a teacher training and developer training side by side. We’re really, really excited about that.
Corey: This would be enough for a full time job in and of itself from what you’re describing but you go beyond that. You have a series of videos on Lynda.com which is now a LinkedIn property, which is now itself a Microsoft property and All Roads Lead To Rome. On that site you had–last count had 17 courses available, by the time this publishes, you’ll probably have five more, at the rate things tend to move in the space.
Is that an outgrowth of your work teaching kids than moving into adult education? Is this something that just dovetailed along with it or did it come about from somewhere completely different?
Lynn: No, it’s actually the other way. I was a classroom trainer, I’m middle aged, I’ve been training for a long time. In the dot-com boom, I was a Microsoft certified trainer for eight years, that’s how I got into tech.
I actually came out of the business world and I took certification exams and then became a teacher starting in the networking side and then really finding a love for databases. I ended up writing books about SQL Server and all that way back 2007, 2008.
I had this tech training background, this classroom training. What has happened is classroom training has pretty much gone away because of online. I’ve really morphed at into online training and I’ve trained for a number of different providers. I did some work for Pluralsight, I did some work for some other guys too, a long time ago.
The home I found is Lynda.com, Microsoft basically, because they have a really neat setup. First of all, they’re in Santa Barbara which is a wonderful place to go but they have recording studios. If you listened to one of my courses, this is going to be professionally produced, they’ll probably sound the same but I always marvel at how great they make me sound and how great they make me look.
It’s a full team effort which results in, I think, a better experience for the students. As a teacher, I’m happy to be a part of what they’re doing up there.
Corey: It sounds like it’s something that’s absolutely fantastic. Every person I’ve spoken to who’s engaged with it in some way, shape or form, comes a way either on teacher side or the student side with rave reviews. Moving on to a slightly different topic, you’ve been specializing lately in big data projects, I believe that goes beyond your teaching work and into using these tools to solve problems in the real world more or less, is that accurate?
Lynn: I am ADD in being independent. I’m going to teach, teach, teach, teach, teach for a year because I want to go hangout in Santa Barbara. I want to actually build something because if you don’t actually build something, then you’re not really the most effective teacher, you’re just reading out of a book. Anyone who’s involved in technology, why we’re involved is because it’s a creative process and we want to actually build things.
I go back and forth. For example, two years ago, for 14 months straight, I was embedded with a Dev team so I will call myself an architect who codes. It was an IOT project on Amazon for an enterprise; I was a Sprinklr Controller, literally. We've made a phone app for the sprinkler controller for golf courses and stuff like that. We were one of the first people to go out on AWS IoT.
It was a super interesting engagement and I literally was coding everyday either in pair or in group, they call it mob programming at this particular company. Sitting there and seeing the pain when people are working with a new API, seeing the learning around working with new protocols, seeing the learning. This was their first, cloud project. I have this combination of building actual things and then teaching about it.
Corey: I have always more or less stayed away from data in general. My background was always in things that would these days we called it stateless but we never thought of it that way once upon a time. But databases, data stores, data lakes, big data, data tributaries, data estuaries, whatever it is that we call them, it was always the stuff that scared the heck out of me. If you break the data in some form, it’s very difficult, expensive, and sometimes impossible to get that back.
That’s the stuff that leaves scars. As a result, I stayed away from that and I don’t have a whole lot of visibility into the rise of big data these days. What’s driving the, VC frenzy if nothing else around the entire area of big data? Is it real or is it hype?
Lynn: My production thing that I’m doing right now is in genomics, specifically helping to speed up the processing of the results of aggregate genomic sequencing, trying to find the needles and the haystack for diseased conditions. I’m working with this group in Australia which is called the CSIRO, the Commonwealth Scientific and Industrial Research Organization, which is basically the national science foundation of Australia.
They’ve developed some customized libraries for processing because the amount of data coming off the sequencers is of a volume we’ve really never seen before. Certain data collection, situations, and genomics, in my experience, is probably the lead driver of this because human DNA, for example, is three billion data points for each sample.
When you are doing machine learning, looking for variance of interest of different letters, TAGC, then you get into matrix of multiplication. Even if you subsample it, we’re working for sample machine learning with situations of 10 million features for 2000, 3000 samples, that’s a matrix calculation space of 1.7 billion data points. Yes would be my short answer, I’m doing it. Not in all domains since the data is that big.
One of the reasons, in addition to that humanitarian aspect which is helping to speed up research around personalized treatment for things like cancer, is the intellectual challenge of true big data that I think is going to trickle in the other domains. I see IoT on data volumes rising, not to the extent of genomics but exponentially based on what we’ve seen.
