Episoder
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hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love
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data and we have weekly events and today one is one of such events and I guess we
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are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so
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much because this is the time we usually have uh uh our events uh for our guests
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and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of
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slipped my mind but anyways we have a lot of events you can check them in the
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description like there's a link um I don't think there are a lot of them right now on that link but we will be
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adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget
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to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome
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as the one today and of course very important do not forget to join our community where you can hang out with
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other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click
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on that link ask your question and we will be covering these questions during the interview now I will stop sharing my
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screen and uh there is there's a a message in uh and Christopher is from
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you so we actually have this on YouTube but so they have not seen what you wrote
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but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I
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call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't
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need like you we'll need to focus on answering questions and I'll keep an eye
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I'll be keeping an eye on all the question questions so um
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yeah if you're ready we can start I'm ready yeah and you prefer Christopher
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not Chris right Chris is fine Chris is fine it's a bit shorter um
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okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per
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year but we actually skipped one year so because we did not have we haven't had
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Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and
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head chef or hat cook at data kitchen with 25 years of experience maybe this
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is outdated uh cuz probably now you have more and maybe you stopped counting I
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don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the
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co-author of the data Ops cookbook and data Ops Manifesto and it's not the
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first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one
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will be about data hops so we'll catch up and see what actually changed in in
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these two years and yeah so welcome to the interview well thank you for having
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me I'm I'm happy to be here and talking all things related to data Ops and why
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why why bother with data Ops and happy to talk about the company or or what's changed
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excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always
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thanks Johanna for your help so before we start with our main topic for today
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data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who
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have not heard have not listened to the previous podcast maybe you can um talk
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about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed
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in the last two years so we'll do yeah so um my name is Chris so I guess I'm
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a sort of an engineer so I spent about the first 15 years of my career in
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software sort of working and building some AI systems some non- AI systems uh
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at uh Us's NASA and MIT linol lab and then some startups and then um
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Microsoft and then about 2005 I got I got the data bug uh I think you know my
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kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life
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would be fine um because I was a big you started your own company right and uh it didn't work out that way
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and um and what was interesting is is for me it the problem wasn't doing the
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data like I we had smart people who did data science and data engineering the act of creating things it was like the
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systems around the data that were hard um things it was really hard to not have
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errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my
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Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and
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look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and
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very happy um and if there was I'd have to like rce myself um and you know and
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then the second problem is the team I worked for we just couldn't go fast enough the customers were super
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demanding they didn't care they all they always thought things should be faster and we are always behind and so um how
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do you you know how do you live in that world where things are breaking left and right you're terrified of making errors
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um and then second you just can't go fast enough um and it's preh Hadoop era
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right it's like before all this big data Tech yeah before this was we were using
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uh SQL Server um and we actually you know we had smart people so we we we
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built an engine in SQL Server that made SQL Server a column or
