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Snehan Kekre
This is a hands-on project on serving your scikit-learn models for deployment with BentoML. By the time you complete this project, you will be able to build logistic regression models for text classification, serve scikit-learn models with BentoML's REST API model server, and containerize model servers with Docker for production deployments.
Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with basic machine learning concepts, and have built predictive models with scikit-learn.
Note: This course works best for learners who are based in the...
Read more
This is a hands-on project on serving your scikit-learn models for deployment with BentoML. By the time you complete this project, you will be able to build logistic regression models for text classification, serve scikit-learn models with BentoML's REST API model server, and containerize model servers with Docker for production deployments.
Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with basic machine learning concepts, and have built predictive models with scikit-learn.
Note: This course works best for learners who are based in the...
Read more
This is a hands-on project on serving your scikit-learn models for deployment with BentoML. By the time you complete this project, you will be able to build logistic regression models for text classification, serve scikit-learn models with BentoML's REST API model server, and containerize model servers with Docker for production deployments.
Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with basic machine learning concepts, and have built predictive models with scikit-learn.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/9vm8bx/serve
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Fits experienced machine learning practitioners well, teaching them how to efficiently deploy their sklearn models for text classification
Fits well with the requirements of deploying machine learning models into production
Provides strong hands-on experience through a project-based approach to serving scikit-learn models with BentoML's REST API model server
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The course was inaccessible due to regional restrictions and the course end date.
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"I couldn't use the workspace."
Course access is limited to North America.
"This course works best for learners who are based in the North America region."
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 Serve Scikit-Learn Models for Deployment with BentoML with these
activities:
Organize and review course materials
Show steps
Establish a strong foundation by organizing and reviewing course materials, ensuring a comprehensive understanding of the concepts covered.
Show steps
-
Organize lecture notes, slides, and assignments into a coherent structure.
-
Review materials regularly to reinforce your understanding.
Refresher on logistic regression for text classification
Show steps
Review the fundamentals of logistic regression for text classification, ensuring a solid understanding of the underlying concepts and techniques.
Browse courses on
Logistic Regression
Show steps
-
Revisit the concepts of logistic regression and text classification.
-
Go through solved examples and practice exercises on binary classification using logistic regression.
Interactive tutorial on BentoML's REST API model server
Show steps
Enhance your understanding of BentoML's REST API model server through hands-on tutorials, solidifying your knowledge of its capabilities and implementation.
Show steps
-
Follow the official BentoML tutorial for setting up and using the REST API model server.
-
Experiment with different deployment configurations and explore the API endpoints.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Contribute to the BentoML open-source community
Show steps
Engage with the BentoML community by contributing to its open-source repositories, enhancing your understanding of the framework and its ecosystem.
Browse courses on
Open Source
Show steps
-
Explore the BentoML GitHub repositories and identify areas where you can contribute.
-
Submit bug reports, feature requests, or code contributions to the relevant repositories.
Hands-on exercises on containerization with Docker
Show steps
Gain practical experience in containerizing model servers using Docker, strengthening your ability to deploy and manage models in a production environment.
Browse courses on
Docker
Show steps
-
Set up a Docker environment and create a Dockerfile for your model server.
-
Build and run Docker images for your model server.
-
Test and troubleshoot your Dockerized model server.
Mentor junior learners in scikit-learn and BentoML
Show steps
Share your knowledge and support other learners by mentoring them in scikit-learn and BentoML, reinforcing your understanding and solidifying your mastery.
Browse courses on
Mentoring
Show steps
-
Join online forums or communities where you can connect with junior learners.
-
Offer guidance and assistance to learners who are struggling with concepts or projects.
Project: Deploy a text classification model with BentoML
Show steps
Apply your skills to build a practical project where you deploy a text classification model using BentoML, demonstrating your mastery of model serving and deployment.
Browse courses on
Model Deployment
Show steps
-
Choose a text classification dataset and train a logistic regression model.
-
Integrate your model with BentoML and deploy it using the REST API model server.
-
Containerize your model server with Docker and deploy it to a cloud platform or on-premises infrastructure.
Organize and review course materials
Show steps
Establish a strong foundation by organizing and reviewing course materials, ensuring a comprehensive understanding of the concepts covered.
Show steps
Show steps
Show steps
- Organize lecture notes, slides, and assignments into a coherent structure.
- Review materials regularly to reinforce your understanding.
Refresher on logistic regression for text classification
Show steps
Review the fundamentals of logistic regression for text classification, ensuring a solid understanding of the underlying concepts and techniques.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Revisit the concepts of logistic regression and text classification.
- Go through solved examples and practice exercises on binary classification using logistic regression.
Interactive tutorial on BentoML's REST API model server
Show steps
Enhance your understanding of BentoML's REST API model server through hands-on tutorials, solidifying your knowledge of its capabilities and implementation.
Show steps
Show steps
Show steps
- Follow the official BentoML tutorial for setting up and using the REST API model server.
- Experiment with different deployment configurations and explore the API endpoints.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Contribute to the BentoML open-source community
Show steps
Engage with the BentoML community by contributing to its open-source repositories, enhancing your understanding of the framework and its ecosystem.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Explore the BentoML GitHub repositories and identify areas where you can contribute.
- Submit bug reports, feature requests, or code contributions to the relevant repositories.
