MLOps London May - Talks from Hopsworks and The Institute for Ethical AI

📽️ Livestream: https://www.youtube.com/watch?v=MvNChzZH9LM
🧑Attend in-person: https://forms.gle/XvF1cc8ntCzT8zYv5

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MLOps London is back again in May 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:
⏱️ 5.30pm onwards
🍺 Arrival, drinks and networking

⏱️ 6.00pm
🎤 Kick off and welcome

⏱️ 6.20pm
🎤 Fresh Online Feature Stores need high performance Reverse ETL pipelines
🧑 Moritz Meister, Head of Feature Store Engineering @ Hopsworks

Today, the center of gravity for data analytics has shifted towards cloud-native data warehouses like Databricks Lakehouse, Snowflake or Google BigQuery. However, some of the highest value real-time AI use cases require low latency access to features in operational databases. Keeping these features fresh requires reverse ETL (Extract, Transform, Load) at both low latency and high throughput.

In this talk we will describe how we implemented a high throughput, low latency reverse ETL pipeline with Streaming Applications to the Hopsworks Online Feature Store.
We address the challenges of ensuring schema consistency and consistent stable update rates between data warehouses, an intermediate Kafka Cluster and the operational database RonDB.
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Moritz Meister is head of the Feature Store at Hopsworks AB. Moritz has a background in Econometrics and is holding MSc degrees in Computer Science from Politecnico di Milano and Universidad Politecnica de Madrid. He has previously worked as a Data Scientist on projects for Deutsche Telekom and Deutsche Lufthansa in Germany, helping them to productionize machine learning models to improve customer relationship management.

⏱️ 7.00pm
🎤 Secure ML: Automated Security Best Practices in Machine Learning
🧑 Alejandro Saucedo, Director of ML Engineering @ Seldon and Chief Scientist @ The Institute for Ethical AI

As data science capabilities scale, the core concept of security becomes growingly critical. In this talk we will introduce the security challenges that data science practitioners face across the different phases of the machine learning lifecycle, including experimentation, productionisation and monitoring. We will also cover the set of frameworks and best practices that can be used to mitigate these security challenges at each relevant phase of the machine learning lifecycle. We will use a practical example that will allow data science practitioners to adopt these best practices in their daily workflows to ensure a relevant level of security is present in the multiple stages of the machine learning lifecycle.

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