filmov
tv
MLOps 101: Doing Machine Learning the right way
Показать описание
Join us for an informative and practical live session on MLOps 101: Doing Machine Learning the Right Way, featuring Ayush Thakur as our guest speaker.
In this session, we will explore the world of MLOps (Machine Learning Operations) and delve into the best practices and strategies for effectively managing Machine learning projects throughout their lifecycle.
Machine learning has revolutionized the way businesses operate, enabling data-driven decision-making and unlocking new opportunities. However, implementing machine learning models into production can be challenging, requiring a comprehensive approach that goes beyond just model development.
MLOps encompasses the practices, tools, and methodologies that bridge the gap between machine learning development and deployment. It aims to streamline the entire ML lifecycle, from data preparation and model training to deployment, monitoring, and continuous improvement.
During this live session, Ayush Thakur will guide you through the world of MLOps, sharing his deep insights and knowledge on best practices for effectively managing machine learning projects.
In this session, we will explore the world of MLOps (Machine Learning Operations) and delve into the best practices and strategies for effectively managing Machine learning projects throughout their lifecycle.
Machine learning has revolutionized the way businesses operate, enabling data-driven decision-making and unlocking new opportunities. However, implementing machine learning models into production can be challenging, requiring a comprehensive approach that goes beyond just model development.
MLOps encompasses the practices, tools, and methodologies that bridge the gap between machine learning development and deployment. It aims to streamline the entire ML lifecycle, from data preparation and model training to deployment, monitoring, and continuous improvement.
During this live session, Ayush Thakur will guide you through the world of MLOps, sharing his deep insights and knowledge on best practices for effectively managing machine learning projects.