DataTalks.Club

Links:

• 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:

• Christoph’s background
• Kaggle and other competitions
• How Christoph became interested in interpretable machine learning
• Interpretability vs Accuracy
• Christoph’s current competition engagement
• How Christoph chooses topics for books
• Why Christoph started the writing journey with a book
• Self-publishing vs via a publisher
• Christoph’s other books
• What is conformal prediction?
• Christoph’s book on SHAP
• Explainable AI vs Interpretable AI
• Working alone vs with other people
• Christoph’s other engagements and how to stay hands-on
• Keeping a logbook
• Does one have to be an expert on the topic to write a book about it?
• Writing in the open and other feedback gathering methods
• Advice for those who want to be technical writers
• Self-publishing tools
• Finding Christoph online

Links:

• LinkedIn: https://www.linkedin.com/in/christoph-molnar/
• Website: https://christophmolnar.com/

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:

• Jack’s background
• Transitioning from IC to management
• Lesson not taught in traditional school
• The importance of people’s perception, trust, and respect
• How soft skills are relevant to machine learning
• How to put on a salesman hat in machine learning management
• The importance of visuals and building a POC as fast as possible
• 1st Rule of Machine Learning – don’t be afraid to start without machine learning
• The importance of understanding the reality that data represents
• The importance of putting yourself in the shoes of customers
• The importance of software engineering skills in machine learning
• Where to find Jack’s content
• Jack’s next venture

Links:

• Jack's LinkedIn profile: https://www.linkedin.com/in/jackblandin/

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

Links:

• Mini sound bath: https://www.youtube.com/watch?v=g-lDrcSqcrQ

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:

• Lera’s background
• Lera’s move from Ukraine to Germany
• The transition from Marketing to Product Ownership
• The importance of communication and one-on-ones
• The role of Product Owner
• Utilizing Scrum as a Product Owner
• Building teams and cross-functionality
• Lera’s experience learning about search
• The importance of having both technical knowledge and business context
• Open developer positions at AUTODOC
• What experience Lera came to AUTODOC with
• How marketing skills helped Lera in her current role
• Lera’s resource recommendations
• Everything is possible

Links:

• Post: https://www.linkedin.com/posts/leracaiman_elasticsearch-ecommerce-activity-7106615081588674560-5WQO

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

Links:

• LinkedIn: https://www.linkedin.com/in/ioannis-mesionis/
• Github: https://github.com/ioannismesionis
• Website: https://ioannismesionis.github.io/

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

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

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:

• José's background
• How José relocated to Norway and his schedule
• Tech companies in Norway and José role
• Challenges of working as a remote data engineer
• José's newsletter on how to make use of data
• The process of making data useful
• Where José gets inspiration for his newsletter
• Dealing with burnout
• When in Norway, do as the Norwegians do
• The legalities of working remotely in Norway
• The benefits of working remotely

Links:

• LinkedIn: https://www.linkedin.com/in/jmssalas
• Github: https://github.com/jmssalas
• Website & Newsletter: https://jmssalas.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:

• 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

We talked about:

• Meryam's background
• The constant evolution of startups
• How Meryam became interested in LLMs
• What is an LLM (generative vs non-generative models)?
• Why LLMs are important
• Open source models vs API models
• What TitanML does
• How fine-tuning a model helps in LLM use cases
• Fine-tuning generative models
• How generative models change the landscape of human work
• How to adjust models over time
• Vector databases and LLMs
• How to choose an open source LLM or an API
• Measuring input data quality
• Meryam's resource recommendations

Links:

• Website: https://www.titanml.co/
• Beta docs: https://titanml.gitbook.io/iris-documentation/overview/guide-to-titanml...
• Using llama2.0 in TitanML Blog: https://medium.com/@TitanML/the-easiest-way-to-fine-tune-and-inference-llama-2-0-8d8900a57d57
• Discord: https://discord.gg/83RmHTjZgf
• Meryem LinkedIn: https://www.linkedin.com/in/meryemarik/

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:

• Bela's background
• Why startups even need investors
• Why open source is a viable go-to-market strategy
• Building a bottom-up community
• The investment thesis for the TKM Family Office and the blurriness of the funding round naming convention
• Angel investors vs VC Funds vs family offices
• Bela's investment criteria and GitHub stars as a metric
• Inbound sourcing, outbound sourcing, and investor networking
• Making a good impression on an investor
• Balancing open and closed source parts of a product
• The future of open source
• Recent successes of open source companies
• Bela's resource recommendations

