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Janani Ravi
This course covers the important conceptual reasons why models underperform post-deployment, the actual implementation of model deployment using Python Flask, using serverless, cloud-based compute options and using platform-specific machine learning frameworks.
Machine Learning is exploding in popularity, but serious early warning signs are emerging around the performance of ML models in production.
Read more
This course covers the important conceptual reasons why models underperform post-deployment, the actual implementation of model deployment using Python Flask, using serverless, cloud-based compute options and using platform-specific machine learning frameworks.
Machine Learning is exploding in popularity, but serious early warning signs are emerging around the performance of ML models in production.
Read more
This course covers the important conceptual reasons why models underperform post-deployment, the actual implementation of model deployment using Python Flask, using serverless, cloud-based compute options and using platform-specific machine learning frameworks.
Machine Learning is exploding in popularity, but serious early warning signs are emerging around the performance of ML models in production.
In this course, Deploying Machine Learning Solutions you will gain the ability to identify reasons why models might be under-performing in production after doing just fine in training and testing, and ways to mitigate this worrying phenomenon.
First, you will learn how training-serving skew, concept drift, and overfitting are different causes of model underperformance, and how they can be mitigated by post-deployment monitoring.
Next, you will discover how ML models can be deployed, that is made available on HTTP endpoints, using Flask, the popular Python web-serving framework. You will also see how you can deploy models to serverless environments such as Google Cloud Functions
Finally, you will work with platform-specific machine learning services such as Google AI Platform and Amazon SageMaker for model deployment.
When you’re finished with this course, you will have the skills and knowledge to identify issues with models that have been deployed but are not performing to expectations, as well as how to implement deployment using both on-prem and cloud infrastructure.
Register for this course and see more details by visiting:
OpenCourser.com/course/lvhpfv/deploying
What's inside
Syllabus
Course Overview
Understanding Factors that Impact Deployed Models
Deploying Machine Learning Models to Flask
Deploying Machine Learning Models to Serverless Cloud Environments
Read more
Syllabus
Course Overview
Understanding Factors that Impact Deployed Models
Deploying Machine Learning Models to Flask
Deploying Machine Learning Models to Serverless Cloud Environments
Read more
Deploying Machine Learning Models to Google AI Platform
Deploying Deep Learning Models to AWS SageMaker
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores the causes of model underperformance and techniques to mitigate them, improving the accuracy and reliability of ML models in production
Demonstrates deployment methods for ML models using Python Flask, serverless cloud environments, and platform-specific machine learning frameworks, providing practical implementation techniques
Instructor Janani Ravi is recognized for their expertise in machine learning deployment, ensuring learners receive up-to-date knowledge and industry best practices
Covers essential concepts such as training-serving skew, concept drift, and overfitting, providing learners with a strong foundation in the challenges of ML deployment
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Activities
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before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deploying Machine Learning Solutions with these
activities:
Compile a notebook of course notes, assignments, and quizzes
Show steps
Stay organized and have all your course materials in one place.
Show steps
-
Create a notebook.
-
Add course notes, assignments, and quizzes to the notebook.
Join a study group or find a mentor
Show steps
Collaborate with others and get help when you need it.
Show steps
-
Find a study group or mentor.
-
Meet regularly to discuss the course material.
Organize Course Notes and Resources
Show steps
Enhance your understanding and retention of course materials.
Browse courses on
Machine Learning
Show steps
-
Gather all course materials, including notes, assignments, quizzes, and exams.
-
Organize the materials into a logical structure.
-
Review the materials regularly.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Provides a strong foundation in Python-based machine learning libraries and techniques.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the first two chapters of the book.
-
Go through the code examples in the book.
Attend a machine learning meetup or conference
Show steps
Connect with other people interested in machine learning.
Show steps
-
Find a machine learning meetup or conference in your area.
-
Attend the event and talk to other people.
Deploy a Model to Flask
Show steps
Complete this tutorial to better solidify your understanding of deploying models to Flask.
Show steps
-
Read through the documentation for deploying models to Flask.
-
Follow the step-by-step guide to deploy a model to Flask.
-
Test your deployed model to ensure it is working correctly.
Discuss Model Deployment Best Practices
Show steps
Participate in peer sessions to discuss best practices for deploying machine learning models with Flask or serverless.
Show steps
-
Join a peer session or discussion forum.
-
Engage with other participants to share ideas and knowledge.
Predict Model Performance
Show steps
Practice identifying potential factors that could impact deployed models.
Browse courses on
Model Deployment
Show steps
-
Review the course materials on model underperformance.
