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Setting up an ML Platform on GCP: Lessons Learned

Posted Dec 27, 2021 | Views 569
# Machine Learning
# Vertex AI
# Google Cloud Platform
# Loblaw
# Loblaw Digital
# Loblawdigital.co
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SPEAKERS
Mefta Sadat
Mefta Sadat
Mefta Sadat
Senior ML Engineer @ Loblaw Digital

Mefta Sadat is a Senior ML Engineer at Loblaw Digital. He has been here for over three years building the Data Engineering and Machine Learning platform. He focuses on productionizing ML services, tools and data pipelines. Previously Mefta worked at a Toronto based Video Streaming Company and designed and built the recommendation system for the Zoneify App from scratch. He received his MSc in Computer Science from Ryerson University focusing on research to mitigate risk in Software Engineering using ML.

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Mefta Sadat is a Senior ML Engineer at Loblaw Digital. He has been here for over three years building the Data Engineering and Machine Learning platform. He focuses on productionizing ML services, tools and data pipelines. Previously Mefta worked at a Toronto based Video Streaming Company and designed and built the recommendation system for the Zoneify App from scratch. He received his MSc in Computer Science from Ryerson University focusing on research to mitigate risk in Software Engineering using ML.

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Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Vishnu Rachakonda
Vishnu Rachakonda
Vishnu Rachakonda
Data Scientist @ Firsthand

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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SUMMARY

Loblaws is one of Canada’s largest grocery store chains, our team at Loblaw Digital runs several ML systems such as search, recommendations, inventory, and labor prediction on production. In this talk, Mefta shares their experience setting up their ML platform on GCP using Vertex AI and open-source tools. The goal of this platform is to help all the data science teams within their organization to take ML projects from EDA to production rapidly while ensuring end-to-end tracking of these ML pipelines. Mefta also talks about their overall platform architecture and how the MLOps tools fit into the end-to-end ML pipeline.

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