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Arimoro Olayinka Imisioluwa
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/th0cji/mlops
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Emphasizes real-world application by deploying a hospital readmission model, enhancing relevance to healthcare professionals
Provides hands-on experience in building an ensemble model, facilitating practical learning
Incorporates automation into the deployment pipeline, fostering efficiency and scalability
Assumes learners have prior knowledge of R programming and machine learning concepts, limiting accessibility for beginners
Specifically designed for data professionals, making it highly relevant to their field
Provides a comprehensive approach to model deployment, covering various methods
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Reviews summary
Hands-on vetiver deployment in r
According to students, this MLOps in R course offers a highly practical and concise introduction to deploying machine learning models using the vetiver package. Learners praise its hands-on project-based approach, which effectively bridges the gap between model development and real-world production. The course is seen as a valuable resource for data professionals seeking to implement automated deployment pipelines and monitor model performance. While largely positive, some learners advise having a strong foundation in R and tidymodels as the pace can be fast and the content is packed, making it less suitable for absolute beginners in these areas. A few also noted occasional minor technical setup challenges. Overall, it's considered an excellent, focused project for practical MLOps in R.
A short, project-based course delivering specific skills.
"A concise yet comprehensive guide to deploying ML models in R using `vetiver`."
"Fantastic course! The most valuable part was learning to create a fully automated deployment pipeline. This course delivers it concisely."
"It was the perfect short project to solidify my tidymodels knowledge into a production context."
Excellent guidance on using the powerful vetiver package.
"The `vetiver` package is clearly explained, and the project-based learning is effective."
"Vetiver is a game changer, and the course walks you through its application seamlessly."
"The focus on `vetiver` for MLOps in R is precisely what I needed, a crucial tool for production."
Provides essential hands-on experience for model deployment.
"The hands-on experience with the hospital readmission model was incredibly practical. Highly recommend for anyone looking to implement MLOps with R."
"This course provided the perfect framework for deployment. I walked through its application seamlessly. The monitor model performance section was particularly insightful."
"It perfectly bridges the gap between model development and deployment. The focus on MLOps in R is precisely what I needed."
Some users faced technical difficulties with environment setup.
"I encountered some issues with the environment setup that weren't fully covered. The experience was a bit bumpy due to technical difficulties."
"I would have appreciated a bit more troubleshooting guidance for environment issues, but overall it was still a positive experience."
"The environment setup was frustrating and ate into my learning time; it wasn't as smooth as I hoped."
Requires solid R, ML, and tidymodels knowledge.
"My only minor critique is that some parts felt a bit rushed, so a strong existing knowledge of `tidymodels` and R is definitely a prerequisite, as stated."
"I found this course quite difficult. While the idea is good, the pace was too fast for me. I struggled with some of the R code and felt that the prerequisites were perhaps understated."
"This is a good course, but I recommend having my R and ML fundamentals very strong before starting it."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in MLOps in R: Deploying machine learning models using vetiver with these
activities:
Review Tidymodels Framework for Machine Learning
Show steps
Establish a strong foundation by revisiting the Tidymodels framework used for building machine learning models.
Browse courses on
Tidymodels
Show steps
-
Review the basic concepts of tidymodels packages
-
Practice creating and training models using tidymodels
-
Test and evaluate models with tidymodels evaluation functions
-
Explore advanced features of tidymodels for model tuning and more
Practice Building and Deploying Models with Vetiver Package
Show steps
Reinforce the skills learned about building and deploying machine learning models using Vetiver through repetitive exercises.
Browse courses on
Machine Learning Deployment
Show steps
-
Create a dataset with features and an outcome variable
-
Build multiple models using the Vetiver package
-
Choose the best-performing model based on evaluation metrics
-
Deploy the selected model using Vetiver's deployment functions
-
Test the deployed model and monitor its performance
Create an Ensemble Model for Hospital Readmission Prediction
Show steps
Enhance the understanding of ensemble modeling by creating an ensemble model for predicting hospital readmission.
Show steps
-
Study different ensemble methods and their strengths
-
Select and apply an appropriate ensemble method for the hospital readmission dataset
-
Train and evaluate the ensemble model
-
Compare the ensemble model's performance with individual models
-
Analyze the results and draw conclusions
Show all three activities
Review Tidymodels Framework for Machine Learning
Show steps
Establish a strong foundation by revisiting the Tidymodels framework used for building machine learning models.
