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

Existe ainda muitas discussões sobre a regulamentação de IA no Brasil visando o impacto positivo, para promover a ética e transparência no uso da tecnologia, para impulsionar a inovação e proteger os direitos dos cidadãos.

E para abortar sobre este assunto, neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam uma uma das principais referências no tema, o Diogo Cortiz — Professor da PUC-SP , cientista cognitivo, futurista, palestrante e criador de conteúdo ; que conta neste episódio sobre como anda o cenário atual da para regulamentação de IA no Brasil .

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo !

Falamos no episódio

Conheça nosso convidado:

Diogo Cortiz: https://www.linkedin.com/in/diogocortiz/

Bancada Data Hackers:

Paulo Vasconcellos Gabriel Lages Monique Femme

Links de referências:

Participe do Challenge’23 do Data Hackers: https://www.kaggle.com/datasets/datahackers/state-of-data-2022/discussion/415994 Matéria da Forbes: https://forbes.com.br/forbes-tech/2023/06/por-que-as-empresas-estao-despreparadas-para-os-riscos-da-ia/ Palestino é preso após o Facebook traduzir errado o seu “bom dia” : https://gq.globo.com/Prazeres/Tecnologia/noticia/2017/10/palestino-e-preso-apos-o-facebook-traduzir-errado-o-seu-bom-dia.html Projeto de Lei 38/2023: https://www.camara.leg.br/proposicoesWeb/fichadetramitacao?idProposicao=2345695

In this video you will learn how to use Fivetran to ingest data from Salesforce into your Lakehouse. After the data has been ingested, you will then learn how you can transform your data using dbt. Then we will use Databricks SQL to query, visualize and govern your data. Lastly, we will show you how you can use AI functions in Databricks SQL to call language learning models.

Read more about Databricks SQL https://docs.databricks.com/en/sql/index.html#what-is-databricks-sql

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:

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

ABOUT THE TALK: Forcing data through a rectangle shapes the way we solve problems (for example, dimensional fact tables, OLAP Cubes).

Most Data isn’t rectangular it rather exists in hierarchies (orders, items, products, users). Most query results are better returned as a hierarchy (category, brand, product).

Malloy is a new experimental data programming language that, among other things, breaks the rectangle paradigm and several other long held misconceptions in the way we analyze data.

In this talk, Lloyd Tabb shares the ideas behind the Malloy language, semantic data modeling, and his vision for the future of data.

ABOUT THE SPEAKER: Lloyd Tabb spent the last 30 years revolutionizing how the world uses the internet and, by extension, data. He is one of the internet pioneers, having worked at Netscape during the browser wars as the Principal Engineer on Navigator Gold, the first HTML WYSIWYG editor.

Originally a database & languages architect at Borland, Lloyd founded Looker,, which Google acquired in 2019. Lloyd's work at Looker helped define the Modern Data Stack.

At Google, Lloyd continues to pursue his passion for data, and love of programming languages through his current project, Malloy.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

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