We talked about:

Santona's background Focusing on data workflows Upsolver vs DBT ML pipelines vs Data pipelines MLOps vs DataOps Tools used for data pipelines and ML pipelines The “modern data stack” and today's data ecosystem Staging the data and the concept of a “lakehouse” Transforming the data after staging What happens after the modeling phase Human-centric vs Machine-centric pipeline Applying skills learned in academia to ML engineering Crafting user personas based on real stories A framework of curiosity Santona's book and resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/santona-tuli/ Upsolver website: upsolver.com Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Hugo's background Why do tools and the companies that run them have wildly different names Hugo's other projects beside Metaflow Transitioning from educator to DevRel What is DevRel? DevRel vs Marketing How DevRel coordinates with developers How DevRel coordinates with marketers What skills a DevRel needs The challenges that come with being an educator Becoming a good writer: nature vs nurture Hugo's approach to writing and suggestions Establishing a goal for your content Choosing a form of media for your content Is DevRel intercompany or intracompany? The Vanishing Gradients podcast Finding Hugo online

Links:

Hugo Browne's github: http://hugobowne.github.io/ Vanishing Gradients: https://vanishinggradients.fireside.fm/ MLOps and DevOps: Why Data Makes It Differenthttps://www.oreilly.com/radar/mlops-and-devops-why-data-makes-it-different/ Evaluate Metaflow for free, right from your Browser: https://outerbounds.com/sandbox/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about;

Antonis' background The pros and cons of working for a startup Useful skills for working at a startup and the Lean way to work How Antonis joined the DataTalks.Club community Suggestions for students joining the MLOps course Antonis contributing to Evidently AI How Antonis started freelancing Getting your first clients on Upwork Pricing your work as a freelancer The process after getting approved by a client Wearing many hats as a freelancer and while working at a startup Other suggestions for getting clients as a freelancer Antonis' thoughts on the Data Engineering course Antonis' resource recommendations

Links:

Lean Startup by Eric Ries: https://theleanstartup.com/ Lean Analytics: https://leananalyticsbook.com/ Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ Kafka Streaming with python by Khris Jenkins tutorial video: https://youtu.be/jItIQ-UvFI4

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

Bart's background What is data governance? Data dictionaries and data lineage Data access management How to learn about data governance What skills are needed to do data governance effectively When an organization needs to start thinking about data governance Good data access management processes Data masking and the importance of automating data access DPO and CISO roles How data access management works with a data mesh approach Avoiding the role explosion problem The importance of data governance integration in DataOps Terraform as a stepping stone to data governance How Raito can help an organization with data governance Open-source data governance tools

Links:

LinkedIn: https://www.linkedin.com/in/bartvandekerckhove/ Twitter: https://twitter.com/Bart_H_VDK Github: https://github.com/raito-io Website: https://www.raito.io/ Data Mesh Learning Slack: https://data-mesh-learning.slack.com/join/shared_invite/zt-1qs976pm9-ci7lU8CTmc4QD5y4uKYtAA#/shared-invite/email DataQG Website: https://dataqg.com/ DataQG Slack: https://dataqgcommunitygroup.slack.com/join/shared_invite/zt-12n0333gg-iTZAjbOBeUyAwWr8I~2qfg#/shared-invite/email DMBOK (Data Management Book of Knowledge): https://www.dama.org/cpages/body-of-knowledge DMBOK Wheel describing the data governance activities: https://www.dama.org/cpages/dmbok-2-wheel-images

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Boyan's background What is data strategy? Due diligence and establishing a common goal Designing a data strategy Impact assessment, portfolio management, and DataOps Data products DataOps, Lean, and Agile Data Strategist vs Data Science Strategist The skills one needs to be a data strategist How does one become a data strategist? Data strategist as a translator Transitioning from a Data Strategist role to a CTO Using ChatGPT as a writing co-pilot Using ChatGPT as a starting point How ChatGPT can help in data strategy Pitching a data strategy to a stakeholder Setting baselines in a data strategy Boyan's book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/angelovboyan/ Twitter: https://twitter.com/thinking_code Github: https://github.com/boyanangelov Website: https://boyanangelov.com/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

