Orchestrate Everything
ETL was the beginning, but orchestration continues to evolve. Hear first-hand from data engineers at leading companies shipping production AI workflows on Airflow.
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Featuring sessions from
Anyone can demo AI.
The hard part is shipping it.
Join us September 16 for production stories from engineering leaders who've done it, learn hands-on tips for doing the same, and get a first look at where orchestration is going next.
Real Production Stories
Hear from engineering teams running AI in production. No pilots, no prototypes.
Practical Orchestration Patterns
Learn AI orchestration fundamentals in the crash course. Then learn to build the same use cases you just saw from leading data teams, with code from a public repo
See What’s Next
Get a first look at what's new from Astronomer including features built for AI workloads at scale.
AI Orchestration Crash Course
Join Airflow expert Marc Lamberti, creator of Data with Marc, for a preparation crash course for the new Astronomer Certified AI Orchestration Fundamentals exam. Get your questions answered live, plus you'll get a discount code to take the certification for free ($150 value).
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Speakers & Sessions
Orchestrating 100 ML Models in Production: How Ramp Scaled Its ML Platform on Airflow
Ryan Stevens
Director of Applied Sciences, Ramp
Ramp is the finance platform that helps businesses spend less time and money, powered by machine learning behind everything from credit risk assessment to sales lead valuation. In this session, Ryan Stevens will walk through how Airflow orchestrates the full ML lifecycle, from feature creation and ETL to data quality checks and large-batch inference, and how the team evolved it as complexity grew.
850 Engineering Hours Back Every Month: Building a Context-Aware Agentic Airflow Platform at Wix
Yarden Wolf
Data Engineer & AI Tech Lead, Wix
Wix operates more than 8,000 active Airflow Dags maintained by over 100 data engineers. Yarden Wolf explains how the team built a context-aware agentic coding platform that embeds organizational knowledge into AI-assisted development, saving an estimated 850 engineering hours every month.
How Lyft Runs on Airflow: Scaling Orchestration Across Data, Finance, ML, and AI
Julia Lu
Software Engineer, Lyft
At Lyft, Airflow has evolved from a data engineering tool into critical business infrastructure. Julia Lu shares how a small platform team manages more than 7 million tasks each month across 10 production environments supporting ETL, analytics, financial reporting, SOX compliance, and ML feature generation.
Millions of Agents, One Orchestrator: How Aampe (Now Part of MoEngage) Runs AI Decisioning on Airflow
Sami Abboud
CTO & Co-Founder, Aampe (Now Part of MoEngage)
Aampe is an AI decisioning platform that helps leading consumer brands deliver personalized customer engagement at massive scale, using Airflow to orchestrate millions of AI agents processing more than 200 billion decisions each week. Sami Abboud will walk through how the team uses hundreds of Airflow Dags to implement a learning layer and a decisioning layer.
Orchestrating Invoicing at the Fastest-Growing Business in History
Patrick Old
Member of Technical Staff, Mercor
Mercor went from $1M to $2B in run-rate revenue in 24 months, now paying out $4M every day across a global marketplace of experts grading AI models against rubrics. Every graded task becomes a line item that must become an invoice. This talk covers task-based invoicing, Mercor's dominant billing model, and how the data engineering team uses Airflow and dbt with Cosmos on Astronomer to turn inconsistent task data into one canonical, trustworthy source of truth.
A Practical Guide to Orchestrate Everything (with code repository!)
Tamara Fingerlin
Sr. Developer Advocate, Astronomer
Airflow started with ETL/ELT. Today it does everything from continuous compliance checks to feeding AI. Tamara Fingerlin walks through the Dag pattern behind every stage, with code for each example in a public GitHub repo — from basic ETL to putting AI into your Dags with @task.llm and @task.agent.
What's New and What's Next from Astronomer:
Supercharging
Data Teams Starting At Home
Julian LaNeve
CTO at Astronomer
Taylor Merrick
Sr. Manager, Data Engineering, Astronomer
Data teams everywhere are being asked to produce more without more budget or headcount, and Astronomer's own data team hasn't been immune. That pressure pushed us to reimagine the data engineer's role, from hands-on pipeline building to architecting the context and guardrails AI builds within, to rethinking what the platform underneath needs to look like to support it. The result: 6x more output, with the same team. Hear how we did it, and the lessons we've built into Astro to help others get there too.
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Join us September 16.
This event is for data engineers, platform teams, ML practitioners, and data leaders. Free to attend. Sessions available live and on-demand.
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By proceeding you agree to our Privacy Policy, our Website Terms and to receive emails from Astronomer.
FAQ
This event is designed for data engineers, platform teams, ML and MLOps practitioners, analytics engineers, architects, and data leaders who want to do more with data orchestration than traditional ETL and dashboards. Whether you’re scaling Airflow, building AI-powered applications, or driving business-critical data products, you’ll gain practical insights, real-world strategies, and a first look at what’s next with Astronomer.
This event is completely free to attend!
No – the content is valuable for anyone interested in modern data orchestration, AI, and building scalable data products, no Airflow expertise needed.
Yes! Sessions will be recorded and available on demand post event. Several sessions will be hosted live so we recommended joining the day of to get the full experience.
You’ll walk away with actionable strategies to operationalize AI and ML, build production-ready data products, and deliver real business outcomes — plus exclusive insights into the latest capabilities from Astronomer and the future of data orchestration.