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Andrew Ng
and
Cristian Bartolomé Arámburu
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Read more
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Read more
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Selecting and Training a Model
Week 3: Data Definition and Baseline
Register for this course and see more details by visiting:
OpenCourser.com/course/dxkulo/introduction
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What's inside
Syllabus
Week 1: Overview of the ML Lifecycle and Deployment
This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.
Read more
Syllabus
Week 1: Overview of the ML Lifecycle and Deployment
This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.
Read more
Week 2: Select and Train a Model
This week is about model strategies and key challenges in model development. It covers error analysis and strategies to work with different data types. It also addresses how to cope with class imbalance and highly skewed data sets.
Week 3: Data Definition and Baseline
This week is all about working with different data types and ensuring label consistency for classification problems. This leads to establishing a performance baseline for your model and discussing strategies to improve it given your time and resources constraints.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers topics that are standard in industry, such as model training and baseline establishment
Taught by Andrew Ng and Cristian Bartolomé Arámburu, who are recognized for their work in machine learning
Develops skills in model selection, data definition, and baseline establishment, which are core for machine learning engineers
Examines how to tackle concept drift, a highly relevant challenge in machine learning production systems
Provides hands-on practice through prototype development, enhancing the learning experience for students who prefer active learning
Save this course
Save Introduction to Machine Learning in Production to your list so you can find it easily later:
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Reviews summary
Production-level machine learning
learners say this course is a largely positive overview of real-world machine learning engineering. It broadly covers the important qualities of production-level machine learning, like data-centric approaches, error analysis, experiment tracking, and deployment. The course is considered beginner-friendly, but it has value for experienced engineers as well, especially those looking to refresh their knowledge or learn about best practices. The course has practical information that can immediately improve your approach to machine learning, such as how to define project scope and address the challenges of concept drift. Andrew Ng, the instructor, has an engaging teaching style and presents complex ideas in a clear and simple way. He also provides many relevant real-world examples to illustrate his points.
The course stresses the importance of focusing on data quality and understanding before model building.
"I have been in the field of AI powered healthcare imaging industry for some years."
"I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already."
"It covers a lot of the real world problems data scientists find when trying to build machine learning solutions."
The course shares best practices and tips for managing ML projects.
"Excellent course! Andrew Ng is an exceptional human being."
"The content of this course has been especially useful for me."
"Excellent course!! A new way to understand the key factors to master the Machine Learning lifecycle."
"The course content is practical, informative and exciting."
"This is really one of the most important courses I ever took!"
This course emphasizes the practical applications of machine learning in production settings.
"I believe you should never start applying for any ML jobs until you take this!"
"Very valuable course for those who already have some knowledge on machine learning or AI applications."
"Even after having worked several years in the role of an MLE there were some useful ideas here and there that I'm excited about applying in the future."
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 Introduction to Machine Learning in Production with these
activities:
Review Software Development Best Practices
Show steps
Ensure a strong foundation in software development principles, which are essential for building robust and maintainable ML production systems.
Browse courses on
Software Development Best Practices
Show steps
-
Review coding standards and conventions for your preferred programming language.
-
Learn about software design patterns and their application in ML production code.
-
Explore best practices for version control, testing, and continuous integration/continuous delivery (CI/CD) in ML projects.
Review ML Basics
Show steps
Ensure a clear understanding of the foundational concepts of machine learning before diving into the production-focused aspects of the course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review statistical concepts like probability and linear algebra.
-
Go through basic machine learning algorithms like linear regression, logistic regression, and decision trees.
-
Explore fundamental concepts like supervised learning, unsupervised learning, and model evaluation.
Network with ML Production Engineers at Industry Events
Show steps
Expand your professional network, learn about industry trends, and gain insights from experienced ML production engineers.
Show steps
-
Attend industry conferences, meetups, and workshops related to ML production engineering.
-
Introduce yourself to ML production engineers and engage in conversations.
-
Share your experiences and learn from the perspectives of others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an ML Production Engineering Workshop
Show steps
Network with industry professionals, learn about real-world applications of ML production engineering, and gain insights into the latest trends and best practices.