Corey: Do you find that the rise of all these data at edge is necessitating the idea of preprocessing this data before you send it through or are companies still trying shove it across the network and then do the heavy lifting once it arrives?
Lynn: It’s such a paradigm shift, people still think of preprocessing, batching, and they don’t go to the obvious thing which is streaming. The reason is because streaming was seen as special to used case. The skills around big data streaming are relatively hard to find. You see a lot of old thinking even to the extent of, with some of the groups I’ve worked with in the cloud, they’re going to work with virtual machines or past solutions like Elastic MapReduce on Amazon.
We’re really trying to take advantage of some of the newer technologies around containers and server list. For example, we’re trying to work on a prototype visage maker, which is containerized machine learning for one of the tools with the CSIRO group, to get them off of servers.
Everybody starts there, you can’t just suddenly jump to all the new technologies, you have to start with the old patterns and then you have to figure out what are the limits in terms of time and cost and when does it makes sense to move to the newer technologies.
Corey: To that end, tying back to what we said at the very beginning, how you have deep roots in all three of the large public cloud providers that see in North America these days, all three of these companies, AWS, Azure, and GCP, are coming out with a number of big data oriented tools that are aimed at removing the “undifferentiated” heavy lifting to steal Dr. Vogel's term and effectively start delivering insight and ability to separate signal from noise from all of these data offerings.
At this point, from your perspective, is any particular vendor a clear leader of the pack at this moment or are they all more or less still waiting for someone to break out?
Lynn: I think what really started this, they were actually, as they often are, ahead of the market, was Google BigQuery. People just couldn’t understand because you open up Query window and you upload some many, many, many text files and you can just do a Nancy SQL query and there are no servers and nothing to manage. It was just magical, they couldn't understand it.
It’s interesting that Amazon waited until last year to bring out Athena because you know they could’ve done it sooner but Amazon is very good at engaging the customer market, when is the customer ready for the new transitions? I think that’s one of the reasons why they’re so dominant in so many markets. They certainly have fantastic technologies but do they have the appropriate balance for many situations or should bring out new paradigms of understanding when the market can accept the new paradigm.
The uptick on Athena has been pretty substantial so they seem to have gotten that one right. The other one that I’m looking on is Glue. I think that Glue is a very elegant product but I have difficulty getting customers to understand what’s really going on there, they might’ve been little early with Glue.
Corey: I’m not going to shame anyone because I don’t think I understand what’s going on with Glue. Can you distill it down for me?
Lynn: Sure. It’s extract, transform, and load or the preprocessing, as you would say, as a service. Rather than setting up virtual machines and having a bunch of scraps and a bunch of batch processes to denormalize or deduplicate or fix NOLS or all that cleaning stuff that you do with data. You have recipes and PySpark and you have the flexibility of containers which is a lot cheaper and more scalable in terms of your ETL.
Don’t get me wrong, I personally think Glue is fantastic. Being an educator, this will break my heart, that there is a need for education because I’ve worked in that area too. I’m just seeing, with customers, they instantly understand Athena, SQL queries on files, they get that. Amazon is the only one struggling here, by the way. Google has a great product called Dataflow which is the productization of Apache Beam which is similar conceptually.
They even put another product on front of it which is GUI interface, almost like a SQL server integration services interface called Dataprep which generates Dataflow code. Again, the uptake hasn’t been, I think, with the companies are wanting because in the data word, change is more slow than in the programming world. The DBAs and the people that are used to working with the licensed products moving to container-based cloud services, that’s a pretty big jump.
Corey: It seems like a common theme where people will take a look at a new offering from AWS, GCP, and say that its garbage because it lacks a certain feature, it has a certain failure mode that doesn’t work for a particular use case and then they'll write it off and tend to dismiss it. I think that that’s a relatively naïve approach. I see the promise of a lot of the services that have been coming out over the past couple of years.
Are they ready for primetime now? Probably not. Some of these companies would do well to call out some of the shortcomings before people trip over them. What excites and inflames my passion is the ability to see where this is going. Take Lambda for example, picture a version of Lambda that doesn’t have the current limitations as they implemented today. You start to see the world unlock in a number of different ways.
Imagine a lot of this is true for data with the added caveat of, if something working on your data screws up the first time, you’re probably not likely to give it another try three months later just because you’ve been so badly burned the first time. Is that accurate or is that me bringing my–I touched the database once and now I’m not allowed within 500 yards of one ever again–bias speaking.
Lynn: Before you start a new project, I have something called PhaseZero data hygiene which is when is the last time that you did a trial restore, not do you have a backup. When is the last time you successfully did a trial restore?
Corey: I'll ask my DBA about that.
