talk-data.com

This dbt Meetup is an opportunity for the local Stockholm dbt Community to connect and collaborate. If you work with data, this event is for you. We welcome data analysts, scientists, engineers, architects, and more!

➡️ Join the dbt Slack community: https://community.getdbt.com/

🇸🇪 Join the conversation in the #local-sweden channel in dbt Slack to connect with other data practitioners locally.

🤝 Organizer: EQT AB

🏠 Venue: Regeringsgatan 25, 11153, Stockholm

🥣 Refreshments: Light food, salads, and drinks provided

✨ Agenda & Speakers ✨

17.00: Welcome (Hosted by Asia Pongpairote, Thomas Hillerdal and Martin Runeson, EQT)

**** Registration will close at 17.45****

18.00: Extending dbt for large-scale, highly mutable manufacturing data (Filip Vitez - Data Engineer, Guillaume Fetter - Senior Data Engineer, Gordon Ball - Staff Data Engineer, Northvolt)

18:30: Enabling Multiple Teams to Collaborate Concurrently on a 400 TB dbt Project (Mauro Luzzatto - Data Engineer, Johan Blad - Data & ML Engineer, Epidemic Sound)

19:00: Don't repeat yourself: dbt documentation propagation (Fernando Brito - Staff Data Platform Engineer, Voi)

19.30-21.30: Networking, Food & Drink

✍️ Please be prepared to sign an NDA to gain access to the building.

To attend, please read the Required Participation Language for In-Person Events with dbt Labs: https://bit.ly/3QIJXFb

dbt is a data transformation framework that lets analysts and engineers collaborate using their shared knowledge of SQL. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work more efficiently to produce data the entire organization can trust.

Learn more: https://www.getdbt.com/

The past few years have seen significant developments in data science, AI, machine learning, and advanced analytics. But the wider adoption of these technologies has also brought greater cost, risk, regulation, and demands on organizational processes, tasks, and teams. This report explains how ModelOps can provide both technical and operational solutions to these problems. Thomas Hill, Mark Palmer, and Larry Derany summarize important considerations, caveats, choices, and best practices to help you be successful with operationalizing AI/ML and analytics in general. Whether your organization is already working with teams on AI and ML, or just getting started, this report presents ten important dimensions of analytic practice and ModelOps that are not widely discussed, or perhaps even known. In part, this report examines: Why ModelOps is the enterprise "operating system" for AI/ML algorithms How to build your organization's IP secret sauce through repeatable processing steps How to anticipate risks rather than react to damage done How ModelOps can help you deliver the many algorithms and model formats available How to plan for success and monitor for value, not just accuracy Why AI will be soon be regulated and how ModelOps helps ensure compliance