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Integrating multiple MLOps tools together on Google Cloud Platform
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Speaker Bio:
Mefta Sadat, Senior ML Engineer at Loblaw Digital
Mefta Sadat is a Senior ML Engineer at Loblaw Digital. He has been here for 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.
Abstract:
As one of Canada's largest grocery store chains, our team runs several ML systems on production. We wanted to take one such system and integrate it with several MLOps tools including MLflow, Seldon Core, and Feast on Google Cloud Platform. The goal is to reduce overall development time of a model from EDA to deployment, as well as enable end to end tracking of these ML pipelines.
In this talk, we will share our experience on setting up a recommender system using the tools mentioned. We will also talk about our overall platform architecture and how the MLOps tools fit into the end-to-end ML pipeline.
Mefta Sadat, Senior ML Engineer at Loblaw Digital
Mefta Sadat is a Senior ML Engineer at Loblaw Digital. He has been here for 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.
Abstract:
As one of Canada's largest grocery store chains, our team runs several ML systems on production. We wanted to take one such system and integrate it with several MLOps tools including MLflow, Seldon Core, and Feast on Google Cloud Platform. The goal is to reduce overall development time of a model from EDA to deployment, as well as enable end to end tracking of these ML pipelines.
In this talk, we will share our experience on setting up a recommender system using the tools mentioned. We will also talk about our overall platform architecture and how the MLOps tools fit into the end-to-end ML pipeline.
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