courseraАнализ данных

Learn MLOps for Machine Learning

Курс от Pearson

Средний≈ 6.7 чАнглийский

О курсе

When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning model's performance. Tracking all of these items can be complicated. With tools such as DVC, MLFlow, AWS, you can meet the challenge. Milecia McGregor demonstrates how to use MLOps tools to improve machine learning and automate some of the steps in the process.

Навыки, которые вы освоите

Model Deployment MLOps (Machine Learning Operations)Data Management Automation Model Evaluation Data Pipelines Data Preprocessing Continuous Monitoring Data Collection Feature Engineering Model Training Data Processing Cloud Deployment

Программа курса

1 модулей · 40 учебных материалов

01Learn MLOps for Machine Learning40 материалов

Introduction

Course IntroductionВидео

Learning the MLOps Pipeline

Learning objectivesВидео Gather the dataВидео Analyze the dataВидео Prepare the dataВидео

Учитесь у экспертов

Pearson

Преподаватель курса

Milecia McGregor

Senior Staff Engineer