Lynn: Sometimes PhaseZero takes a year. I’m telling you a real world story, AdOne company, they have big data. They copied their data nine times, that’s one way to do it. If it doesn’t work for some tool, just make another copy. Clearly, that’s not what we want to do. You have to have your house in order before you put this new stuff on top.
That’s what you work with an independent consultant rather than working directly with a vendor because the vendor just wants you to use their product, whereas somebody who’s had a little bit of real-world experience says, “Okay, before we go on this new stuff, let’s make sure that we’re having clean data put in because it’s still garbage in, garbage out.”
Corey: I’m privileged in that my data exploration projects in my own consulting work is generally limited to Amazon bills. We’re talking in the hindsight in tens of gigabytes which at that scale is I’m going save a copy of that before I start doing anything too ridiculous to it because the space is cheap. I’m not manipulating it directly in place since I’m still dealing with the size of data that on submachines can fit in RAM and it fits on any laptop that I’ve used in recent memory. It doesn’t generally tend to hold up at petabyte scale.
Lynn: The whole thing with moving towards data links too, that encourages experimentations. Storage in S3, Google Cloud Storage, or Azure Blob is phenomenally cheaper than storing in RDS or Redshift, or Spanner. That’s one of those hygiene things that I’m trying to help customers with. Even though we’re talking about all of these fancy circles of stuff, you still run into people that have never archived their data.
They have ten years of data on a relational database because they just did. As you’re moving into the cloud, you’re going right, let’s partition off the data you’re not using which tends to be 75% of database, throw that in the S3 and then you can experiment with that because it’s really cheap. It’s that discipline that you want to have before you really get going.
The other thing that I want to mention, because it’s something I’ve really seen come up in the last year and it’s really given customers value quickly, is the machine learning APIs. There is so much hype right now around TensorFlow and MXnet and all the other ones. They’re great for their particular purpose but I just actually worked on a course on machine learning.
I spent more time because of that course, working with Amazon machine learning APIs like Rekognition, Lex, and Polly. I am really, really impressed with what is available now in terms of special purpose machine learning and I’m going to directing my customers to look at that before they go and blow custom models.
I really think that combining that with getting historical data in the S3, that will be opening up a lot of doors for our customers to do interesting experiments. It’s really a tip I wanted to pass on because it’s something I see very newly available across all three cloud vendors. A lot of competition there because of course, whoever gets your data processing dollars is probably going to be the most successful cloud vendor.
That’s why storage prices are going to the bottom because they want you to store your data up on their data lake and then they’re going to make their money on processing, both NCSQL and increasingly machine learning.
Corey: I’m seeing that in my own practice too. When you start looking at multicloud workloads or arbitraging between different providers, it works really well to say that I can move this containerized workload to a different provider and save $0.20 an hour. The counter argument too is due to the data gravity and data transfer cost. Migrating the data to a place where that container can work on it intelligently is a $20,000 project. That tends to wind up restricting a lot of the compute to where the data lives.
Lynn: I know you do work around cloud cost and so do I, in fact I made a course around it because I had so many people asking me. As you well know, understanding particularly–I have to call for this Amazon cloud cost is almost the full time job, unfortunately. One of the things that I do hope happens is that the other vendors push Amazon towards better tooling.
I have to celebrate Google because for example, if you're spinning up a VM there, it’s literally a slider. You put a slider of how many CPUs, how much space you want and you can see it shows you the price on the page. People will say, "What do you want from Amazon?" I’ve said this over and over and over, I want the slider. I don’t want to know about 57 different EC2 instance, names, and numbers. I want the slider, I want simplification. I think the other vendors are going to drive that.
Corey: GCP is definitely ahead of the pack with respect the costing approaches. The idea of "kill all billable resources" in a project is phenomenal. I’ve been saying for a while that my entire business, which is fixing the horrifying AWS bill should not exist. I would love a day where Amazon releases a product or a service that renders me completely irrelevant and I get to go work on other problems in the space that aren’t the bill.
I shouldn’t have to effectively be a finance department’s data science team more or less and setup these convoluted processes and controls. What I do shouldn’t be a thing but we’re somehow in this weird place where I don’t see a clean way for AWS to fix this in the near term.
Lynn: I think you have a deeper insight than I do. I literally been hired to do just that, similar to you, I had one startup. They were spending $200,000 a month, getting no return. I said, "I’m gonna take a percentage.”
Corey: I wish I found more people willing to be okay with percentages, I would be able to retire in a month. We’ve talked a little bit about education and we’ve talked a little bit about the rise of various cloud services in the big data space, let’s put those two together. As cloud proliferates further and further, how does that impact technical education?