-
Identify different factors that can cause model underperformance.
-
For each factor, provide examples of how it could manifest in a deployed model.
Deploy Machine Learning Models to Flask
Show steps
Gain hands-on experience in deploying models to a web-serving framework.
Browse courses on
Model Deployment
Show steps
-
Follow the course materials on deploying models to Flask.
-
Implement a simple machine learning model in Flask.
-
Deploy the model to a web server.
Follow a machine learning tutorial from Coursera or edX
Show steps
Gain hands-on experience and learn from experts in the field.
Browse courses on
Machine Learning
Show steps
-
Choose a tutorial that is relevant to your interests.
-
Complete the tutorial.
-
Apply what you have learned to a project.
Deploy a Model to Serverless Cloud Environments
Show steps
Complete these exercises to solidify your understanding of deploying models to serverless cloud environments.
Show steps
-
Choose a serverless cloud provider.
-
Create a new project or use an existing one.
-
Deploy your model to the serverless cloud environment.
-
Test your deployed model to ensure it is working correctly.
Develop a Serverless Model Deployment Plan
Show steps
Apply your knowledge of serverless computing to design a deployment plan.
Browse courses on
Model Deployment
Show steps
-
Research serverless computing options.
-
Design a deployment plan for a specific machine learning model.
-
Implement the deployment plan using Google Cloud Functions.
Solve machine learning practice problems on LeetCode or HackerRank
Show steps
Strengthen your problem-solving skills and prepare for job interviews.
Browse courses on
Machine Learning
Show steps
-
Choose a set of practice problems.
-
Work through the problems.
-
Review your solutions.
Deploy a Model to Google AI Platform
Show steps
Develop a project to further strengthen your skills in deploying models to Google AI Platform.
Show steps
-
Create a new Google AI Platform project or use an existing one.
-
Deploy your model to Google AI Platform.
-
Test your deployed model to ensure it is working correctly.
-
Monitor your deployed model to ensure it is performing as expected.
Create a Model Deployment Toolkit
Show steps
Gather and organize resources to create a toolkit for deploying machine learning models.
Show steps
-
Compile a list of tools and resources for deploying models.
-
Create a documentation guide or tutorial.
-
Share your toolkit with others to help them deploy their models.
Contribute to Google AI Platform
Show steps
Enhance your understanding of platform-specific machine learning services by contributing to an open-source project.
Browse courses on
Model Deployment
Show steps
-
Review the Google AI Platform documentation.
-
Identify an area where you can contribute.
-
Submit a pull request with your proposed changes.
Write a blog post on a machine learning topic
Show steps
Reinforce your understanding and improve your communication skills.
Browse courses on
Machine Learning
Show steps
-
Choose a topic that you are interested in.
-
Research the topic thoroughly.
-
Write a draft of your blog post.
-
Proofread and edit your blog post.
-
Publish your blog post.
Deploy a Deep Learning Model to AWS SageMaker
Show steps
Create a deliverable to demonstrate your skills in deploying deep learning models to AWS SageMaker.
Show steps
-
Create a new AWS SageMaker project or use an existing one.
-
Deploy your deep learning model to AWS SageMaker.
-
Test your deployed model to ensure it is working correctly.
-
Monitor your deployed model to ensure it is performing as expected.
Build a machine learning model to solve a real-world problem
Show steps
Apply your skills to a practical problem and gain valuable experience.
Browse courses on
Machine Learning
Show steps
-
Identify a problem that you want to solve.
-
Collect data.
-
Build a machine learning model.
-
Evaluate your model.
-
Deploy your model.
Contribute to an open source machine learning project
Show steps
Gain experience with real-world machine learning projects.
Browse courses on
Machine Learning
Show steps
-
Find an open source machine learning project that you are interested in.
-
Contribute to the project.
Compile a notebook of course notes, assignments, and quizzes
Show steps
Stay organized and have all your course materials in one place.
Show steps
Show steps
Show steps
- Create a notebook.
- Add course notes, assignments, and quizzes to the notebook.
Join a study group or find a mentor
Show steps
Collaborate with others and get help when you need it.
Show steps
Show steps
Show steps
- Find a study group or mentor.
- Meet regularly to discuss the course material.
Organize Course Notes and Resources
Show steps
Enhance your understanding and retention of course materials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, quizzes, and exams.
- Organize the materials into a logical structure.
- Review the materials regularly.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Provides a strong foundation in Python-based machine learning libraries and techniques.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the first two chapters of the book.