Katharine's background Katharine's ML privacy startup GDPR, CCPA, and the “opt-in as the default” approach What is data privacy? Finding Katharine's book – Practical Data Privacy The various definitions of data privacy and “user profiles” Privacy engineering and privacy-enhancing technologies Why data privacy is important What is differential privacy? The importance of keeping privacy in mind when designing systems Data privacy on the example of ChatGPT Katharine's resource suggestions for learning about data privacy

Links:

LinkedIn: https://www.linkedin.com/in/katharinejarmul/

Twitter: https://twitter.com/kjam

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

Arseny's background Working on machine learning in startups What is Machine Learning System Design? Constraints and requirements Known unknowns vs unknown unknowns (Design stage) Writing a design document Technical problems vs product-oriented problems The solution part of the Design Document What motivated Arseny to write a book on ML System Design Examples of a Design Document in the book The types of readers for ML System Design Working with the co-author Reacting to constraints and feedback when writing a book Arseny's favorite chapter of the book Other resources where you can learn about ML System Design Twitter Giveaway

Links:

Book: https://www.manning.com/books/machine-learning-system-design?utm_source=AGMLBookcamp&utm_medium=affiliate&utm_campaign=book_babushkin_machine_4_25_23&utm_content=twitter Discount: poddatatalks21 (35% off)

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Johannes’s background Johannes’s Open Source Spotlight demos – Refinery and Bricks The difficulties of working with natural language processing (NLP) Incorporating ChatGPT into a process as a heuristic What is Bricks? The process of starting a startup – Kern Making the decision to go with open source Pros and cons of launching as open source Kern’s business model Working with enterprises Johannes as a salesperson The team at Kern Johannes’s role at Kern How Johannes and Henrik separate responsibilities at Kern Working with very niche use cases The short story of how Kern got its funding Johannes’s resource recommendation

Links:

Refinery's GitHub repo: https://github.com/code-kern-ai/refinery Bricks' Github repo: https://github.com/code-kern-ai/bricks Bricks Open Source Spotlight demo: https://www.youtube.com/watch?v=r3rXzoLQy2U Refinery Open Source Spotlight demo: https://www.youtube.com/watch?v=LlMhN2f7YDg Discord: https://discord.com/invite/qf4rGCEphW Ker's Website: https://www.kern.ai

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Aaisha’s background How homeschooling affects self-study Deciding on what to learn about Establishing whether a resource is good How Aaisha focuses on learning Deciding on what kind of project to build Find research materials Aaisha’s experience with the Data Talks Club ML Zoomcamp ML Zoomcamp projects Aaisha’s interest in bioinformatics Keeping motivated with deadlines Notes and time-tracking tools Drawbacks to self-studying Aaisha’s interest in machine learning Aaisha’s least favorable part of ML Zoomcamp Helping people as a way to learn Using ChatGPT as a “study group” Is it possible to use self-studying to learn high-level topics Switching topics to avoid burnout Aaisha’s resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/aaisha-muhammad/ Twitter: https://twitter.com/ZealousMushroom Github: https://github.com/AaishaMuhammad Website: http://www.aaishamuhammad.co.za/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Shir’s background Debrief culture The responsibilities of a group manager Defining the success of a DS manager The three pillars of data science management Managing up Managing down Managing across Managing data science teams vs business teams Scrum teams, brainstorming, and sprints The most important skills and strategies for DS and ML managers Making sure proof of concepts get into production

Links:

The secret sauce of data science management: https://www.youtube.com/watch?v=tbBfVHIh-38 Lessons learned leading AI teams: https://blogs.intuit.com/2020/06/23/lessons-learned-leading-ai-teams/ How to avoid conflicts and delays in the AI development process (Part I): https://blogs.intuit.com/2020/12/08/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-i/ How to avoid conflicts and delays in the AI development process (Part II): https://blogs.intuit.com/2021/01/06/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-ii/ Leading AI teams deck: https://drive.google.com/drive/folders/1_CnqjugtsEbkIyOUKFHe48BeRttX0uJG Leading AI teams video: https://www.youtube.com/watch?app=desktop&v=tbBfVHIh-38