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database so we built a column or database inside of SQL Server um so uh
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in order to make certain things fast and and uh yeah it was it was really uh it's not
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bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's
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still queries um things like that we we uh at the time uh you would use olap
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engines we didn't use those but you those reports you know are for models it's it's not that different um you know
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we had a rack of servers instead of the cloud um so yeah and I think so what what I
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took from that was uh it's just hard to run a team of people to do do data and analytics and it's not
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really I I took it from a manager perspective I started to read Deming and
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think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um
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and so how do you run that factory so it produces things that are good of good
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quality and then second since I had come from software I've been very influenced
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by by the devops movement how you automate deployment how you run in an agile way how you
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produce um how you how you change things quickly and how you innovate and so
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those two things of like running you know running a really good solid production line that has very low errors
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um and then second changing that production line at at very very often they're kind of opposite right um and so
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how do you how do you as a manager how do you technically approach that and
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then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off
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uh with some customers we started building some software and realized that we couldn't work any other way and that
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the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our
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methods and then so yeah we've been in so we've been in business now about a little over 10
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years oh that's cool and uh like what
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uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do
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you remember roughly when devops as I think started to appear like when did people start calling these principles
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and like tools around them as de yeah so agile Manifesto well first of all the I
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mean I had a boss in 1990 at Nasa who had this idea build a
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little test a little learn a lot right that was his Mantra and then which made
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made a lot of sense um and so and then the sort of agile software Manifesto
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came out which is very similar in 2001 and then um the sort of first real
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devops was a guy at Twitter started to do automat automated deployment you know
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push a button and that was like 200 Nish and so the first I think devops
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Meetup was around then so it's it's it's been 15 years I guess 6 like I was
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trying to so I started my career in 2010 so I my first job was a Java
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developer and like I remember for some things like we would just uh SFTP to the
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machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like
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it was not really the I wouldn't call it this way right you were deploying you
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had a Dey process I put it yeah
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right was that so that was documented too it was like put the jar on production cross your
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fingers I think there was uh like a page on uh some internal Viki uh yeah that
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describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is
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why that changed right and and we laugh at it now but that was why didn't you
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invest in automating deployment or a whole bunch of automated regression
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tests right that would run because I think in software now that would be rare
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that people wouldn't use C CD they wouldn't have some automated tests you know functional
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regression tests that would be the exception whereas that the norm at the beginning of your career and so that's
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what's interesting and I think you know if we if we talk about what's changed in the last two three years I I think it is
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getting more standard there are um there's a lot more companies who are
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talking data Ops or data observability um there's a lot more tools that are a lot more people are
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using get in data and analytics than ever before I think thanks to DBT um and
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there's a lot of tools that are I think getting more code Centric right that
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they're not treating their configuration like a black box there there's several
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bi tools that tout the fact that they that they're uh you know they're they're git Centric you know and and so and that
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they're testable and that they have apis so things like that I think people maybe let's take a step back and just do a
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quick summary of what data Ops data Ops is and then we can talk about like what changed in the last two years sure so I
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guess it starts with a problem and that it's it sort of
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admits some dark things about data and analytics and that we're not really successful and we're not really happy um
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and if you look at the statistics on sort of projects and problems and even
