MLOps London March - Talks from Toloka and Mesh AI

📽️ Livestream: https://youtu.be/LlzZGcpzqVM
🧑Attend in-person: https://forms.gle/6MDievH6ES8csVvq9

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MLOps London is back again in March with more talks on production machine learning, DevOps and Data Science. The plan is currently 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:
⏱️ 5.30pm onwards
🍺 Arrival, drinks and networking

⏱️ 6.00pm
🎤 Kick off and welcome

⏱️ 6.20pm
🎤 From Model-centric to Data-centric Artificial Intelligence
🧑 Magdalena Konkiewicz - Data Evangelist at toloka.ai

Modern AI products are based on three pillars: algorithms, hardware, and data. The first two have been explored in depth in the last decade, but the last pillar—data—has been neglected and therefore become a bottleneck of Machine Learning. In this talk, Magda will advocate a data-centric approach to AI and show an example of an experiment where the algorithm freezes, allowing only data to be manipulated. She will also discuss crowdsourcing as a scalable and efficient solution to data annotation and how it can be implemented in the ML life cycle, as well as demonstrate real-life cases of this type of project.

⏱️ 7.00pm
🎤 Moving ML into Production is Difficult: Trials and Tribulations from the trenches
🧑 Sean Robertson - Partner, Mesh AI

Having stood static for so long, the gap in intelligent decisions at the application layer represents an astonishing opportunity to leap ahead of competitors, provide stand-out service to customers and adapt rapidly to market conditions.‍
And enterprises have the capacity to make that leap: they are investing heavily in artificial intelligence (AI) and data science capabilities. But they are building them in isolation from the rest of their business and are struggling to take these capabilities to production (and there are good reasons why). In our experience becoming obsessed with production, and therefore value, by deploying performant and stable machine learning is critical.

In this discussion, we will talk through our experiences and discussion points on how to maximise the impact of MLOPS including:
What is production
Well architected
Data pipelines
Inference at scale
Automation
Observability
Breaking down team silos
Accelerating through governance
Measurement

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