Show steps
-
Research and identify relevant ML production engineering workshops.
-
Register and attend the workshop.
-
Actively participate in discussions, ask questions, and share your experiences.
Error Analysis and Debugging
Show steps
Develop a systematic approach to identifying and resolving errors in machine learning models, improving the overall quality and reliability of your production systems.
Browse courses on
Error Analysis
Show steps
-
Analyze common error messages and identify their root causes.
-
Use debugging tools and techniques to isolate and fix errors in machine learning code.
-
Practice error analysis and debugging on real-world machine learning projects.
Practice Model Deployment with TensorFlow Serving
Show steps
Gain hands-on experience in deploying machine learning models using industry-standard tools, solidifying the concepts covered in the course.
Browse courses on
Model Deployment
Show steps
-
Follow tutorials on setting up TensorFlow Serving.
-
Deploy a simple machine learning model using TensorFlow Serving.
-
Explore advanced deployment techniques like load balancing and autoscaling.
Contribute to Open-Source ML Production Tools
Show steps
Gain practical experience in developing and maintaining production-ready ML tools, while contributing to the wider ML community.
Show steps
-
Identify open-source ML production tools that align with your interests and skills.
-
Review the documentation and codebase of the chosen tools.
-
Contribute bug fixes, feature enhancements, or documentation improvements to the project.
Mentor Junior ML Engineers
Show steps
Share your knowledge and experience with aspiring ML engineers, reinforcing your understanding of the concepts covered in the course and fostering a supportive learning community.
Show steps
-
Identify opportunities to mentor junior ML engineers, such as through online forums or local meetups.
-
Provide guidance on technical concepts, project development, and career growth.
-
Create a supportive and encouraging learning environment for your mentees.
Review Software Development Best Practices
Show steps
Ensure a strong foundation in software development principles, which are essential for building robust and maintainable ML production systems.
Browse courses on
Software Development Best Practices
Show steps
Show steps
Show steps
- Review coding standards and conventions for your preferred programming language.
- Learn about software design patterns and their application in ML production code.
- Explore best practices for version control, testing, and continuous integration/continuous delivery (CI/CD) in ML projects.
Review ML Basics
Show steps
Ensure a clear understanding of the foundational concepts of machine learning before diving into the production-focused aspects of the course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review statistical concepts like probability and linear algebra.
- Go through basic machine learning algorithms like linear regression, logistic regression, and decision trees.
- Explore fundamental concepts like supervised learning, unsupervised learning, and model evaluation.
Network with ML Production Engineers at Industry Events
Show steps
Expand your professional network, learn about industry trends, and gain insights from experienced ML production engineers.
Show steps
Show steps
Show steps
- Attend industry conferences, meetups, and workshops related to ML production engineering.
- Introduce yourself to ML production engineers and engage in conversations.
- Share your experiences and learn from the perspectives of others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an ML Production Engineering Workshop
Show steps
Network with industry professionals, learn about real-world applications of ML production engineering, and gain insights into the latest trends and best practices.
Show steps
Show steps
Show steps
- Research and identify relevant ML production engineering workshops.
- Register and attend the workshop.
- Actively participate in discussions, ask questions, and share your experiences.
Error Analysis and Debugging
Show steps
Develop a systematic approach to identifying and resolving errors in machine learning models, improving the overall quality and reliability of your production systems.
Browse courses on
Error Analysis
Show steps
Show steps
Show steps
- Analyze common error messages and identify their root causes.
- Use debugging tools and techniques to isolate and fix errors in machine learning code.
- Practice error analysis and debugging on real-world machine learning projects.
Practice Model Deployment with TensorFlow Serving
Show steps
Gain hands-on experience in deploying machine learning models using industry-standard tools, solidifying the concepts covered in the course.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Follow tutorials on setting up TensorFlow Serving.
- Deploy a simple machine learning model using TensorFlow Serving.
- Explore advanced deployment techniques like load balancing and autoscaling.
Contribute to Open-Source ML Production Tools
Show steps
Gain practical experience in developing and maintaining production-ready ML tools, while contributing to the wider ML community.