- Go through the code examples in the book.
Attend a machine learning meetup or conference
Show steps
Connect with other people interested in machine learning.
Show steps
Show steps
Show steps
- Find a machine learning meetup or conference in your area.
- Attend the event and talk to other people.
Deploy a Model to Flask
Show steps
Complete this tutorial to better solidify your understanding of deploying models to Flask.
Show steps
Show steps
Show steps
- Read through the documentation for deploying models to Flask.
- Follow the step-by-step guide to deploy a model to Flask.
- Test your deployed model to ensure it is working correctly.
Discuss Model Deployment Best Practices
Show steps
Participate in peer sessions to discuss best practices for deploying machine learning models with Flask or serverless.
Show steps
Show steps
Show steps
- Join a peer session or discussion forum.
- Engage with other participants to share ideas and knowledge.
Predict Model Performance
Show steps
Practice identifying potential factors that could impact deployed models.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Review the course materials on model underperformance.
- Identify different factors that can cause model underperformance.
- For each factor, provide examples of how it could manifest in a deployed model.
Deploy Machine Learning Models to Flask
Show steps
Gain hands-on experience in deploying models to a web-serving framework.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Follow the course materials on deploying models to Flask.
- Implement a simple machine learning model in Flask.
- Deploy the model to a web server.
Follow a machine learning tutorial from Coursera or edX
Show steps
Gain hands-on experience and learn from experts in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a tutorial that is relevant to your interests.
- Complete the tutorial.
- Apply what you have learned to a project.
Deploy a Model to Serverless Cloud Environments
Show steps
Complete these exercises to solidify your understanding of deploying models to serverless cloud environments.
Show steps
Show steps
Show steps
- Choose a serverless cloud provider.
- Create a new project or use an existing one.
- Deploy your model to the serverless cloud environment.
- Test your deployed model to ensure it is working correctly.
Develop a Serverless Model Deployment Plan
Show steps
Apply your knowledge of serverless computing to design a deployment plan.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Research serverless computing options.
- Design a deployment plan for a specific machine learning model.
- Implement the deployment plan using Google Cloud Functions.
Solve machine learning practice problems on LeetCode or HackerRank
Show steps
Strengthen your problem-solving skills and prepare for job interviews.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a set of practice problems.
- Work through the problems.
- Review your solutions.
Deploy a Model to Google AI Platform
Show steps
Develop a project to further strengthen your skills in deploying models to Google AI Platform.
Show steps
Show steps
Show steps
- Create a new Google AI Platform project or use an existing one.
- Deploy your model to Google AI Platform.
- Test your deployed model to ensure it is working correctly.
- Monitor your deployed model to ensure it is performing as expected.
Create a Model Deployment Toolkit
Show steps
Gather and organize resources to create a toolkit for deploying machine learning models.
Show steps
Show steps
Show steps
- Compile a list of tools and resources for deploying models.
- Create a documentation guide or tutorial.
- Share your toolkit with others to help them deploy their models.
Contribute to Google AI Platform
Show steps
Enhance your understanding of platform-specific machine learning services by contributing to an open-source project.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Review the Google AI Platform documentation.
- Identify an area where you can contribute.
- Submit a pull request with your proposed changes.
Write a blog post on a machine learning topic
Show steps
Reinforce your understanding and improve your communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic that you are interested in.
- Research the topic thoroughly.
- Write a draft of your blog post.
- Proofread and edit your blog post.
- Publish your blog post.
Deploy a Deep Learning Model to AWS SageMaker
Show steps
Create a deliverable to demonstrate your skills in deploying deep learning models to AWS SageMaker.
Show steps
Show steps
Show steps
- Create a new AWS SageMaker project or use an existing one.
- Deploy your deep learning model to AWS SageMaker.
- Test your deployed model to ensure it is working correctly.
- Monitor your deployed model to ensure it is performing as expected.
Build a machine learning model to solve a real-world problem
Show steps
Apply your skills to a practical problem and gain valuable experience.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem that you want to solve.
- Collect data.
- Build a machine learning model.
- Evaluate your model.
- Deploy your model.
Contribute to an open source machine learning project
Show steps
Gain experience with real-world machine learning projects.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open source machine learning project that you are interested in.
- Contribute to the project.
