MLOps: Databricks and MLFlow Hyper-parameter Tuning for XGBoost Models
Offered By: The Machine Learning Engineer via YouTube
Course Description
Overview
Explore hyperparameter tuning of an XGBoost model using Databricks and MLflow in this 56-minute video tutorial. Learn how to leverage the powerful combination of Databricks and MLflow for efficient machine learning operations. Dive into a hands-on demonstration of implementing hyperparameter optimization with HyperOpt, enhancing model performance and streamlining the MLOps workflow. Access the accompanying code on GitHub to follow along and apply the techniques to your own projects.
Syllabus
MLOps MLFlow: Databricks and MLFLow Hyper-parameter Tuning #machinelearning
Taught by
The Machine Learning Engineer
Related Courses
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and OptimizationDeepLearning.AI via Coursera Machine Learning in the Enterprise
Google Cloud via Coursera Art and Science of Machine Learning 日本語版
Google Cloud via Coursera Art and Science of Machine Learning auf Deutsch
Google Cloud via Coursera Art and Science of Machine Learning en Español
Google Cloud via Coursera