MLOps: How to develop and sustain AI at scale?

In this QuantumBlack Sydney in-person, extended to Asia-Pacific virtual meet up, we will discuss the topic of MLOps and show how to implement MLOps in practice.

The session will begin with a discussion MLOps practices to develop and sustain ML systems at scale by George Mathews from QuantumBlack, Sydney. This will be followed by a presentation on machine learning operationalisation challenges and practical strategies to resolve them by Jesse Wu from DataRobot, Sydney.

We will be hosting this hybrid (in-person and virtual) Meetup on Wednesday, 29th June 2022 from 5.30 - 7:30 PM AEST / GMT+10. If you’d like to attend the session in person at the McKinsey Office in Sydney (35/88 Phillip St, Sydney NSW 2000) please note that we have limited seats available and follow government guidance.

In this session, we will focus on how to develop and sustain AI at scale.

Agenda (AEST / GMT+10)
5:30 – Networking, food and drinks
6.00 – Zoom link active; Opening and introductions
6:05-6:30PM – "MLOps practices to develop and sustain ML systems at scale" by George Mathews, QuantumBlack, AI by McKinsey
6:30-6:40PM – Q&A
6:40-7:10PM – "Setting up for success with MLOps" by Jesse Wu, DataRobot
7:10-7:20PM – Q&A
7:20-7:40PM – Closing the event, drinks and networking

MLOps practices to develop and sustain ML systems at scale by George Mathews, QuantumBlack, AI by McKinsey, Sydney

Developing, deploying, and sustaining multiple ML systems across an organisation is challenging. In this session we will talk about these challenges and show how they can be managed through a rigorous set of practices that covers the full ML product lifecycle. In particular, we will share examples of best practice approaches to develop, deploy and sustain ML products and the benefits of standardisation across an organisation.

Setting up for success with MLOps by Jesse Wu, DataRobot, Sydney

Deploying machine learning models introduces unique differences to typical software engineering projects. This talk will give a short overview of the challenges involved with operationalising machine learning models, and the strategies that can be put into place to overcome these difficulties. We'll then deep dive into a few concrete examples of how an organisation might streamline their MLOps process.

Zoom link and password: To be shared within 24 hours of the event, check back for details.
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