We may earn an affiliate commission when you visit our partners.
Google Cloud Training
In this course, we dive into the components and best practices of building high-performing ML systems in production environments.
Read more
In this course, we dive into the components and best practices of building high-performing ML systems in production environments.
Read more
In this course, we dive into the components and best practices of building high-performing ML systems in production environments.
We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs.
This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.
Register for this course and see more details by visiting:
OpenCourser.com/course/lt1e5z/production
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
Introduction to Advanced Machine Learning on Google Cloud
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
Read more
Syllabus
Introduction to Advanced Machine Learning on Google Cloud
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
Read more
Architecting Production ML Systems
This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
Designing Adaptable ML Systems
In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
Designing High-Performance ML Systems
In this module, you identify performance considerations for machine learning models.
Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
Building Hybrid ML Systems
Understand the tools and systems available and when to leverage hybrid machine learning models.
Summary
This module reviews what you learned in this course.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops and refines skills and knowledge that are core for those engaged with ML system building
Provides architects, engineers, and data scientists with the specific knowledge and skills needed to build and refine high-performing production-ready ML systems
Taught by Google Cloud Training, a recognized organization known for their work in ML
Emphasizes the practical aspects of creating an ML system in a production environment
Offers hands-on labs and interactive materials
Relies on the outdated version of TensorFlow 2.4
Save this course
Save Production Machine Learning Systems to your list so you can find it easily later:
Save
Save
Reviews summary
Highly valuable knowledge for production
Learners' reactions to this course are largely positive, describing it as an insightful and valuable learning experience. Many attest to gaining a great deal of practical knowledge about deploying machine learning models in production. The course is well-structured and features excellent content, engaging assignments, and helpful instructors. However, some learners express that the course could be improved by providing more detailed projects, including more thorough explanations of certain concepts, and offering a clearer structure that integrates all topics more effectively.
The instructors and staff are helpful and responsive.
"These are topics that are "hard to teach"."
"Great presenters though, really liked your style folks!"
The course is well-structured and offers excellent, valuable content for learning.
"Overall very insightful course, well structured and well presented."
"The course is really well designed and the content is crystal clear, just Awesome !"
"In general a lot of very valuable knowledge."
Learners gain practical hands-on experience in deploying machine learning models in production.
"Overall this is a very good course. + Working as a less experienced data scientist, I gained a lot of hands-on knowledge when putting a machine learning model into production."
"Really focuses on topics for building production ML Systems"
"Excellent overview of designing real-world ML systems."
The course is heavily focused on Google Cloud Platform (GCP).
"This course gives a good grasp of modern Machine Learning at production and all the problems you'll encounter when launching your models in public. The downside is that you'll see limited ways for handling those problems as this course is extremely tied to Google products. Feels like an advertisement and therefore should have been free to access."
"Also it felt like I was constantly being pitched to buy and use GCP services."
The course could be improved by providing more detailed explanations, clear structure, and robust architecture.
"Overall this is a very good course. + Working as a less experienced data scientist, I gained a lot of hands-on knowledge when putting a machine learning model into production. - It would be better to provide more architectural overviews, or further readings, regarding ML on Google Cloud, just for people with less GCP knowledge to catch up easily."
"Some of the content was really interesting, particularly about the hybrid ML systems, dynamically training models, distributed training and data parallelism, but overall, the information was mostly high level with few exercises or labs to delve into actually designing and implementing this stuff."
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 Production Machine Learning Systems with these
activities:
Organize your notes and materials
Show steps
Stay organized and improve retention by compiling your notes and materials.
Show steps
-
Review your notes and materials from the course.
-
Organize your notes and materials into a logical structure.
-
Create a system for storing and retrieving your notes and materials.
Review Probability and Statistics
Show steps
Improves foundational understanding of probability and statistics concepts, which are essential for understanding machine learning models.
Browse courses on
Probability
Show steps
-
Review probability concepts, such as conditional probability and Bayes' theorem
-
Review statistical concepts, such as mean, median, and standard deviation
-
Practice solving probability and statistics problems
Join a study group or discussion forum
Show steps
Collaborate with peers to enhance understanding and critical thinking.
Show steps
-
Find a study group or discussion forum related to machine learning.
-
Join the group or forum.
-
Participate in discussions and ask questions.
-
Help other members of the group or forum.
19 other activities
Expand to see all activities and additional details
Show all 22 activities
Review basics of supervised machine learning
Show steps
Review key concepts covered in a supervised machine learning course by watching videos, reading articles, or doing practice exercises.
