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ML Monthly Meetup - Azure Machine Learning Deep Dive

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ML Monthly Meetup - Azure Machine Learning Deep Dive

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Welcome to our in-person ML monthly meetup (February). Join us for deep dive tech talks on AI/ML, food/drink, networking with speakers&peers developers, and win lucky draw prizes.

[Important RSVP instructions]

  • Attendees are required to register at the event website. (Correct name is required for badges and check in. NO walk-ins, NO access without badge)
  • Contact us to submit topics and/or sponsor the meetup on venue/food/swags/prizes. https://forms.gle/JkMt91CZRtoJBSFUA
  • Community on Slack for events chat, speakers office hour and sharing learning, job openings and projects collaboration. join slack

Agenda:

  • 6:00pm~6:30pm: Checkin, Food and Networking
  • 6:30pm~6:40pm: Welcome/Sponsor intro
  • 6:40pm~8:00pm: Tech talks
  • 8:00pm~8:30pm: Lucky Draw & Mingle

Tech Talk 1: MLOps with Apache Airflow
Speaker: Viraj Parekh @Astronomer
Abstract: At the end of the day ML pipelines are just data pipelines for living software. And, as more and more DS/ML teams standardize on Python, Apache Airflow has emerged as the secret ingredient in MLOps. As an open source, Python based workflow manager, Airflow allows data teams to stitch together all the technologies needed in productionizing ML workloads. Out of the box, not only does Airflow have the rich scheduling APIs vast ecosystem of connectors to express even the most complex pipelines, but it is also flexible enough to layer upon additional frameworks. This talk will go through:
- A high level introduction to Airflow
- Various ML architectures used by members of the Airflow community
- A demo of AstroPythonSDK; a new OSS project that makes it easier for data scientists to write production quality pipelines.

Tech Talk 1: Azure Machine Learning Deep Dive
Speaker: Armando Lacerda, MVP @ Microsoft
Abstract: Machine learning models allow scale the decision making process. But they don’t grow on tress or can’t be found on streets. They must be designed, trained, tested and finally put into action. In this session you will learn how this process is implemented in Microsoft’s Azure platform. Armando Lacerda will demo all the way through from creating the workspace to deploying the model in production.

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