Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

Angela's background Angela's role at Sam's Club The usefulness of knowing ML as a data engineer Angela's career path Transitioning from data analyst to data engineer/system designer Best practices for system design and data engineering Working with document databases Working with network-based databases Detecting fraud with a network-based database Selecting the database type to work with Neo4j vs Postgres The importance of having software engineering knowledge in data engineering Data quality check tooling The greatest challenges in data engineering Debugging and finding the root cause of a failed job What kinds of tools Angela uses on a daily basis Working with external data sources Angela's resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/aramirez1305/ Twitter: https://twitter.com/angelamaria__r Github: https://github.com/aramir62 Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20

Free ML Engineering course: http://mlzoomcamp.com

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

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

We talked about:

Loïc's background Data management Loïc's transition to data engineer Challenges in the transition to data engineering What is a data architect? The output of a data architect's work Establishing metrics and dimensions The importance of communication Setting up best practices for the team Staying relevant and tech-watching Setting up specifications for a pipeline Be agile, create a POC, iterate ASAP, and build reusable templates Reaching out to Loïc for questions

Links:

Loiic LinkedIn: https://www.linkedin.com/in/loicmagnien/

Free ML Engineering course: http://mlzoomcamp.com

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

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

We talked about:

Maria's background Marvelous MLOps Maria's definition of MLOps Alternate team setups without a central MLOps team Pragmatic vs non-pragmatic MLOps Must-have ML tools (categories) Maturity assessment What to start with in MLOps Standardized MLOps Convincing DevOps to implement Understanding what the tools are used for instead of knowing all the tools Maria's next project plans Is LLM Ops a thing? What Ahold Delhaize does Resource recommendations to learn more about MLOps The importance of data engineering knowledge for ML engineers

Links:

LinkedIn: https://www.linkedin.com/company/marvelous-mlops/

Website: https://marvelousmlops.substack.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:

Aleksander's background Aleksander as a Causal Ambassador Using causality to make decisions Counterfactuals and and Judea Pearl Meta-learners vs classical ML models Average treatment effect Reducing causal bias, the super efficient estimator, and model uplifting Metrics for evaluating a causal model vs a traditional ML model Is the added complexity of a causal model worth implementing? Utilizing LLMs in causal models (text as outcome) Text as treatment and style extraction The viability of A/B tests in causal models Graphical structures and nonparametric identification Aleksander's resource recommendations

Links:

The Book of Why: https://amzn.to/3OZpvBk Causal Inference and Discovery in Python: https://amzn.to/46Pperr Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL

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:

Sandra's background Making a YouTube channel to break into the LLM space The business cases for LLMs LLMs as amplifiers The befits of keeping a human in the loop when using LLMs (AI limitations) Using LLMs as assistants Building an app that uses an LLM Prompt whisperers and how to improve your prompts Sandra's 7-day LLM experiment Sandra's LLM content recommendations Finding Sandra online

Links:

LinkedIn: https://www.linkedin.com/in/sandrakublik/ Twitter: https://twitter.com/sandra_kublik Youtube: https://www.youtube.com/@sandra_kublik

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

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) Evidently: https://www.evidentlyai.com/ Article: https://medium.com/people-ai-engineering/design-documents-for-ml-models-bbcd30402ff7

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:

Polina's background How common it is for PhD students to build ML pipelines end-to-end Simultaneous PhD and industry experience Support from both the academic and industry sides How common the industrial PhD setup is and how to get into one Organizational trust theory How price relates to trust How trust relates to explainability The importance of actionability Explainability vs interpretability vs actionability Complex glass box models Does the explainability of a model follow explainability? What explainable AI bring to customers and end users Can all trust be turned into KPI?

Links:

LinkedIn: https://www.linkedin.com/in/polina-mosolova/ Neural Additive Models paper: https://proceedings.neurips.cc/paper/2021/file/251bd0442dfcc53b5a761e050f8022b8-Paper.pdf Neural Basis Model paper: https://arxiv.org/pdf/2205.14120.pdf Interpretable Feature Spaces paper: https://kdd.org/exploration_files/vol24issue1_1._Interpretable_Feature_Spaces_revised.pdf

We talked about:

Simon's background What MLOps is and what it isn't Skills needed to build an ML platform that serves 100s of models Ranking the importance of skills The point where you should think about building an ML platform The importance of processes in ML platforms Weighing your options with SaaS platforms The exploratory setup, experiment tracking, and model registry What comes after deployment? Stitching tools together to create an ML platform Keeping data governance in mind when building a platform What comes first – the model or the platform? Do MLOps engineers need to have deep knowledge of how models work? Is API design important for MLOps? Simon's recommendations for furthering MLOps knowledge

Links:

LinkedIn: https://www.linkedin.com/in/simonstiebellehner/ Github: https://github.com/stiebels Medium: https://medium.com/@sistel

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:

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;

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:

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:

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: 

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

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:

Marysia’s background What data-centric AI is Data-centric Kaggle competitions The mindset shift to data-centric AI Data-centric does not mean you should not iterate on models How to implement the data-centric approach Focusing on the data vs focusing on the model Resources to help implement the data-centric approach Data-centric AI vs standard data cleaning Making sure your data is representative Knowing when your data is good enough The importance of user feedback “Shadow Mode” deployment What to do if you have a lot of bad data or incomplete data Marysia’s role at PyData How Marysia joined PyData The difference between PyData and PyCon Finding Marysia online

Links:

Embetter & Bulk Demo: https://www.youtube.com/watch?v=L---nvDw9KU

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:

Sadat’s background Sadat’s backend engineering experience Sadat’s pivot point as a backend engineer Sadat’s exposure to ML and Data Science Sadat’s Act Before you Think approach (with safety nets) Sadat’s street cred and transition into management The hiring process as an internal candidate The importance of people management skills The Brag List The most difficult part of transitioning to management Focusing on projects and setting milestones Sadat’s transition from EM to data science management How much domain knowledge is needed for management? The main difference between engineering and management How being an EM helped Sadat transition no DS management 53:32 Transitioning to DS management from other roles How to feel accomplished as a manager Sadat’s book recommendations Sadat’s meetups

Links:

Sadat's Meetup page: https://www.meetup.com/berlin-search-technology-meetup/ Meetup event "Bias in AI: how to measure it and how to fix it event": https://www.meetup.com/data-driven-ai-berlin-meetup/events/289927565/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Irina’s background Irina as a mentor Designing curriculum and program management at AI Guild Other things Irina taught at AI Guild Why Irina likes teaching Students’ reluctance to learn cloud Irina as a manager Cohort analysis in a nutshell How Irina started teaching formally Irina’s diversity project in the works How DataTalks.Club can attract more female students to the Zoomcamps How to get technical feedback at work Antipatterns and overrated/overhyped topics in data analytics Advice for young women who want to get into data science/engineering Finding Irina online Fundamentals for data analysts Suggestions for DataTalks.club collaborations Conclusions

Links:

LinkedIn Account: https://www.linkedin.com/in/irinabrudaru/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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