MLOps in R: Deploying machine learning models using vetiver

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

MLOps in R: Deploying machine learning models using vetiver

Coursera · Intermediate ·🏭 MLOps & LLMOps ·3mo ago

Key Takeaways

Deploys machine learning models using vetiver in R

Original Description

Did you know that over 70% of machine learning models never make it into production? Are you ready to defy the odds and become a master at deploying machine learning models in R? This Guided Project was created to help data professionals accomplish efficient model deployment using Vetiver in R. More specifically, in this 2-hour long project-based course, you will learn how to build an ensemble model, set up the deployment framework, deploy the model using various methods, and monitor model performance. To achieve this, you will create a fully automated deployment pipeline by working through a realistic scenario of deploying a hospital readmission model in a healthcare setting. This project is unique because it combines hands-on experience with the aim to bridge the gap between machine learning development and production deployment. In order to be successful in this project, you will need a solid understanding of R programming, basic machine learning concepts, and familiarity with building machine learning models using tidymodels.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixing, increasing model reliability and reducing downtime
Medium · Machine Learning
📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixes, increasing model reliability and reducing downtime
Medium · DevOps
📰
qModel Open-Source Platform v1.2.0 Released: Streamlined Python Model Integration & Execution Pipeline
Learn how to streamline Python model integration and execution with qModel Open-Source Platform v1.2.0, a tool for MLOps and AI development
Dev.to AI
📰
Inference Infrastructure Best Practices for High-Traffic AI Applications
Learn best practices for building scalable inference infrastructure for high-traffic AI applications to ensure reliable and efficient deployment
Dev.to AI
Up next
Pole Pruner How A Rope Lever Shears High Branches
Innoforge Studio
Watch →