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the psychology like I think about a year or two we did a survey of
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data Engineers 700 data engineers and 78% of them wanted their job to come with a therapist and 50% were thinking
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of leaving the career altogether and so why why is everyone sort of unhappy well I I I think what happens is
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teams either fall into two buckets they're sort of heroic teams who
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are doing their they're working night and day they're trying really hard for their customer um and then they get
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burnt out and then they quit honestly and then the second team have wrapped
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their projects up in so much process and proceduralism and steps that doing
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anything is sort of so slow and boring that they again leave in frustration um
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or or live in cynicism and and that like the only outcome is quit and
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start uh woodworking yeah the only outcome really is quit and start working
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and um as a as a manager I always hated that right because when when your team
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is either full of heroes or proceduralism you always have people who have the whole system in their head
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they're certainly key people and then when they leave they take all that knowledge with them and then that
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creates a bottleneck and so both of which are aren aren't and I think the
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main idea of data Ops is there's a balance between fear and herois
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that you can live you don't you know you don't have to be fearful 95% of the time maybe one or two% it's good to be
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fearful and you don't have to be a hero again maybe one or two per it's good to be a hero but there's a balance um and
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and in that balance you actually are much more prod
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In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.🔗 CONNECT WITH Guillaume LemaîtreLinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58Github - https://github.com/glemaitreWebsite - https://glemaitre.github.io/🔗 CONNECT WITH DataTalksClubJoin the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/ 🔗 CONNECT WITH ALEXEYTwitter - https://twitter.com/Al_Grigor Linkedin - https://www.linkedin.com/in/agrigorev/ 🎙 ABOUT THE PODCASTAt DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds.We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.You can access all the podcast episodes here - https://datatalks.club/podcast.html📚Check our free online coursesML Engineering course - http://mlzoomcamp.comData Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you're a company and want to support us, contact at [email protected]
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Links:
LinkedIn:https://www.linkedin.com/company/frontline100/ Ba Linh Le's LinkedIn: https://www.linkedin.com/in/ba-linh-le-/ Sabrina's LinkedIn: https://www.linkedin.com/in/sabina-firtala/ Twitter: https://x.com/frontline_100?mx=2 Website: https://www.frontline100.com/Free LLM course: https://github.com/DataTalksClub/llm-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.You can access all the podcast episodes here - https://datatalks.club/podcast.html📚Check our free online coursesML Engineering course - http://mlzoomcamp.comData Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you’re a company, support us at [email protected]
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We talked about:
Erum's Background Omdena Academy and Erum’s Role There Omdena’s Community and Projects Course Development and Structure at Omdena Academy Student and Instructor Engagement Engagement and Motivation The Role of Teaching in Community Building The Importance of Communities for Career Building Advice for Aspiring Instructors and Freelancers DS and ML Talent Market Saturation Resources for Learning AI and Community Building Erum’s Resource RecommendationsLinks:
LinkedIn: https://www.linkedin.com/in/erum-afzal-64827b24/
Twitter: https://twitter.com/Erum55449739
Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Vincent’s Background SciKit Learn’s History and Company Formation Maintaining and Transitioning Open Source Projects Teaching and Learning Through Open Source Role of Developer Relations and Content Creation Teaching Through Calm Code and The Importance of Content Creation Current Projects and Future Plans for Calm Code Data Processing Tricks and The Importance of Innovation Learning the Fundamentals and Changing the Way You See a Problem Dev Rel and Core Dev in One Why :probabl. Needs a Dev Rel Exploration of Skrub and Advanced Data Processing Personal Insights on SciKit Learn and Industry Trends Vincent’s Upcoming ProjectsLinks:
probabl. YouTube channel: https://www.youtube.com/@UCIat2Cdg661wF5DQDWTQAmg Calmcode website: https://calmcode.io/ probabl. website: https://probabl.ai/Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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Links:
Biodiversity and Artificial Intelligence pdf: https://www.gpai.ai/projects/responsible-ai/environment/biodiversity-and-AI-opportunities-recommendations-for-action.pdfFree Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Anahita's Background Mechanical Engineering and Applied Mechanics Finite Element Analysis vs. Machine Learning Optimization and Semantic Reporting Application of Knowledge Graphs in Research Graphs vs Tabular Data Computational graphs Graph Data Science and Graph Machine Learning Combining Knowledge Graphs and Large Language Models (LLMs) Practical Applications and Projects Challenges and Learnings Anahita’s RecommendationsLinks:
GitHub repo: https://github.com/antahiap/ADPT-LRN-PHYS/tree/mainFree Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Tereza’s background Switching from an Individual Contributor to Lead Python Pizza and the pizza management metaphor Learning to figure things out on your own and how to receive feedback Tereza as a leadership coach Podcasts Tereza’s coaching framework (selling yourself vs bragging) The importance of retrospectives The importance of communication and active listening Convincing people you don’t have power over Building relationships and empathy Inclusive leadershipLinks:
LinkedIn: https://www.linkedin.com/in/tereza-iofciu/ Twitter: https://twitter.com/terezaif Github: https://github.com/terezaif Website: https:// terezaiofciu.comFree Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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Links:
VectorHub: https://superlinked.com/vectorhub/?utm_source=community&utm_medium=podcast&utm_campaign=datatalks Daniel's LinkedIn: https://www.linkedin.com/in/svonava/Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.htmlThis podcast is sponsored by VectorHub, a free open-source learning community for all things vector embeddings and information retrieval systems.