Browse courses on
Tidymodels
Show steps
Show steps
Show steps
- Review the basic concepts of tidymodels packages
- Practice creating and training models using tidymodels
- Test and evaluate models with tidymodels evaluation functions
- Explore advanced features of tidymodels for model tuning and more
Practice Building and Deploying Models with Vetiver Package
Show steps
Reinforce the skills learned about building and deploying machine learning models using Vetiver through repetitive exercises.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Create a dataset with features and an outcome variable
- Build multiple models using the Vetiver package
- Choose the best-performing model based on evaluation metrics
- Deploy the selected model using Vetiver's deployment functions
- Test the deployed model and monitor its performance
Create an Ensemble Model for Hospital Readmission Prediction
Show steps
Enhance the understanding of ensemble modeling by creating an ensemble model for predicting hospital readmission.
Show steps
Show steps
Show steps
- Study different ensemble methods and their strengths
- Select and apply an appropriate ensemble method for the hospital readmission dataset
- Train and evaluate the ensemble model
- Compare the ensemble model's performance with individual models
- Analyze the results and draw conclusions
Show all three activities
Career center
Learners who complete MLOps in R: Deploying machine learning models using vetiver will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are experts at transforming raw data into valuable information that enterprises can use to make more informed decisions. This course can help you develop the skills you need to succeed as a Data Scientist by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses improve their operations and make better decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. This course will teach you the skills you need to succeed in this role by providing you with hands-on experience in building and deploying machine learning models using Vetiver in R. You will learn how to create a fully automated deployment pipeline, which is an essential skill for any Machine Learning Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you develop the skills you need to succeed as a Software Engineer by teaching you how to deploy machine learning models using Vetiver in R. This knowledge will be valuable for any Software Engineer who is working on projects that involve machine learning.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course can help you develop the skills you need to succeed as a Data Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses improve their operations and make better decisions.
Business Analyst
Business Analyst
Business Analysts help businesses identify and solve problems by analyzing data and developing solutions. This course can help you develop the skills you need to succeed as a Business Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses improve their operations and make better decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make better decisions. This course can help you develop the skills you need to succeed as a Statistician by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses and organizations improve their operations and make better decisions.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that is used to store and process data. This course can help you develop the skills you need to succeed as a Data Engineer by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement data pipelines that can help businesses improve their operations and make better decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you develop the skills you need to succeed as a Quantitative Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help investment firms make better decisions.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course can help you develop the skills you need to succeed as an Actuary by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help insurance companies and other organizations assess risk and uncertainty.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you develop the skills you need to succeed as an Operations Research Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses improve their operations and make better decisions.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to help businesses understand their customers. This course can help you develop the skills you need to succeed as a Market Researcher by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses understand their customers and make better decisions.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks that could impact businesses and organizations. This course can help you develop the skills you need to succeed as a Risk Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help businesses and organizations identify and assess risks.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment decisions. This course can help you develop the skills you need to succeed as a Financial Analyst by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help investment firms make better decisions.
Health Economist
Health Economist
Health Economists use economic principles to analyze healthcare issues. This course can help you develop the skills you need to succeed as a Health Economist by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help healthcare providers make better decisions.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course can help you develop the skills you need to succeed as a Biostatistician by teaching you how to deploy machine learning models using Vetiver in R. With this knowledge, you will be able to build and implement predictive models that can help researchers make better decisions.
For more career information including salaries, visit:
OpenCourser.com/course/th0cji/mlops
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
MLOps in R: Deploying machine learning models using vetiver.
Provides a practical introduction to machine learning in R. It valuable resource for anyone interested in getting started with machine learning.
Provides a comprehensive overview of interpretable machine learning techniques. It valuable resource for anyone interested in understanding how machine learning models work and making them more interpretable.
Provides a comprehensive overview of deep learning techniques for healthcare applications. It valuable resource for anyone interested in using deep learning to improve healthcare outcomes.
Save
Provides a comprehensive overview of machine learning from a theoretical and practical perspective. It valuable resource for anyone interested in understanding the foundations of machine learning.
Provides a comprehensive overview of statistical learning methods. It valuable resource for anyone interested in understanding the theoretical foundations of machine learning.
Provides a gentle introduction to programming in Python. It valuable resource for anyone interested in learning how to automate tasks in R.
Provides a comprehensive overview of data manipulation techniques in R. It valuable resource for anyone interested in learning how to clean and prepare data for analysis.
Save
Provides a comprehensive overview of data science techniques in R. It valuable resource for anyone interested in learning how to use R for data analysis and machine learning.
Provides a comprehensive overview of machine learning techniques in R. It valuable resource for anyone interested in learning how to use R for data analysis and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/th0cji/mlops
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