Browse courses on
Supervised Machine Learning
Show steps
-
Review the concept of a training set and test set
-
Refresh supervised ML techniques such as linear regression, logistic regression, and decision trees
Follow TensorFlow Tutorials
Show steps
Provides hands-on experience with TensorFlow, the framework used in the course for building and deploying ML models.
Browse courses on
TensorFlow
Show steps
-
Complete the TensorFlow 'Hello World' tutorial
-
Follow the TensorFlow tutorials on building and evaluating basic machine learning models
-
Experiment with different TensorFlow features and functions
Solve LeetCode problems on TensorFlow
Show steps
Solidify your understanding of TensorFlow concepts by practicing with coding problems.
Browse courses on
TensorFlow
Show steps
-
Explore LeetCode problems tagged with TensorFlow.
-
Choose a problem and read the problem statement carefully.
-
Implement the solution using TensorFlow.
-
Review your solution and optimize for efficiency and correctness.
Review linear algebra and probability
Show steps
Review these foundational math skills to facilitate comprehension in the course.
Browse courses on
Linear Algebra
Show steps
-
Go over your notes and textbooks from linear algebra and probability coursework.
-
Work through practice problems.
-
Take a practice quiz or exam.
Collect best practices for ML systems
Show steps
Enhance your understanding of ML system design by compiling and organizing key best practices.
Browse courses on
Best Practices
Show steps
-
Research and gather information from online sources, articles, and documentation.
-
Organize the information into categories or themes.
-
Create a presentation or document summarizing your findings.
Explore Google Cloud tutorials on ML systems
Show steps
Enhance your knowledge of ML systems by following interactive tutorials provided by Google Cloud.
Show steps
-
Visit the Google Cloud website and search for ML tutorials
-
Select tutorials that align with the concepts covered in this course
-
Work through the tutorials step-by-step and experiment with the code examples
Solve Machine Learning Practice Problems
Show steps
Provides opportunities to practice applying machine learning algorithms and techniques to solve real-world problems.
Browse courses on
Machine Learning
Show steps
-
Find online or textbook-based machine learning practice problems
-
Solve practice problems using the techniques and concepts covered in the course
-
Review and analyze solutions to identify areas for improvement
Join Study Groups or Discussion Forums
Show steps
Encourages collaboration, knowledge sharing, and peer support, which can enhance understanding and retention.
Browse courses on
Machine Learning
Show steps
-
Join online or in-person study groups focused on machine learning
-
Participate in discussion forums and ask questions to clarify concepts
-
Collaborate with peers on small projects or assignments
Attend a TensorFlow workshop
Show steps
Gain hands-on experience with TensorFlow and learn how to build and deploy high-performing ML systems.
Browse courses on
TensorFlow
Show steps
-
Research and register for TensorFlow workshops in your area
-
Attend the workshop and actively participate in hands-on exercises
-
Ask questions and engage with experts to deepen your understanding
Solve practice problems on model evaluation metrics
Show steps
Consolidate skills in model evaluation by working through practice problems covering a range of metrics, such as accuracy, precision, recall.
Browse courses on
Model Evaluation
Show steps
-
Practice calculating metrics for regression models, such as Mean Squared Error (MSE) and R-squared
-
Apply metrics to evaluate the performance of classification models
-
Interpret and compare results to determine the most appropriate model for a specific problem
Develop a sample ML project for production
Show steps
Gain practical experience by building and deploying a small-scale ML system.
Browse courses on
Project Development
Show steps
-
Define the problem statement and project scope.
-
Choose suitable ML algorithms and techniques.
-
Train and evaluate the ML model.
-
Deploy the model and monitor its performance.
Practice building and training ML models
Show steps
Solidify your understanding of ML systems by implementing and training models on real-world datasets.
Browse courses on
Model Training
Show steps
-
Choose a dataset and define your ML problem
-
Build a model using a suitable ML algorithm
-
Train your model and evaluate its performance
-
Iterate and refine your model to improve its accuracy
Follow tutorials on distributed TensorFlow
Show steps
Expand your knowledge of TensorFlow's capabilities by exploring distributed training and deployment.
Browse courses on
Distributed TensorFlow
Show steps
-
Identify tutorials from reliable sources, such as TensorFlow documentation or Coursera.
-
Follow the tutorials step-by-step, experimenting with different configurations.
-
Troubleshoot any issues and optimize your code for performance.
Model deployment practice
Show steps
Practice deploying machine learning models to reinforce the skills learned in the course.
Browse courses on
Model Deployment
Show steps
-
Select a machine learning model to deploy.
-
Prepare the model for deployment.
-
Choose a deployment platform.
-
Deploy the model to the platform.
-
Monitor and evaluate the deployed model.