Show steps
Show steps
Show steps
- Identify open-source ML production tools that align with your interests and skills.
- Review the documentation and codebase of the chosen tools.
- Contribute bug fixes, feature enhancements, or documentation improvements to the project.
Mentor Junior ML Engineers
Show steps
Share your knowledge and experience with aspiring ML engineers, reinforcing your understanding of the concepts covered in the course and fostering a supportive learning community.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior ML engineers, such as through online forums or local meetups.
- Provide guidance on technical concepts, project development, and career growth.
- Create a supportive and encouraging learning environment for your mentees.
Career center
Learners who complete Introduction to Machine Learning in Production will develop knowledge and skills
that may be useful to these careers:
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects design and build the architecture for machine learning systems. This course can help build a foundation for understanding the different components of machine learning production systems and how to design and deploy them. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines and infrastructure. This course can help build a foundation for understanding the different components of machine learning production systems and how to design and deploy them. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Cloud Architect
Cloud Architect
Cloud Architects design and build cloud-based architectures. This course can help build a foundation for understanding the different components of machine learning production systems and how to deploy them to the cloud. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Data Science Manager
Data Science Manager
Data Science Managers oversee data science teams and projects. This course can help build a foundation for understanding the different components of machine learning production systems and how to design and deploy them. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. This course can help build a foundation for understanding the different components of machine learning production systems and how to deploy them to production. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Data Scientist
Data Scientist
Data Scientists use data to build machine learning models. This course can help build a foundation for understanding the different components of machine learning production systems and how to deploy them to production. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers have the skills to build, deploy, and maintain machine learning models. This course can help build a foundation for designing and building machine learning systems that can be deployed to production. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to business problems. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Product Manager
Product Manager
Product Managers plan and develop products. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to product development. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use math and statistics to solve financial problems. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to financial problems. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Market Researcher
Market Researcher
Market Researchers study market trends and consumer behavior. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to market research. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use math and statistics to solve operational problems. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to operational problems. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Statistician
Statistician
Statisticians use math and statistics to collect and analyze data. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to data analysis. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Actuary
Actuary
Actuaries use math and statistics to assess risk. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to risk assessment. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. This course can help build a foundation for understanding the different components of machine learning production systems and how to apply them to storytelling. It also provides an overview of the challenges and requirements of machine learning production systems and how to address them.
For more career information including salaries, visit:
OpenCourser.com/course/dxkulo/introduction
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
Introduction to Machine Learning in Production.
Provides a comprehensive overview of statistical learning, from the basics to advanced topics. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a practical introduction to machine learning using TensorFlow 2.0. It covers topics such as data preprocessing, model training, and deployment.
Provides a practical introduction to machine learning using Java. It covers topics such as data preprocessing, model training, and evaluation.
Provides a comprehensive introduction to machine learning using Python. It covers topics such as data preprocessing, model training, and evaluation.
Provides a practical introduction to machine learning using Python. It covers topics such as data preprocessing, model training, and evaluation.
Provides a practical introduction to machine learning using R. It covers topics such as data preprocessing, model training, and evaluation.
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a gentle introduction to machine learning for non-technical readers. It covers topics such as data preprocessing, model training, and evaluation.
Provides a gentle introduction to machine learning using Python. It covers topics such as data preprocessing, model training, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dxkulo/introduction
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Effort
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Level
Advanced
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Cristian Bartolomé Arámburu
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning Engineering for Production (MLOps). It's comprised of four courses:
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production
- Introduction to Machine Learning in Production (viewing)
- Machine Learning Data Lifecycle in Production
Good to know
Covers topics that are standard in industry, such as model training and baseline establishment
Taught by Andrew Ng and Cristian Bartolomé Arámburu, who are recognized for their work in machine learning
Develops skills in model selection, data definition, and baseline establishment, which are core for machine learning engineers
Examines how to tackle concept drift, a highly relevant challenge in machine learning production systems
Provides hands-on practice through prototype development, enhancing the learning experience for students who prefer active learning
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