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Nadia’s background Academic research in software engineering Design patterns Software engineering for ML systems Problems that people in industry have with software engineering and ML Communication issues and setting requirements Artifact research in open source products Product vs model Nadia’s open source product dataset Failure points in machine learning projects Finding solutions to issues using Nadia’s dataset and experience The problem of siloing data scientists and other structure issues The importance of documentation and checklists Responsible AI How data scientists and software engineers can work in an Agile way

Links:

Model Card: https://arxiv.org/abs/1810.03993 Datasheets: https://arxiv.org/abs/1803.09010 Factsheets: https://arxiv.org/abs/1808.07261 Research Paper: https://www.cs.cmu.edu/~ckaestne/pdf/icse22_seai.pdf Arxiv version: https://arxiv.org/pdf/2110.

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Aleksander’s background The difficulty of selling data stack as a service How Aleksander got into consulting The Mom Test – extracting feedback from people User interviews Why Aleksander’s data stack as a service startup was not viable How Aleksander decided to switch to consulting Finding clients to consult Figuring out how to position your services Geographical limitations Figuring out your target audience The importance of networking and marketing Pricing your services The pitfalls of daily and hourly pricing and how to balance incentives Is Germany a good place to found a company? Aleksander’s book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/alkrusz/ Twitter: https://twitter.com/alkrusz Website: www.leukos.io

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Ruslan’s background Fighting procrastination and perfectionism What is biohacking? The role of dopamine and other hormones in daily life How meditation can help The influence light has on our bodies Behavioral biohacking Daylight lamps and using light to wake up Sleep cycles How nutrition affects productivity Measuring productivity Examples of unsuccessful biohacking attempts Stoicism, voluntary discomfort, and self-challenges Biohacking risks and ways to prevent them Coffee and tea biohacking Using self-reflection and tracking to measure results Mindset shifting Stoicism book recommendation Work/life balance Ruslan’s biohacking resource recommendation

Links:

LinkedIn: https://www.linkedin.com/in/ruslanshchuchkin/

ree data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Parvathy’s background Brainstorming sessions with nonprofits to establish data maturity Example of an Analytics for a Better World project The overall data maturity situation of nonprofits vs private sector Solving the skill gap Publicly available content The Analytics for a Better World Academy The Academy’s target audience How researchers can work with Analytics for a Better World Improving data maturity in nonprofit organizations People, processes, and technology Typical tools that Analytics for a Better World recommends to nonprofits Profiles in nonprofits Does Analytics for a Better World has a need for data engineers? The Analytics for a Better World team Factors that help organizations become more data-driven Parvathy’s resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/parvathykrishnank/ Twitter:  https://twitter.com/ABWInstitute Github: https://github.com/Analytics-for-a-Better-World Website:  https://analyticsbetterworld.org/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about: 

Dania’s background Founding the AI Guild Datalift Summit Coming up with meetup topics Diversity in Berlin Other types of diversity besides gender The pitfalls of lacking diversity Creating an environment where people can safely share their experiences How the AI Guild helps organizations become more diverse How the AI guild finds women in the fields of AI and data science Advice for people in underrepresented groups Organizing a welcoming environment and creating a code of conduct AI Guild’s consulting work and community AI Guild team Dania’s resource recommendations Upcoming Datalift Summit

Links:

Call for Speakers for the #datalift summit (Berlin, 14 to 16 June 2023): https://eu1.hubs.ly/H02RXvX0 Coded Bias documentary on Netflix: https://www.netflix.com/de/title/81328723#:~:text=This%20documentary%20investigates%20the%20bias,flaws%20in%20facial%20recognition%20technology. Book Weapons of Math Destruction by Cathy O'Neil: https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction Book Lean In by Sheryl Sandberg: https://en.wikipedia.org/wiki/Lean_In