Follow tutorials on designing and deploying ML systems
Show steps
Gain practical experience in the process of designing and deploying ML systems by following step-by-step tutorials and hands-on exercises.
Browse courses on
Machine Learning
Show steps
-
Walk through a tutorial on selecting an appropriate cloud platform for model deployment
-
Follow a guide on implementing a CI/CD pipeline for ML models
-
Learn best practices for monitoring and maintaining ML systems in production
Build a Simple Machine Learning Model
Show steps
Provides practical experience with the entire machine learning pipeline, from data preparation and model selection to model evaluation and deployment.
Browse courses on
Machine Learning
Show steps
-
Choose a simple machine learning task and dataset
-
Use TensorFlow to build and train a machine learning model
-
Evaluate the model's performance using appropriate metrics
-
Document the project and share the results
Join study groups on ML systems
Show steps
Enhance your learning through collaboration and discussion with peers.
Browse courses on
Learning Communities
Show steps
-
Identify or create study groups with fellow learners.
-
Regularly meet to discuss course materials, share insights, and work through problems together.
Write a blog post about machine learning best practices
Show steps
Reinforce your understanding of machine learning best practices by writing a blog post about them.
Show steps
-
Research machine learning best practices.
-
Write an outline for your blog post.
-
Write the first draft of your blog post.
-
Edit and revise your blog post.
-
Publish your blog post.
Participate in a machine learning competition
Show steps
Test your skills and learn from others by participating in a machine learning competition.
Show steps
-
Find a machine learning competition that interests you.
-
Register for the competition.
-
Build a machine learning model to solve the competition problem.
-
Submit your model to the competition.
-
Analyze the results of the competition.
Organize your notes and materials
Show steps
Stay organized and improve retention by compiling your notes and materials.
Show steps
Show steps
Show steps
- Review your notes and materials from the course.
- Organize your notes and materials into a logical structure.
- Create a system for storing and retrieving your notes and materials.
Review Probability and Statistics
Show steps
Improves foundational understanding of probability and statistics concepts, which are essential for understanding machine learning models.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability concepts, such as conditional probability and Bayes' theorem
- Review statistical concepts, such as mean, median, and standard deviation
- Practice solving probability and statistics problems
Join a study group or discussion forum
Show steps
Collaborate with peers to enhance understanding and critical thinking.
Show steps
Show steps
Show steps
- Find a study group or discussion forum related to machine learning.
- Join the group or forum.
- Participate in discussions and ask questions.
- Help other members of the group or forum.
19 other activities
Expand to see all activities and additional details
Show all 22 activities
Review basics of supervised machine learning
Show steps
Review key concepts covered in a supervised machine learning course by watching videos, reading articles, or doing practice exercises.
Browse courses on
Supervised Machine Learning
Show steps
Show steps
Show steps
- Review the concept of a training set and test set
- Refresh supervised ML techniques such as linear regression, logistic regression, and decision trees
Follow TensorFlow Tutorials
Show steps
Provides hands-on experience with TensorFlow, the framework used in the course for building and deploying ML models.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Complete the TensorFlow 'Hello World' tutorial
- Follow the TensorFlow tutorials on building and evaluating basic machine learning models
- Experiment with different TensorFlow features and functions
Solve LeetCode problems on TensorFlow
Show steps
Solidify your understanding of TensorFlow concepts by practicing with coding problems.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Explore LeetCode problems tagged with TensorFlow.
- Choose a problem and read the problem statement carefully.
- Implement the solution using TensorFlow.
- Review your solution and optimize for efficiency and correctness.
Review linear algebra and probability
Show steps
Review these foundational math skills to facilitate comprehension in the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go over your notes and textbooks from linear algebra and probability coursework.
- Work through practice problems.
- Take a practice quiz or exam.
Collect best practices for ML systems
Show steps
Enhance your understanding of ML system design by compiling and organizing key best practices.
Browse courses on
Best Practices
Show steps
Show steps
Show steps
- Research and gather information from online sources, articles, and documentation.
- Organize the information into categories or themes.
- Create a presentation or document summarizing your findings.
Explore Google Cloud tutorials on ML systems
Show steps
Enhance your knowledge of ML systems by following interactive tutorials provided by Google Cloud.