Links:

• Understand who is engaging with your open source project article: https://www.crowd.dev/
• Top 6 Books on Developer Community Building: https://www.crowd.dev/post/top-6-books-on-developer-community-building
• Which open source software metrics matter: https://www.bvp.com/atlas/measuring-the-engagement-of-an-open-source-software-community#Which-open-source-software-metrics-matter

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:

• 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:

• Rosona’s background
• How mathematics knowledge helps in industry
• What is industrial data?
• Setting up an industrial process using blue paint
• Internet companies’ data vs industrial data
• Explaining industrial processes using packing peanuts
• Why productive industry needs data
• Measuring product qualities
• How data specialists use industrial data
• Defining and measuring sustainability
• Using data in reactionary measures to changing regulations
• Types of industrial data
• Solving problems and optimizing with industrial data
• Industrial solvers
• Tiny data vs Big data in productive industry
• The advantages of coming from academia into productive industry
• Materials and resources for industrial data
• Women in industry
• Why Rosona decided to shift to industrial data

Links:

• Kaggle dataset: https://www.kaggle.com/datasets/paresh2047/uci-semcom

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

We talked about:

• Johanna’s background
• Open science course and reproducible papers
• Research software engineering
• Convincing a professor to work on software instead of papers
• The importance of reproducible analysis
• Why academia is behind on software engineering
• The problems with open science publishing in academia
• The importance of standard coding practices
• How Johanna got into research software engineering
• Effective ways of learning software engineering skills
• Providing data and analysis for your project
• Johanna’s initial experience with software engineering in a project
• Working with sensitive data and the nuances of publishing it
• How often Johanna does hackathons, open source, and freelancing
• Social media as a source of repos and Johanna’s favorite communities
• Contributing to Git repos
• Publishing in the open in academia vs industry
• Johanna’s book and resource recommendations
• Conclusion

Links:

• The Society of Research Software Engineering,  plus regional chapters: https://society-rse.org/
• The RSE Association of Australia and New Zealand: https://rse-aunz.github.io/
• Research Software Engineers (RSEs) The people behind research software: https://de-rse.org/en/index.html
• The software sustainability institute: https://www.software.ac.uk/
• The Carpentries (beginner git and programming courses): https://carpentries.org/
• The Turing Way Book of  Reproducible Research: https://the-turing-way.netlify.app/welcome

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:

• Loris’ background
• Transitioning from physics to data
• Aligning people on concepts
• Lead indicators and stickiness
• Context, semantics, and meaning
• Communication and being memorable
• Making data digestible for business and building trust
• The importance of understanding the language of business
• Stakeholder mapping
• Attending business meetings as a data professional
• Organizing your stakeholder map
• Prioritizing
• How to support the business strategy
• Learning to speak online
• Resource recommendations from Loris

Links:

• Discovering Data Discord server: https://bit.ly/discovering-data-discord
• Loris' LinkedIn: https://www.linkedin.com/in/lorismarini/
• Loris' Twitter: https://twitter.com/LorisMarini

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

We talked about:

• Angelica’s background
• Angelica’s books
• Data journalism
• How Angelica got into data journalism
• The field of digital humanities and Angelica’s data journalism course
• Technical articles vs data journalism articles
• Transforming reports into data storytelling
• Are reports to stakeholders considered technical writing?
• Data visualization in articles
• Article length
• The process of writing an article
• Finding writing topics
• How Angelica got into writing a book (communication with publishers)
• The process for writing a book
• Brainstorming
• Reviews and revisions
• Conclusion

Links:

