Episodit
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In this podcast episode, we talked with Isabella Bicalho about Career advice, learning, and featuring women in ML and AI.
About the Speaker:
Isabella is a Machine Learning Engineer and Data Scientist with three years of hands-on AI development experience. She draws upon her early computational research expertise to develop ML solutions. While contributing to open-source projects, she runs a newsletter dedicated to showcasing women's accomplishments in data science.
During this event, the guest discussed her transition into machine learning, her freelance work in AI, and the growing AI scene in France. She shared insights on freelancing versus full-time work, the value of open-source contributions, and developing both technical and soft skills. The conversation also covered career advice, mentorship, and her Substack series on women in data science, emphasizing leadership, motivation, and career opportunities in tech.0:00 Introduction1:23 Background of Isabella Bicalho2:02 Transition to machine learning4:03 Study and work experience5:00 Living in France and language learning6:03 Internship experience8:45 Focus areas of Inria9:37 AI development in France10:37 Current freelance work11:03 Freelancing in machine learning13:31 Moving from research to freelancing14:03 Freelance vs. full-time data science17:00 Finding first freelance client18:00 Involvement in open-source projects20:17 Passion for open-source and teamwork23:52 Starting new projects25:03 Community project experience26:02 Teaching and learning29:04 Contributing to open-source projects32:05 Open-source tools vs. projects33:32 Importance of community-driven projects34:03 Learning resources36:07 Green space segmentation project39:02 Developing technical and soft skills40:31 Gaining insights from industry experts41:15 Understanding data science roles41:31 Project challenges and team dynamics42:05 Turnover in open-source projects43:05 Managing expectations in open-source work44:50 Mentorship in projects46:17 Role of AI tools in learning47:59 Overcoming learning challenges48:52 Discussion on substack49:01 Interview series on women in data50:15 Insights from women in data science51:20 Impactful stories from substack53:01 Leadership challenges in projects54:19 Career advice and opportunities56:07 Motivating others to step out of comfort zone57:06 Contacting for substack story sharing58:00 Closing remarks and connections
🔗 CONNECT WITH ISABELLA BICALHOGithub: github https://github.com/bellabf LinkedIn: / isabella-frazeto
🔗 CONNECT WITH DataTalksClubJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.htmlDatalike Substack - https://datalike.substack.com/LinkedIn: / datatalks-club
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Reflection on an Almost Two-Year Journey of Generative AI in Industry – Maria Sukhareva
About the speaker:
Maria Sukhareva is a principal key expert in Artificial Intelligence in Siemens with over 15 years of experience at the forefront of generative AI technologies. Known for her keen eye for technological innovation, Maria excels at transforming cutting-edge AI research into practical, value-driven tools that address real-world needs. Her approach is both hands-on and results-focused, with a commitment to creating scalable, long-term solutions that improve communication, streamline complex processes, and empower smarter decision-making. Maria's work reflects a balanced vision, where the power of innovation is met with ethical responsibility, ensuring that her AI projects deliver impactful and production-ready outcomes.
We talked about:
00:00 DataTalks.Club intro
02:13 Career journey: From linguistics to AI
08:02 The Evolution of AI Expertise and its Future
13:10 AI vulnerabilities: Bypassing bot restrictions
17:00 Non-LLM classifiers as a more robust solution
22:56 Risks of chatbot deployment: Reputational and financial
27:13 The role of AI as a tool, not a replacement for human workers
31:41 The role of human translators in the age of AI
34:49 Evolution of English and its Germanic roots
38:44 Beowulf and Old English
39:43 Impact of the Norman occupation on English grammar
42:34 Identifying mushrooms with AI apps and safety precautions
45:08 Decoding ancient languages like Sumerian
49:43 The evolution of machine translation and multilingual models
53:01 Challenges with low-resource languages and inconsistent orthography
57:28 Transition from academia to industry in AI
Join our Slack: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
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Puuttuva jakso?