Hands-on exercises on containerization with Docker
Show steps
Gain practical experience in containerizing model servers using Docker, strengthening your ability to deploy and manage models in a production environment.
Browse courses on
Docker
Show steps
Show steps
Show steps
- Set up a Docker environment and create a Dockerfile for your model server.
- Build and run Docker images for your model server.
- Test and troubleshoot your Dockerized model server.
Mentor junior learners in scikit-learn and BentoML
Show steps
Share your knowledge and support other learners by mentoring them in scikit-learn and BentoML, reinforcing your understanding and solidifying your mastery.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Join online forums or communities where you can connect with junior learners.
- Offer guidance and assistance to learners who are struggling with concepts or projects.
Project: Deploy a text classification model with BentoML
Show steps
Apply your skills to build a practical project where you deploy a text classification model using BentoML, demonstrating your mastery of model serving and deployment.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a text classification dataset and train a logistic regression model.
- Integrate your model with BentoML and deploy it using the REST API model server.
- Containerize your model server with Docker and deploy it to a cloud platform or on-premises infrastructure.
Career center
Learners who complete Serve Scikit-Learn Models for Deployment with BentoML will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course on serving scikit-learn models for deployment with BentoML can be helpful for Machine Learning Engineers who want to learn how to deploy their models into production. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help organizations make better decisions. This course on serving scikit-learn models for deployment with BentoML can be helpful for Data Scientists who want to learn how to deploy their models into production. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course on serving scikit-learn models for deployment with BentoML can be helpful for Software Engineers who want to learn how to deploy machine learning models into production. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations make better decisions. This course on serving scikit-learn models for deployment with BentoML may be useful for Data Analysts who want to learn how to deploy their models into production. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing data and recommending solutions. This course on serving scikit-learn models for deployment with BentoML may be useful for Business Analysts who want to learn how to use machine learning to improve their analyses. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. This course on serving scikit-learn models for deployment with BentoML may be useful for Product Managers who want to learn how to use machine learning to improve their products. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course on serving scikit-learn models for deployment with BentoML may be useful for Marketing Managers who want to learn how to use machine learning to improve their campaigns. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Sales Manager
Sales Manager
Sales Managers are responsible for developing and executing sales strategies. This course on serving scikit-learn models for deployment with BentoML may be useful for Sales Managers who want to learn how to use machine learning to improve their sales strategies. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Operations Manager
Operations Manager
Operations Managers are responsible for planning and managing the day-to-day operations of an organization. This course on serving scikit-learn models for deployment with BentoML may be useful for Operations Managers who want to learn how to use machine learning to improve their operations. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to help organizations make better decisions. This course on serving scikit-learn models for deployment with BentoML may be useful for Financial Analysts who want to learn how to use machine learning to improve their analyses. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of an organization. This course on serving scikit-learn models for deployment with BentoML may be useful for Human Resources Managers who want to learn how to use machine learning to improve their HR practices. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Project Manager
Project Manager
Project Managers are responsible for planning and managing projects. This course on serving scikit-learn models for deployment with BentoML may be useful for Project Managers who want to learn how to use machine learning to improve their project management practices. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for helping customers achieve success with a company's products or services. This course on serving scikit-learn models for deployment with BentoML may be useful for Customer Success Managers who want to learn how to use machine learning to improve their customer success strategies. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Account Manager
Account Manager
Account Managers are responsible for managing relationships with customers. This course on serving scikit-learn models for deployment with BentoML may be useful for Account Managers who want to learn how to use machine learning to improve their customer relationships. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
Technical Writer
Technical Writer
Technical Writers create documentation for technical products or services. This course on serving scikit-learn models for deployment with BentoML may be useful for Technical Writers who want to learn how to write documentation for machine learning models. The course covers topics such as building logistic regression models for text classification, serving scikit-learn models with BentoML's REST API model server, and containerizing model servers with Docker for production deployments.
For more career information including salaries, visit:
OpenCourser.com/course/9vm8bx/serve
Reading list
We've selected 11 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
Serve Scikit-Learn Models for Deployment with BentoML.
Practical guide to machine learning with scikit-learn, Keras, and TensorFlow, three of the most popular Python libraries for machine learning. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Save
Comprehensive guide to deep learning, written by three of the leading researchers in the field. It covers all the essential topics, from neural networks to deep learning architectures, and includes many examples and exercises.
Comprehensive guide to machine learning with R, a popular programming language for data science. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Comprehensive guide to machine learning with Python, a popular programming language for data science. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Comprehensive guide to data science, written in a clear and engaging style. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Classic textbook on statistical learning. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Comprehensive guide to statistical learning, written in a clear and concise style. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Save
Comprehensive guide to data mining, a popular technique for extracting knowledge from data. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Practical guide to machine learning with Python, a popular programming language for data science. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Save
Comprehensive guide to R for data science, a popular programming language for data analysis and visualization. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
Comprehensive guide to Python for data analysis, a popular programming language for data science. It covers all the essential topics, from data preprocessing to model evaluation, and includes many examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9vm8bx/serve
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Fits experienced machine learning practitioners well, teaching them how to efficiently deploy their sklearn models for text classification
Fits well with the requirements of deploying machine learning models into production
Provides strong hands-on experience through a project-based approach to serving scikit-learn models with BentoML's REST API model server
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