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Tatiana’s background Going from academia to healthcare to the tech industry What staff engineers do Transferring skills from academia to industry and learning new ones The importance of having mentors Skipping junior and mid-level straight into the staff role Convincing employers that you can take on a lead role Seeing failure as a learning opportunity Preparing for coding interviews Preparing for behavioral and system design interviews The importance of having a network and doing mock interviews How much do staff engineers work with building pipelines, data science, ETC, MPOps, etc.? Context switching Advice for those going from academia to industry The most exciting thing about working as an AI staff engineer Tatiana’s book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/tatigabru/  Twitter:  https://twitter.com/tatigabru Github: https://github.com/tatigabru Website:  http://tatigabru.com/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Jekaterina’s background How Jekaterina started freelancing Jekaterina’s initial ways of getting freelancing clients How being a generalist helped Jekaterina’s career Connecting business and data How Jekaterina’s LinkedIn posts helped her get clients Jekaterina’s work in fundraising Cohorts and KPIs Improving communication between the data and business teams Motivating every link in the company’s chain The cons of freelancing Balancing projects and networking The importance of enjoying what you do Growing the client base In the office work vs working remotely Jekaterina’s advice who people who feel stuck Jekaterina’s resource recommendations

Links:

Jekaterina's LinkedIn: https://www.linkedin.com/in/jekaterina-kokatjuhha/

Join DataTalks.Club: https://datatalks.club/slack.html

We talked about

Chris’s background Switching careers multiple times Freedom at companies Chris’s role as an internal consultant Chris’s sabbatical ChatGPT How being a generalist helped Chris in his career The cons of being a generalist and the importance of T-shaped expertise The importance of learning things you’re interested in Tips to enjoy learning new things Recruiting generalists The job market for generalists vs for specialists Narrowing down your interests Chris’s book recommendations

Links:

Lex Fridman: science, philosophy, media, AI (especially earlier episodes): https://www.youtube.com/lexfridman Andrej Karpathy, former Senior Director of AI at Tesla, who's now focused on teaching and sharing his knowledge: https://www.youtube.com/@AndrejKarpathy Beautifully done videos on engineering of things in the real world: https://www.youtube.com/@RealEngineering Chris' website: https://szafranek.net/ Zalando Tech Radar: https://opensource.zalando.com/tech-radar/ Modal Labs, new way of deploying code to the cloud, also useful for testing ML code on GPUs: https://modal.com Excellent Twitter account to follow to learn more about prompt engineering for ChatGPT: https://twitter.com/goodside Image prompts for Midjourney: https://twitter.com/GuyP Machine Learning Workflows in Production - Krzysztof Szafanek: https://www.youtube.com/watch?v=CO4Gqd95j6k From Data Science to DataOps: https://datatalks.club/podcast/s11e03-from-data-science-to-dataops.html

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Luke’s background Luke’s podcast - AI Game Changers How Luke helps people get jobs What’s changed in the recruitment market over the last 6 months Getting ready for the interview process Stage “zero” – the filter between the candidate and the company Preparing for the introduction stage – research and communication Reviewing the fundamentals during preparation Preparing for the technical part of the interview Establishing the hiring company’s expectations Depth vs breadth Overly theoretical and mathematical questions in interviews Bombing (failing) in the middle of an interview Applying to different roles within the same company Luke’s resource recommendations

Links:

Luke's LinkedIn: https://www.linkedin.com/in/lukewhipps/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Pauline’s background Pauline’s work as a manager at IBM What is indie hacking? Pauline initial indie hacking projects Getting ready for launch Responsibilities and challenges in indie hacking Pauline’s latest indie hacking project Going live and marketing Challenges with Unreal Me Staying motivated with indie hacking projects Skills Pauline picked up while doing indie hacking projects Balancing a day job and indie hacking Micro SaaS and AboutStartup.io How Pauline comes up with ideas for projects Going from an idea on paper to building a project Pauline’s Twitter success Connecting with Pauline online Pauline’s indie hacking inspiration Pauline’s resource recommendation

Links:

Website: https://wintopy.io/ Pauline's Twitter: https://twitter.com/Pauline_Cx Pauline's LinkedIn: https://www.linkedin.com/in/paulineclavelloux/  Blog about Indiehacking: https://aboutstartup.io

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html