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We talked about:
00:00 DataTalks.Club intro
00:00 Large Hadron Collider and Mentorship
02:35 Career overview and transition from physics to data science
07:02 Working at the Large Hadron Collider
09:19 How particles collide and the role of detectors
11:03 Data analysis challenges in particle physics and data science similarities
13:32 Team structure at the Large Hadron Collider
20:05 Explaining the connection between particle physics and data science
23:21 Software engineering practices in particle physics
26:11 Challenges during interviews for data science roles
29:30 Mentoring and offering advice to job seekers
40:03 The STAR method and its value in interviews
50:32 Paid vs unpaid mentorship and finding the right fit
About the speaker:
Anastasia is a particle physicist turned data scientist, with experience in large-scale experiments like those at the Large Hadron Collider. She also worked at Blue Yonder, scaling AI-driven solutions for global supply chain giants, and at Kaufland e-commerce, focusing on NLP and search. Anastasia is a mentor for Ml/AI, dedicated to helping her mentees achieve their goals. She is passionate about growing the next generation of data science elite in Germany: from Data Analysts up to ML Engineers.
Join our Slack: https://datatalks .club/slack.html
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We talked about:
00:00 DataTalks.Club intro
02:34 Career journey and transition into MLOps
08:41 Dutch agriculture and its challenges
10:36 The concept of "technical debt" in MLOps
13:37 Trade-offs in MLOps: moving fast vs. doing things right
14:05 Building teams and the role of coordination in MLOps
16:58 Key roles in an MLOps team: evangelists and tech translators
23:01 Role of the MLOps team in an organization
25:19 How MLOps teams assist product teams
27 :56 Standardizing practices in MLOps
32:46 Getting feedback and creating buy-in from data scientists
36:55 The importance of addressing pain points in MLOps
39:06 Best practices and tools for standardizing MLOps processes
42:31 Value of data versioning and reproducibility
44:22 When to start thinking about data versioning
45:10 Importance of data science experience for MLOps
46:06 Skill mix needed in MLOps teams
47:33 Building a diverse MLOps team
48:18 Best practices for implementing MLOps in new teams
49:52 Starting with CI/CD in MLOps
51:21 Key components for a complete MLOps setup
53:08 Role of package registries in MLOps
54:12 Using Docker vs. packages in MLOps
57:56 Examples of MLOps success and failure stories
1:00:54 What MLOps is in simple terms
1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance
Join our Slack: https://datatalks .club/slack.html
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We talked about:
00:00 DataTalks.Club intro01:56 Using data to create livable cities02:52 Rachel's career journey: from geography to urban data science04:20 What does a transport scientist do?05:34 Short-term and long-term transportation planning06:14 Data sources for transportation planning in Singapore08:38 Rachel's motivation for combining geography and data science10:19 Urban design and its connection to geography13:12 Defining a livable city15:30 Livability of Singapore and urban planning18:24 Role of data science in urban and transportation planning20:31 Predicting travel patterns for future transportation needs22:02 Data collection and processing in transportation systems24:02 Use of real-time data for traffic management27:06 Incorporating generative AI into data engineering30:09 Data analysis for transportation policies33:19 Technologies used in text-to-SQL projects36:12 Handling large datasets and transportation data in Singapore42:17 Generative AI applications beyond text-to-SQL45:26 Publishing public data and maintaining privacy45:52 Recommended datasets and projects for data engineering beginners49:16 Recommended resources for learning urban data science
About the speaker:
Rachel is an urban data scientist dedicated to creating liveable cities through the innovative use of data. With a background in geography, and a masters in urban data science, she blends qualitative and quantitative analysis to tackle urban challenges. Her aim is to integrate data driven techniques with urban design to foster sustainable and equitable urban environments.