Lynn: My work in this area follows my own daughter, one kid. She’s now in a university. She actually started early, she went to Stanford in the summer when she was 17 because she’s a good student. That combined with some other work I’ve done with the university. I was a judge at the data hackathon last year for Southern California, 15 teams. This is a true story–15 teams at the Expedia data site and of the 15 teams, zero used the cloud, most of them subsampled the data. The winning team split the data set across their seven laptops so they could process it. I looked at them and I said, "Oh my goodness, have you guys never heard of EC2?" They we’re like, "Yeah but we just don’t know." I have this vision. I’ve been shopping out to the cloud vendors of creating a series called cloud for college students.
I actually have set of students flying up worldwide. I have it on my agenda, now that I’m going to talk about it, I'll have to do it. I want to do it this year and I want to have a public series. I’ve surveyed the students to see what pain is because they’re only going to be interested if they have pain. It's certainly something really basic like, have you ever lost homework? How do you do a multicloud redundant backup? Because they’ve all lost homework.
Another thing, if we had to install something on your laptop and it screwed up on python-build because you don’t know how to do virtual environments, here’s how to do a virtual machine. For some of the data science students, have you ever had to wait two days to run your workload because you had an old laptop? Here’s how to get Amazon machine learning AMI or GPUs on it.
I’ve got the whole vision, I am going to start educating. It starts with one person, we need a lot more. I think that we need to start with our college students because these kids are not using the cloud. Seriously, they split the data on seven laptops. These are the top universities in California, are you kidding me.
Corey: As you mentioned earlier, you’re affiliated with some more professional ADD. I have actual ADD as well. One of the challenges I have is I have difficulty paying attention to certain forms of learning. For me, sitting in a class, I’ll zone out. Make me watch a video, I have to keep rewinding because my mind wanders and I miss something important that was just said. Reading is the way that I tend to absorb things most effectively.
I fall into the trap sometimes of assuming that this is how everyone learns, I had to be convinced to start a podcast because I’m generally not a podcast listener for some of those reasons. The challenge that I find is that most people I interact with are not like me. They tend to learn better from someone teaching them through guided learning.
It seems that a lot of the online course that I’m seeing doesn’t have the same interactive element where you have a teacher there to ask questions to. Online forums are full of people referencing–I saw one last night, Zed Shaw learned Python the hard way. Someone was asking if the people in the forum could take them through the program that, he uses an example and explained it line by line because they didn’t get it.
To some extent, that strikes me as the exact thing, you should be able to ask a teacher about but it feels like removing more and more towards unidirectional, one too many learning styles. Is that just a selection bias on my side or is this something that is starting to look like a paradigm shift?
Lynn: It is, it’s concerning to me. Obviously, I have a bias because I have been a teacher of adults for so many years in classrooms, both public classrooms and in private company engagements. I’ve taught synchronously both live and online for 15 years, I’ve just come right up front and state my bias that teachers are an important part of the process.
In addition to my bias, I’ve done some research, I’ve read people’s research work around the impact of multimodal teaching as you talked about. That’s something that a skilled teacher will bring to a classroom. The simplest way, auditory, visual, and kinesthetic, there’s a lot more nuances to it. I believe passionately that although the cloud is ready for server less by education is not ready for teacher less. I don’t think it’s a good thing.
Corey: I think you just titled the episode.
Lynn: I’m continuing to get data because opinions are only that. As I continue to gather data, this is something that I think I’m going to continue to speak and write about because I think that it is an important change in our society. One, that we need to understand the implications of and with everything else, we need to have data around.
Corey: I have a personal bias towards meeting people where they are. Everyone learns differently and being able to convey information in a way that resonates with the audience in a way they can digest has always been important–it’s why I think that there isn’t only one way to do things. I’ve fallen to the traps semi frequently of assuming that everyone is going to learn the way that I am.
It turns out that most people don’t generally tend to learn best by reading man pages in the dark in their parent’s basement but that was my approach to it. A common theme among conversations that I’ve had with various guests on this podcast is starting to emerge, that is the road that many of us walked to get to the point of technical skill that we have today. Isn’t there anymore?
I don’t see that helpdesk jobs that existed in the same way that did when held those roles. I don’t see, email assistant men wanted, as a job description that exists. Having used AWS for just shy of 10 years now, even today, I login to the console, I see the fine print and that not the fine print, that’s the list of services. I have lost sight of what it would take to get to where I am today if I were to start fresh now.
It feels on some level like those of us who have gotten to where we are have either done so by absorbing such a body of knowledge that is not reasonable to expect people to pick that up or worse, those of us who’ve gotten here to some extent have pulled the ladder up behind us. How do you get people to a point starting with very little more than an interest and a natural aptitude for absorbing information to start becoming a modern technologist in today’s world?
