Free online community for data builders. Learn data, ML, MLOps, LLMs, RAG, and AI engineering. Join free hands-on courses, events, workshops, and podcasts.
Joined October 2020
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Want to talk about data?
Data science, career questions, practical machine learning, and more?
Join @DataTalksClub! The link is in the profile description.
Learn more about our activities in the thread! 🧵
DataTalksClub retweeted
Our new free course: Production AI Agents for Generative Media, built with Nebius.
Come learn to build AI agents that generate images, video, and audio on GPUs, and run them in production.
ALT Course details for building AI agents that create media assets on GPUs, including datasets and module format over five weeks.
Our new free course: Production AI Agents for Generative Media, built with Nebius.
Come learn to build AI agents that generate images, video, and audio on GPUs, and run them in production.
ALT Course details for building AI agents that create media assets on GPUs, including datasets and module format over five weeks.
DataTalksClub retweeted
What if you could trigger Airflow Dags from Kafka messages?
On Nov 4, Astronomer's Marc Lamberti shows how to:
🔸 Build event-driven pipelines
🔸 Trigger Dags from Kafka, Amazon SQS, etc.
🔸 Run many watchers without dropping events
👉 astronomer.io/events/webinar…
ALT Astronomer webinar, Nov 4, 11AM ET / 4PM GMT: Build event-driven workflows with Apache Airflow, with Marc Lamberti and Kenten Danas
Together, we'll build a Personalized Media Factory. A record goes in, and an agent:
- Writes the copy
- Generates an image plus voice or video
- Evaluates and regenerates on failure
- Scales to 1,000+ records with a cost per asset
The materials are open to everyone. Homework, the final project, and peer review are for selected participants, and seats are limited.
The course starts on November 17.
Apply here: courses.datatalks.club/regis…
What if you could trigger Airflow Dags from Kafka messages?
On Nov 4, Astronomer's Marc Lamberti shows how to:
🔸 Build event-driven pipelines
🔸 Trigger Dags from Kafka, Amazon SQS, etc.
🔸 Run many watchers without dropping events
👉 astronomer.io/events/webinar…
ALT Astronomer webinar, Nov 4, 11AM ET / 4PM GMT: Build event-driven workflows with Apache Airflow, with Marc Lamberti and Kenten Danas
Build event-driven workflows that scale with Airflow
What if you could trigger Dags from Kafka messages?
Join Airflow expert Marc Lamberti on Nov 4th as he demos Airflow 3 pipelines that trigger in real time and how to run them at scale with your data.
astronomer.io/events/webinar…
AI agents are inflating the numbers you rely on.
🔸 Engagement metrics inflated by machine traffic
🔸 Ad attribution quietly broken
🔸 One industry hit harder than most
Here's what to do about it 👉 insights.snowplow.io/l/10928…
Dewang Sultania suggests a guardrail check: list every tool your agent can call, then ask what happens if each runs incorrectly 100 times. Start with Chapter 1 of Designing AI Systems.
youtube.com/watch?v=T1R-IfYr…
ALT Dewang Sultania Designing AI Systems Chapter 1 guardrails resource card
What helps AI agents answer business questions correctly?
Next Query 2026 by PhoenixAI | Nov 4 | Free virtual summit
➡ Business meaning + fresh context
➡ Permissions + resilient analytics
➡ Production architecture + migration lessons
Register: nextquery.phoenixdata.ai/?ut…
AI agents are inflating the numbers you rely on.
🔸 Engagement metrics inflated by machine traffic
🔸 Ad attribution quietly broken
🔸 One industry hit harder than most
Here's what to do about it
👉 insights.snowplow.io/l/10928…
DataTalks.Club Weekly #299 is out.
This week: AI agent traffic vs real readers, ML Zoomcamp Module 5, Project 1 criteria, two Nov 2 events, and a Tejas Kumar recording on AI evals.
mailchi.mp/datatalks.club/we…
Don't miss our next live podcast episode! We're excited to have Louis-François Bouchard join us to discuss Shipping Reliable LLMs and the Rise of AI Engineering. Tune in to learn more about their experience and insights.
Register here: luma.com/nassme8x
How can people understand, evaluate, and appropriately rely on AI advice for high-stakes decisions?
Daan Di Scala researches how structured knowledge can make LLM systems more trustworthy in agrifood, healthcare, and legal.
ALT Daan Di Scala, an AI researcher, discusses GraphRAG applications in high-stakes decision-making for a webinar.
Daan is an AI researcher at TNO, the Dutch research institute for applied science, and studies AI trustworthiness in high-stakes domains.
Daan's PhD at Utrecht University is on Knowledge-Grounded AI for Trustworthy Advice: how structured knowledge can enhance LLM systems.
In our webinar GraphRAG in Real-Life Use Cases, Daan will:
- Discuss how knowledge-driven RAG and GraphRAG can make retrieval more trustworthy
- Take you through 3 applied use cases and show the effect of different approaches
Register here: luma.com/dtc-r9hr
A churn model with 80% accuracy sounds good.
Here comes the uncomfortable check:
A dummy model that predicts no customer will ever churn has 73% accuracy, because only 27% of the customers actually churned.
The improvement over doing nothing is much smaller than you thought.
ALT The model predictions show an 80% accuracy rate for churn, while a dummy model predicts 73% accuracy with only 27% actual churn.
Here's what can help:
1. Compare against a dummy baseline before reporting accuracy
2. Look at the errors in detail, with a confusion table, precision, and recall
That comparison comes from Module 4 of Machine Learning Zoomcamp. The lesson walks through it step by step, with code: github.com/DataTalksClub/mac…
