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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
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…
DataTalksClub retweeted
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
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…
DataTalksClub retweeted
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…
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
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
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.
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.
That comparison comes from Module 4 of Machine Learning Zoomcamp. The lesson walks through it step by step, with code: github.com/DataTalksClub/mac…
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
This is called class imbalance.
When one class is much more common, a model can get high accuracy by always predicting it, while being useless for the minority class.
And the minority class is often the one you care about: the customers who are about to leave.
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.
There's a treat too: a $150 code toward the official Airflow 3 certification exam.
Register for Debugging Airflow’s scariest issues:
astronomer.io/events/webinar…
Debugging Airflow doesn't have to be scary.
On October 29, Astronomer's Airflow experts will show which context to surface so that you and your AI agent can debug even the spookiest pipeline issues.
Airflow pipelines have monsters of their own 👻
🧛 Vampire tasks that drain memory from your workers
👻 Ghost tasks that maybe ran, but left no logs in sight
Sound familiar? You're not alone.
ALT Webinar announcement about debugging Airflow issues, featuring two speakers and playful ghost graphics, scheduled for October 29.
This week, Dewang and Suhas are our Book of the Week authors. Ask them your questions about Designing AI Systems in our Slack: datatalks.club/books/2026101…
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