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We talked about:
Reem’s background Context-aware sensing and transfer learning Shifting focus from PhD to industry Reem’s experience with startups and dealing with prejudices towards PhDs AI interviewing solution How candidates react to getting interviewed by an AI avatar End-to-end overview of a machine learning project The pitfalls of using LLMs in your process Mitigating biases Addressing specific requirements for specific roles Reem’s resource recommendationsLinks:
LinkedIn: https://www.linkedin.com/in/reemmahmoud/recent-activity/all/ Website: https://topmate.io/reem_mahmoudFree Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Sara’s background On being a Google PhD fellow Sara’s volunteer work Finding AI volunteer work Sara’s Fruit Punch challenge How to take part in AI challenges AI Wonder Girls Hackathons Things people often miss in AI projects and hackathons Getting creative Fostering your social media Tips on applying for volunteer projects Why it’s worth doing volunteer projects Opportunities for data engineers and students Sara’s newsletter suggestionsLinks:
Dev and AI hackathons: https://devpost.com/ Healthcare-focused challenges: https://grand-challenge.org/challenges/ Volunteering in projects (AI4Good): https://www.fruitpunch.ai/ Volunteering in projects (AI4Good) 2: https://www.omdena.com/ Twitter: https://twitter.com/el_ateifSara Instagram: https://www.instagram.com/saraelateif/ LinkedIn: https://www.linkedin.com/in/sara-el-ateif/ Youtube: www.youtube.com/@elateifsaraFree Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Sarah’s background How Sarah became a coach and found her niche Sarah’s clients How Sarah helps her clients find the perfect job Finding a specialization Informational interviews Building a connection for mutual benefit The networking strategy Listing your projects in the CV The importance of doing research yourself and establishing your interests How to land a part-time job when the company wants full-time Age is not a factor Applying for jobs after finishing a course and the importance of sharing your learnings Sarah resource recommendationsLinks:
LinkedIn: https://www.linkedin.com/in/sarahmestiri/ Website: https://thrivingcareermoms.com/ Personal Website: https://www.sarahmestiri.com/ Youtube channel: https://www.youtube.com/@thrivingcareermoms444Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Nemanja’s background When Nemanja first work as a data person Typical problems that ML Ops folks solve in the financial sector What Nemanja currently does as an ML Engineer The obstacle of implementing new things in financial sector companies Going through the hurdles of DevOps Working with an on-premises cluster “ML Ops on a Shoestring” (You don’t need fancy stuff to start w/ ML Ops) Tactical solutions Platform work and code work Programming and soft skills needed to be an ML Engineer The challenges of transitioning from and electrical engineering and sales to ML Ops The ML Ops tech stack for beginners Working on projects to determine which skills you needLinks:
LinkedIn: https://www.linkedin.com/in/radojkovic/Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Ivan’s background How Ivan became interested in investing Getting financial data to run simulations Open, High, Low, Close, Volume Risk management strategy Testing your trading strategies Sticking to your strategy Important metrics and remembering about trading fees Important features Deployment How DataTalks.Club courses helped Ivan Ivan’s site and course sign-upLinks:
Exploring Finance APIs: https://pythoninvest.com/long-read/exploring-finance-apis Python Invest Blog Articles: https://pythoninvest.com/blogFree ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Rob’s background Going from software engineering to Bayesian modeling Frequentist vs Bayesian modeling approach About integrals Probabilistic programming and samplers MCMC and Hakaru Language vs library Encoding dependencies and relationships into a model Stan, HMC (Hamiltonian Monte Carlo) , and NUTS Sources for learning about Bayesian modeling Reaching out to RobLinks:
Book 1: https://bayesiancomputationbook.com/welcome.html Book/Course: https://xcelab.net/rm/statistical-rethinking/Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Atita’s background How NLP relates to search Atita’s experience with Lucidworks and OpenSource Connections Atita’s experience with Qdrant and vector databases Utilizing vector search Major changes to search Atita has noticed throughout her career RAG (Retrieval-Augmented Generation) Building a chatbot out of transcripts with LLMs Ingesting the data and evaluating the results Keeping humans in the loop Application of vector databases for machine learning Collaborative filtering Atita’s resource recommendationsLinks:
LinkedIn: https://www.linkedin.com/in/atitaarora/ Twitter: https://x.com/atitaarora Github: https://github.com/atarora Human-in-the-Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning Relevant Search: https://www.manning.com/books/relevant-search Let's learn about Vectors: https://hub.superlinked.com/Langchain: https://python.langchain.com/docs/get_started/introduction Qdrant blog: https://blog.qdrant.tech/ OpenSource Connections Blog: https://opensourceconnections.com/blog/Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Adrian’s background The benefits of freelancing Having an agency vs freelancing What let Adrian switch over from freelancing The conception of DLT (Growth Full Stack) The investment required to start a company Growth through the provision of services Growth through teaching (product-market fit) Moving on to creating docs Adrian’s current role Strategic partnerships and community growth through DocDB Plans for the future of DLT DLT vs Airbyte vs Fivetran Adrian’s resource recommendationsLinks:
Adrian's LinkedIn: https://www.linkedin.com/in/data-team/ Twitter: https://twitter.com/dlt_library Github: https://github.com/dlt-hub/dlt Website: https://dlthub.com/docs/introFree ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Dimitri’s background The first steps of transitioning into freelance Working with recruiters (contracting) Deciding on what to charge for your services Establishing your network Self-marketing Contracting vs freelancing Which channel is better for those starting out? Cutting out the middleman Where to look for clients and how to vet them The different way of getting into freelancing Going back to a full-time job after freelancing Common mistakes freelancers make Dimitri’s resource suggestions Reaching out to DimitriLinks:
LinkedIn profile: http://www.linkedin.com/in/visnadi The DataFreelancer website: https://thedatafreelancer.com/Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html
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We talked about:
Maria’s background Deciding to go into telecare (healthcare) Current difficulties in healthcare Getting into the healthcare industry as a lifestyle brand The importance of a plan B and being flexible What is SQIN and the importance of communication Going from lipstick to skin health analysis The importance of community and broadening your audience The importance of feedback and communicating benefits The current state and growth of SQIN Convincing investors and the importance of proving profitability Maria’s role at SQIN Balancing a newborn child and a new companyLinks:
Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html - Se mer
