What we're about

MLOps London is a community of like-minded engineers, developers and data scientists who focus on the challenges faced while building and deploying production Machine Learning systems at scale. It is an open and diverse place to meet, share experiences and learn from each other.

Our regular meetups are an opportunity to hear from leaders in the field about challenges, tooling and best practices. If you are interested in speaking or want to suggest a great talk you’ve heard before please let us know (no vendor or recruitment pitches please).

Submit a talk:

https://forms.gle/JfvyCDDh1Re23CCN6

Code of Conduct:

https://communitycodeofconduct.com/

Upcoming events (1)

MLOps London November

Needs a location

📽️ Livestream: https://youtu.be/tXZO5x_aVRM
🧑 In Person: https://forms.gle/GWrGXBFwCpKYD6W97
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MLOps London is back again in November with more talks on production machine learning, DevOps and Data Science. The plan, as usual, is to run another hybrid event so please come along in person if you're local. If you're further afield you will still be able to join the stream.

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AGENDA:
⏱️ 6.00pm onwards
🍺 Arrival, drinks and networking

⏱️ 6.30pm
🎤 Kick off and welcome

⏱️ 6.40pm
🎤 AI Assurance: Shift-Left and Shift-Right in Machine Learning Systems’ Testing
🧑 Daniel Geater - VP AI Delivery @ Qualitest

It’s no great secret that AI and ML systems make them a challenge for many teams to test. Over the last few years AI infused applications have increased in prominence across sectors and services, a trend that’s set to continue.

As AI & ML infused technology is used more and more and we push for faster and faster delivery, those testing challenges will become more and more apparent and quality assurance teams will need to rise to meet them to preserve velocity or risk massive increases in production issues and reduced system reliability.

Join Qualitest’s Daniel Geater (VP AI Delivery) to find out about how to establish left-shifted and right-shifted quality assurance to enable confident, rapid releases of ML Models by combining automation, Data Science and Quality Engineering best practices.

⏱️ 7.25pm
🎤 Distributed Training
🧑 Uroš Lipovšek - Machine Learning Engineer @ AWS

We will explore how to train large language models like GPT3 and Megatron efficiently with kubernetes and slurm. Distributed training is also useful for smaller models to increase speed of ML lifecycle, we will showcase this with computer vision model. Talk will be concluded with demo of observability and profiling tools which are used to find bottlenecks in the training process.

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If you are attending in person please complete the registration form (link at top of this description)

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