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Deploying Machine Learning Solutions
Janani Ravi
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.
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.
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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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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