• Data Journalism examples (FENCED OUT): https://www.washingtonpost.com/graphics/world/border-barriers/europe-refugee-crisis-border-control/??noredirect=on
• Data Journalism examples (La tierra esclava): https://latierraesclava.eldiario.es/
• Small medium publication aiming at being Stack Overflow of Medium: https://medium.com/syntaxerrorpub
• Example of a self-published book on Data Visualization: https://www.amazon.com/Introduction-Data-Visualization-Storytelling-Scientist-ebook/dp/B07VYCR3Z6/ref=sr_1_4?crid=4JRJ48O7K8TK&keywords=joses+berengueres&qid=1668270728&sprefix=joses+beremguere%2Caps%2C273&sr=8-4
• My novels (in Italian) La bambina e il Clown: https://www.amazon.it/Bambina-Clown-Angelica-Lo-Duca/dp/1500984515/ref=sr_1_9?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=2KGK9GMN0FAHI&keywords=la+bambina+e+il+clown&qid=1668270769&sprefix=la+bambina+e+il+clown%2Caps%2C88&sr=8-9
• My novels (in Italian) Il Violinista: https://www.amazon.it/Violinista-1-Angelica-Lo-Duca/dp/1501009672/ref=sr_1_1?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=12KTF9EF5UKIG&keywords=il+violinista+lo+duca&qid=1668270791&sprefix=il+violinista+lo+duca%2Caps%2C81&sr=8-1
• Course on Data Journalism: https://www.coursera.org/learn/visualization-for-data-journalism

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:

• Nikola’s background
• Making the first steps towards a transition to BI and Analytics Engineering
• Learning the skills necessary to transition to Analytics Engineering
• The in-between period – from Marketing to Analytics Engineering
• Nikola’s current responsibilities
• Understanding what a Data Model is
• Tools needed to work as an Analytics Engineer
• The Analytics Engineering role over time
• The importance of DBT for Analytics Engineers
• Where can one learn about data modeling theory?
• Going from Ancient Greek and Latin to understanding Data (Just-In-Time Learning)
• The importance of having domain knowledge to analytics engineering
• Suggestion for those wishing to transition into analytics engineering
• The importance of having a mentor when transitioning
• Finding a mentor
• Helpful newsletters and blogs
• Finding Nikola online

Links:

• Nikola's LinkedIn account: https://www.linkedin.com/in/nikola-maksimovic-40188183/

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:

• About Anna and METRO
• Anna’s background
• The importance of a technical background for data product owners
• What are product owners?
• Product owners vs product managers
• Anna’s work on recommender systems at METRO
• Expanding the data team
• Types of algorithms used for recommender systems
• What kind of knowledge and skills data product owners need to have
• Problems and ideas should come from the business
• How Anna handles all her responsibilities
• The process for starting work on new domains
• Product portfolio management
• ProductTank and Anna’s role in it
• Anna’s resource recommendations

Links:

• Data Science for Business Book: https://www.amazon.de/-/en/Foster-Provost/dp/1449361323/ref=sr_1_1?keywords=data+science+for+business&qid=1666404807&qu=eyJxc2MiOiIxLjg3IiwicXNhIjoiMS41MiIsInFzcCI6IjEuNDYifQ%3D%3D&sr=8-1
• Article on Data Science Products: https://www.linkedin.com/pulse/way-create-data-science-products-lessons-learnt-anna-hannemann-phd/

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:

• Audience Poll
• Andrey’s background
• What data science practice is
• Best DS practice in a traditional company vs IT-centric companies
• Getting started with building data science practice (finding out who you report to)
• Who the initiative comes from
• Finding out what kind of problems you will be solving (Centralized approach)
• Moving to a semi-decentralized approach
• Resources to learn about data science practice
• Pivoting from the role of a software engineer to data scientist
• The most impactful realization from data science practice
• Advice for individual growth
• Finding Andrey online

Links: 

• Data Teams book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused/dp/1484262271/

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:

• Sonal’s background
• How the idea for Zingg came about
• What Zingg is
• The difference between entity resolution and identity resolution
• How duplicate detection relates to entity resolution
• How Sonal decided to start working on Zingg
• How Zingg works
• What Zingg runs on
• Switching from consultancy to working on a new open source solution
• Why Zingg is open source
• Open source licensing
• Working on Zingg initially vs now
• Zingg’s current and future team
• Sonal’s biggest current challenge
• Avoiding problems with entity/identity resolution through database design
• Identity resolution vs basic joins, data fusions, and fuzzy joins
• Deterministic matching vs probabilistic machine learning
• Identity and entity resolution applications for fraud detection
• Graph algorithms vs classic ML in entity resolution
• Identity resolution success stories
• What Sonal would do differently given the chance to start over with Zingg
• Advice for those seeking to realize their own solution to a data problem
• Reading suggestion from Sonal
• Conclusion

Links:

• Open-Source Spotlight demo "Zingg":https://www.youtube.com/watch?v=zOabyZxN9b0
• Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs book: https://www.amazon.com/Creative-Selection-Inside-Apples-Process/dp/1250194466

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