Career center
Learners who complete Deploying Machine Learning Solutions will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying ML models. This course can help you develop the skills needed to deploy and monitor ML models, which is essential for ensuring that models perform well in real-world applications. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Data Scientist
Data Scientist
Data Scientists use ML models to analyze large datasets and solve complex business problems. This course can help you develop the skills needed to deploy and monitor ML models, which is essential for ensuring that models perform well in real-world applications. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course can help you develop the skills needed to deploy and monitor ML models, which is increasingly important for modern software applications. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Data Analyst
Data Analyst
Data Analysts use ML models to analyze data and generate insights. This course can help you develop the skills needed to deploy and monitor ML models, which is essential for ensuring that models perform well in real-world applications. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Cloud Engineer
Cloud Engineer
Cloud Engineers are responsible for designing, deploying, and maintaining cloud-based infrastructure. This course can help you develop the skills needed to deploy and monitor ML models in the cloud, which is becoming increasingly common. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using serverless cloud environments and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. This course can help you develop the skills needed to deploy and monitor ML models, which is becoming increasingly important for DevOps teams. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Product Manager
Product Manager
Product Managers are responsible for defining and managing the development of software products. This course can help you develop the skills needed to deploy and monitor ML models, which is increasingly important for modern software products. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to automate business processes and improve efficiency. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Data Architect
Data Architect
Data Architects are responsible for designing and managing data architectures. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of data architectures. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Security Analyst
Security Analyst
Security Analysts are responsible for protecting organizations from cyber threats. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to detect and prevent cyber threats. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Systems Analyst
Systems Analyst
Systems Analysts are responsible for analyzing and designing computer systems. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of computer systems. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of databases. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Network Administrator
Network Administrator
Network Administrators are responsible for managing and maintaining computer networks. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of computer networks. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Web Developer
Web Developer
Web Developers are responsible for designing and developing websites. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of websites. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
Mobile Developer
Mobile Developer
Mobile Developers are responsible for designing and developing mobile applications. This course can help you develop the skills needed to deploy and monitor ML models, which can be used to improve the performance of mobile applications. You will learn how to identify and mitigate factors that can impact deployed models, such as training-serving skew, concept drift, and overfitting. You will also gain experience deploying ML models using Flask, serverless cloud environments, and platform-specific machine learning services such as Google AI Platform and Amazon SageMaker.
For more career information including salaries, visit:
OpenCourser.com/course/lvhpfv/deploying
Reading list
We've selected 12 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
Deploying Machine Learning Solutions.
Great introduction to machine learning for those who are already familiar with Python. It covers a wide range of topics, including data preprocessing, model selection, and evaluation. It also includes a lot of hands-on exercises that will help you to learn how to apply machine learning to real-world problems.
Great resource for anyone who wants to learn more about the statistical foundations of machine learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees. It also includes a lot of helpful exercises and examples that will help you to understand the concepts of machine learning.
Save
Great resource for anyone who wants to learn more about the Bayesian approach to machine learning. It covers a wide range of topics, including Bayesian inference, Bayesian optimization, and Gaussian processes.
Great resource for anyone who wants to learn more about probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov networks, and conditional random fields.
Great introduction to reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradient methods.
Great introduction to deep reinforcement learning. It covers a wide range of topics, including deep Q-learning, policy gradient methods, and actor-critic methods.
Great introduction to natural language processing. It covers a wide range of topics, including tokenization, stemming, and parsing.
Great introduction to speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and machine translation.
Save
Great introduction to computer vision. It covers a wide range of topics, including image processing, feature extraction, and object recognition.
Great resource for anyone who wants to learn more about the theoretical foundations of machine learning. It covers a wide range of topics, including linear algebra, probability theory, and optimization.
Save
Great resource for anyone who wants to learn more about the algorithmic foundations of machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Great resource for anyone who wants to learn more about the practical applications of machine learning. It covers a wide range of topics, including data preprocessing, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lvhpfv/deploying
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Time
184 hours
Level
Advanced
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Machine Learning Literacy. It's comprised of five courses:
- Preparing Data for Machine Learning
- Designing a Machine Learning Model
- Creating Machine Learning Models
- Deploying Machine Learning Solutions (viewing)
- Building Your First Machine Learning Solution
Good to know
Explores the causes of model underperformance and techniques to mitigate them, improving the accuracy and reliability of ML models in production
Demonstrates deployment methods for ML models using Python Flask, serverless cloud environments, and platform-specific machine learning frameworks, providing practical implementation techniques
Instructor Janani Ravi is recognized for their expertise in machine learning deployment, ensuring learners receive up-to-date knowledge and industry best practices
Covers essential concepts such as training-serving skew, concept drift, and overfitting, providing learners with a strong foundation in the challenges of ML deployment
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