Lynn: I am focusing now on college students because I have one so I have access to the student community, basically. The other situation is I’m continuing to implement my 20% or 25% volunteer rule of life. The project I’m doing in Australia, I’m actually not being paid for. Over my many years of being a technical professional, I’ve done three substantial projects, one with the electronic medical records project in Zambia for 5 years, 10 years of teaching kids programming, and now this is my second year of working with CSIRO, I personally do this 20% time.
When I was employed, I made it a condition, a part of my contract so I was paid and bonused for the DigiGirlz work and I engaged with newer technology with people in this work. I will work with homemakers who are transitioning into the workforce, I work with students, I work with people who are coming into technology. Sometimes they’ll work with me for free, sometimes I will pay them at a reduced rate.
I can’t solve the problem at scale but I can just say that what is working for me is I’m doing apprenticeships. I have one student now, he was one of the winners of the hackathon that worked for me for 40 hours for free. Now he is an apprentice and we’re working refactoring some of the Scholar Code for the random forest for this Variant Spark genomic algorithm. I’m taking on personal apprentices. I don’t know if that’s scalable but that’s what I’m doing.
Corey: It’s fantastic seeing people taking apprentices because I do feel that to some extent, something of a trade that you learn by doing. What I’m trying to figure out, I’m sure I’m not alone in this one, is how you start scaling that. Even now, if I were to take on an assistant and start brain dumping to them all the stuff that I’ve picked up about AWS alone, it feels like there would still be geared to a foundational knowledge that I’ve just either forgotten or taken for granted.
Getting people from a pedagogical standpoint to a point where they can have those conversations and start to contribute in a meaningful way is a difficult place to get to, I wish I saw a clearer path that there.
Lynn: I do too, I wish there was some national support or program or something like a job core. I’m not a politician but it just seems to me we have this huge gap. Again, I took some of the students from the data hackathon to a one day event in LA that was hiring. They were all graduating seniors and they all had CS degrees or Data Science degrees or both. Some of them got offers for free internships and stuff.
But I was astounded that because they didn’t have experience with the cloud and it was usually Amazon but basically they were asked any cloud, none of them had any experience with the cloud, none of them got any job offers. That was again one of the reasons why I want to do this cloud for college students. I was frankly floored that I took all these, I took four students. I took them around to all my vendor friends and nobody got a job offer because they didn’t have cloud experience. I thought, "I have to do something about this."
Corey: Entry level role, three years experience required. I wish there was a clearer path. They need Lynn as a service to some extent, that gets dangerously close to teacher less.
Lynn: Maybe I could make a bot with Lex, a little Lynn bot.
Corey: Have it pop up in Slack and correct you from time to time when you’re about to say something foolish, that would be great. I love that model.
Lynn: It’s kind of annoying to me.
Corey: I feel that way about every chat bot that I tend to encounter. Is there anything you’d like to mention or leave us with as we close out, something you want us to remember or think about as we wait the long week until the next episode?
Lynn: I made personal example of life-long learning and I’m really trying to bust the ages of myth. You live up in the Valley so good for you, I only visit occasionally but I am over 50. I am doing TensorFlow and doing the hottest, newest thing, and relearning linear algebra. My brain is not broken, people of the computing world.
I always hold up myself as an example and then I often have people who are in the similar age group come up to me and say, "Thank you for saying that," because it needs to be said. It’s a real problem in our industry. When you get to be 40, you’re considered to be a manager or useless which is basically the same thing. I am a hardcore technical and I am over 50. I am going strong and I’d like to see more people alongside. Don’t be afraid to try to learn something new. You don’t have to study but you can study, your brain is not broken.
Corey: This has increasingly been top of mind, I am just creeping up on 40 in the next few years. That is something that I’m starting to see that we–almost a cult here at the Valley that worships youth where if you’re not a 22-year old willing to work 110 hours a week on your startup, then you don’t have what it takes to succeed. That’s a toxic, painful myth.
Speaking with you is always a delight because I don’t believe we’ve ever had a conversation where I didn’t come away brimming with new ideas and inspiration and things to research that you’ve just touched on briefly.
You don’t get that level of depth and breadth by having gone to a boot camp for 18 weeks. This is something that you learn by doing and there’s a definite value in experience. Thank you for saying that, it’s something that I don’t think we see touched upon enough these days.
Lynn: You’re welcome.
Corey: Thank you very much for joining me. This was Lynn Langit. I’m Corey Quinn and this is Screaming In The Cloud.