[In-person] ML Monthly Meetup #22 (MLOps, Data Stream Processing)

Please register on event website: https://www.aicamp.ai/event/eventdetails/W2022111005
[Important update]

• We reached the room capacity and close the registration.
• Attendees are required to register at the event website above. (We will have your visitor badge ready when you arrive. NO walk-ins, NO access without badge)
• Attendees are required to be fully vaccinated, bring your Govt. ID and vaccination proof.
• Attendees are requested to park their vehicles only at designated parking area only.
• Attendees should return the temporary visitor pass to the receptionist at the end the event.

Description:
Data streaming platforms provide the tools needed to bring together real-time streams of data. It enables data intelligent, real-time applications and systems, and empowers teams to act on data instantly.

We will host a series of meetup events to showcase some of the cutting edge developments that are happening in data stream processing in the open sourced tools and machine learning community. We will invite the PMCs of Apache Beam, Airflow, Spark, Flink, Kafka, etc.. and teams who use these tools to share practical experiences, best practices in building data stream processing pipelines. The community of real-time data technology leaders and developers will come together to share best practices and use cases as well as explore the vision and future of data streaming.

Agenda:
* 6:00pm~7:00pm: Checkin, Networking and Food/drink
* 7:00pm~8:30pm: Tech talks
* 8:30pm: Closing

Tech Talk 1: MLOps for Batch Processing using Airflow
Abstract: Apache Airflow is an open-source task scheduler and workflow manager. As opposed to end-to-end MLOps solutions, it does one thing, really well — run jobs. Airflow comes out of the box with many task types, including PythonOperator and KubernetesPodOperator and . These specific operators mean you can run a Python function or spin up a container, respectively. In this talk we'll look at how we can leverage Airflow to schedule and run large scale ML batch predictions effectively.
Speaker: Joinal Ahmed, Google