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Applying ML in Trustpilot: Lessons Learned when MLOps Wasn't Even a Thing

Posted Nov 24, 2022 | Views 137
# Trustpilot
# Google Cloud Platform
# Model Deployment
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SPEAKER
Stefano Bosisio
Stefano Bosisio
Stefano Bosisio
ML Engineer @ Trustpilot

Stefano is a Machine Learning Engineer at Trustpilot. Here, Stefano helps data science teams to have a smooth journey from model prototyping to model deployment and retirement. Stefano's background is in Biomedical Engineering (Milan Polytechnic) and a Ph.D. in Computational Chemistry (University of Edinburgh). Stefano is always happy to write and share knowledge through his medium blog, as well as playing piano, baking and crocheting.

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Stefano is a Machine Learning Engineer at Trustpilot. Here, Stefano helps data science teams to have a smooth journey from model prototyping to model deployment and retirement. Stefano's background is in Biomedical Engineering (Milan Polytechnic) and a Ph.D. in Computational Chemistry (University of Edinburgh). Stefano is always happy to write and share knowledge through his medium blog, as well as playing piano, baking and crocheting.

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SUMMARY

Do you want to start tackling Machine Learning Operations (MLOps) and know more about it? Or do you want to find more data engineering and MLOps solutions for your business needs? In this talk we will be dealing with MLOps principles and practices, lessons learned and the architectural solutions implemented in Trustpilot.

In the last 3 years Machine Learning (ML) engineers, data scientists, and data engineers have joint efforts to bring Trustpilot to have a more robust and consistent use of artificial intelligence (AI) under MLOps principles. From identifying and defining MLOps gaps within the data science journey and listening to business needs, this joint collaboration effort resulted in the creation of the MLOps infrastructure within the Google Cloud Platform (GCP).

As a result, the entire ML journey has been drastically improved, easing models prototyping, data retrieval processes and lowering models development and deployment time from months to weeks.

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