Online Applied AI & DevOps in partnership with QuantumBlack

We are pleased to announce our first virtual meetup event of 2021! Last year we took our events online and had continued our success with over 100 people in attendance. After a fantastic first virtual event last year, we thought we would once again partner with QuantumBlack

The main theme will be ‘Machine Learning in Production’ so expect some insightful talks from our presenters.

The great thing about taking it virtual is that you can ask questions throughout the session - in between each talk we will allow some time for the speakers to answer some of your questions.

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ZOOM LINK. Join meeting:

https://mckinsey.zoom.us/j/95053253564?pwd=bDZTalpockdMcWYxcWVNWU9XMkdIdz09

Passcode: 385980

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Evening Itinerary:

6:00 pm - 6:05 pm - Demetrious Vassiliou, Director at Chi Square Analytics - https://www.linkedin.com/in/demetriousvassiliou/

Introduction for the evening

6:05 pm - 6:20 pm - Lim Hoang, Junior Principal Software Engineer at QuantumBlack - https://www.linkedin.com/in/limhn/

-Data pipeline meets Feature Store: A practical look with Kedro, Feast & AWS SageMaker Feature Store

Lim will be discussing how feature store is emerging as a key component in an MLOps platform.
He will introduce the benefits of using feature store and how you might integrate a feature store solution into your existing data pipeline. He will also discuss the integration of an open-source feature store (Feast 0.8) and a managed one (AWS SageMaker Feature Store) with a Kedro data pipeline.

6:20 pm - 6:35 pm - Jan Teichmann, Senior Data Science Consultant at Trainline - https://www.linkedin.com/in/janteichmann/

-How to make your data run on time: Learnings from building real-time data products.

In a world defined by constant change who has time to wait for their batch jobs to run every 24h? The true value of the data transformation is when we take the leap into realtime. In this presentation we will look at learnings and design patterns for realtime data products.

Some of questions we look at are:
· What are the components of a realtime data product platform?
· What options exist to integrate data science outputs with a realtime data platform?
· How can we combine the value and advantages of batch systems with the speed of realtime data platforms?

6:35 pm – 6:50pm - Alex Spanos, Lead Data Scientist at TrueLayer - https://www.linkedin.com/in/alexspanos/)

-Lessons from bootstrapping MLOps at TrueLayer

MLOps has become one of the most active areas in the Data Science/Machine Learning tech scene. However, a canonical stack has not quite emerged, and niche new technologies muddy the waters for newcomers trying to introduce MLOps in their organisation. In this talk, Alex will present basic and practical ways to get started.

6:50-7:30 - Breakout session

Please remember to register to receive the webinar link :)

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ZOOM LINK. Join meeting:

https://mckinsey.zoom.us/j/95053253564?pwd=bDZTalpockdMcWYxcWVNWU9XMkdIdz09

Passcode: 385980

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