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Continuously Improving Large Scale Recommenders with MLOps Tools and Practices

Offered By: MLOps World: Machine Learning in Production via YouTube

Tags

MLOps Courses Machine Learning Courses Deep Learning Courses Recommender Systems Courses Continuous Improvement Courses GPU Acceleration Courses Kubeflow Courses

Course Description

Overview

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Explore the world of large-scale recommendation systems and their continuous improvement through MLOps tools and practices in this 36-minute conference talk from MLOps World: Machine Learning in Production. Learn how to operationalize recommendation systems at scale and deliver ongoing enhancements in production environments. Discover the power of NVIDIA Merlin, an open-source framework for building GPU-accelerated recommender systems, combined with Kubeflow as an orchestrator on Google Kubernetes Engine. Gain insights from deep learning experts Mengdi Huang and Shashank Verma as they share their experiences in developing and implementing cutting-edge recommender systems. Understand the importance of evolving recommendation systems through new data integration and algorithmic improvements to maintain relevance and effectiveness in production settings.

Syllabus

Continuously Improving Large Scale Recommenders with MLOps Tools and Practices


Taught by

MLOps World: Machine Learning in Production

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