Links: - https://datamall.lta.gov.sg/content/datamall/en/dynamic-data.html 00:00 DataTalks.Club intro01:56 Using data to create livable cities02:52 Rachel's career journey: from geography to urban data science04:20 What does a transport scientist do?05:34 Short-term and long-term transportation planning06:14 Data sources for transportation planning in Singapore08:38 Rachel's motivation for combining geography and data science10:19 Urban design and its connection to geography13:12 Defining a livable city15:30 Livability of Singapore and urban planning18:24 Role of data science in urban and transportation planning20:31 Predicting travel patterns for future transportation needs22:02 Data collection and processing in transportation systems24:02 Use of real-time data for traffic management27:06 Incorporating generative AI into data engineering30:09 Data analysis for transportation policies33:19 Technologies used in text-to-SQL projects36:12 Handling large datasets and transportation data in Singapore42:17 Generative AI applications beyond text-to-SQL45:26 Publishing public data and maintaining privacy45:52 Recommended datasets and projects for data engineering beginners49:16 Recommended resources for learning urban data scienceJoin our slack: https: //datatalks.club/slack.html
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We talked about:
00:00 DataTalks.Club intro
00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev
02:29 The founding of DataTalks .Club
03:52 Alexey's transition from Java work to DataTalks.Club
04:58 Growth and success of DataTalks.Club courses
12:04 Motivation behind creating a free-to-learn community
24:03 Staying updated in data science through pet projects
26 :37 Hosting a second podcast and maintaining programming skills
28:56 Skepticism about LLMs and their relevance
31:53 Transitioning to DataTalks.Club and personal reflections
33:32 Memorable moments and the first event's success
36:19 Community building during the pandemic
38:31 AI's impact on data analysts and future roles
42:24 Discussion on AI in healthcare
44:37 Age and reflections on personal milestones
47:54 Building communities and personal connections
49:34 Future goals for the community and courses
51:18 Community involvement and engagement strategies
53:46 Ideas for competitions and hackathons
54:20 Inviting guests to the podcast
55:29 Course updates and future workshops
56:27 Podcast preparation and research process
58:30 Career opportunities in data science and transitioning fields
1:01 :10 Book recommendations and personal reading experiences
About the speaker:
Alexey Grigorev is the founder of DataTalks.Club.
Join our slack: https://datatalks.club/slack.html
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We talked about:
00:00 DataTalks.Club intro
08:06 Background and career journey of Katarzyna
09:06 Transition from linguistics to computational linguistics
11:38 Merging linguistics and computer science
15:25 Understanding phonetics and morpho-syntax
17:28 Exploring morpho-syntax and its relation to grammar
20:33 Connection between phonetics and speech disorders
24:41 Improvement of voice recognition systems
27:31 Overview of speech recognition technology
30:24 Challenges of ASR systems with atypical speech
30:53 Strategies for improving recognition of disordered speech
37:07 Data augmentation for training models
40:17 Transfer learning in speech recognition
42:18 Challenges of collecting data for various speech disorders
44:31 Stammering and its connection to fluency issues
45:16 Polish consonant combinations and pronunciation challenges
46:17 Use of Amazon Transcribe for generating podcast transcripts
47:28 Role of language models in speech recognition
49:19 Contextual understanding in speech recognition
51:27 How voice recognition systems analyze utterances
54:05 Personalization of ASR models for individuals
56:25 Language disorders and their impact on communication
58:00 Applications of speech recognition technology
1:00:34 Challenges of personalized and universal models
1:01:23 Voice recognition in automotive applications
1:03:27 Humorous voice recognition failures in cars
1:04:13 Closing remarks and reflections on the discussion
About the speaker:
Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis.
Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.
Join our slack: https://datatalks.club/slack.html
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0:00
hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love
0:06
data and we have weekly events and today one is one of such events and I guess we
0:12
are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so
0:19
much because this is the time we usually have uh uh our events uh for our guests
0:27
and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of
0:34
slipped my mind but anyways we have a lot of events you can check them in the
0:41
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
0:48
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
0:56
to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome
1:02
as the one today and of course very important do not forget to join our community where you can hang out with
1:09
other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click
1:18
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
1:39
but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I
1:46
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
1:53
need like you we'll need to focus on answering questions and I'll keep an eye
1:58
I'll be keeping an eye on all the question questions so um
2:04
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
2:18
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
3:37
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
5:11
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
6:20
database so we built a column or database inside of SQL Server um so uh
6:26
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
9:14
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
10:11
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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