Show steps
Show steps
Show steps
- Visit the Google Cloud website and search for ML tutorials
- Select tutorials that align with the concepts covered in this course
- Work through the tutorials step-by-step and experiment with the code examples
Solve Machine Learning Practice Problems
Show steps
Provides opportunities to practice applying machine learning algorithms and techniques to solve real-world problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find online or textbook-based machine learning practice problems
- Solve practice problems using the techniques and concepts covered in the course
- Review and analyze solutions to identify areas for improvement
Join Study Groups or Discussion Forums
Show steps
Encourages collaboration, knowledge sharing, and peer support, which can enhance understanding and retention.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join online or in-person study groups focused on machine learning
- Participate in discussion forums and ask questions to clarify concepts
- Collaborate with peers on small projects or assignments
Attend a TensorFlow workshop
Show steps
Gain hands-on experience with TensorFlow and learn how to build and deploy high-performing ML systems.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Research and register for TensorFlow workshops in your area
- Attend the workshop and actively participate in hands-on exercises
- Ask questions and engage with experts to deepen your understanding
Solve practice problems on model evaluation metrics
Show steps
Consolidate skills in model evaluation by working through practice problems covering a range of metrics, such as accuracy, precision, recall.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Practice calculating metrics for regression models, such as Mean Squared Error (MSE) and R-squared
- Apply metrics to evaluate the performance of classification models
- Interpret and compare results to determine the most appropriate model for a specific problem
Develop a sample ML project for production
Show steps
Gain practical experience by building and deploying a small-scale ML system.
Browse courses on
Project Development
Show steps
Show steps
Show steps
- Define the problem statement and project scope.
- Choose suitable ML algorithms and techniques.
- Train and evaluate the ML model.
- Deploy the model and monitor its performance.
Practice building and training ML models
Show steps
Solidify your understanding of ML systems by implementing and training models on real-world datasets.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Choose a dataset and define your ML problem
- Build a model using a suitable ML algorithm
- Train your model and evaluate its performance
- Iterate and refine your model to improve its accuracy
Follow tutorials on distributed TensorFlow
Show steps
Expand your knowledge of TensorFlow's capabilities by exploring distributed training and deployment.
Browse courses on
Distributed TensorFlow
Show steps
Show steps
Show steps
- Identify tutorials from reliable sources, such as TensorFlow documentation or Coursera.
- Follow the tutorials step-by-step, experimenting with different configurations.
- Troubleshoot any issues and optimize your code for performance.
Model deployment practice
Show steps
Practice deploying machine learning models to reinforce the skills learned in the course.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Select a machine learning model to deploy.
- Prepare the model for deployment.
- Choose a deployment platform.
- Deploy the model to the platform.
- Monitor and evaluate the deployed model.
Follow tutorials on designing and deploying ML systems
Show steps
Gain practical experience in the process of designing and deploying ML systems by following step-by-step tutorials and hands-on exercises.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Walk through a tutorial on selecting an appropriate cloud platform for model deployment
- Follow a guide on implementing a CI/CD pipeline for ML models
- Learn best practices for monitoring and maintaining ML systems in production
Build a Simple Machine Learning Model
Show steps
Provides practical experience with the entire machine learning pipeline, from data preparation and model selection to model evaluation and deployment.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a simple machine learning task and dataset
- Use TensorFlow to build and train a machine learning model
- Evaluate the model's performance using appropriate metrics
- Document the project and share the results
Join study groups on ML systems
Show steps
Enhance your learning through collaboration and discussion with peers.
Browse courses on
Learning Communities
Show steps
Show steps
Show steps
- Identify or create study groups with fellow learners.
- Regularly meet to discuss course materials, share insights, and work through problems together.
Write a blog post about machine learning best practices
Show steps
Reinforce your understanding of machine learning best practices by writing a blog post about them.
Show steps
Show steps
Show steps
- Research machine learning best practices.
- Write an outline for your blog post.
- Write the first draft of your blog post.
- Edit and revise your blog post.
- Publish your blog post.
Participate in a machine learning competition
Show steps
Test your skills and learn from others by participating in a machine learning competition.
Show steps
Show steps
Show steps
- Find a machine learning competition that interests you.
- Register for the competition.
- Build a machine learning model to solve the competition problem.
- Submit your model to the competition.
- Analyze the results of the competition.
Career center
Learners who complete Production Machine Learning Systems will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. This course can help you build a strong foundation in the principles of machine learning and provide you with the skills you need to succeed in this role. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to analyze data and extract insights. This course can help you build the skills you need to be a successful Data Scientist. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered software applications. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Product Manager
Product Manager
Product Managers are responsible for defining the vision and roadmap for products. This course can help you build an understanding of the principles of machine learning and how it can be used to improve products. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to use machine learning to analyze data. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Business Analyst
Business Analyst
Business Analysts use data to identify opportunities and solve problems. This course can help you build an understanding of the principles of machine learning and how it can be used to improve business outcomes. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered financial models. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve problems in business and industry. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered solutions to business problems. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered risk management models. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment recommendations. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered financial analysis tools. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Sales Analyst
Sales Analyst
Sales Analysts use data to identify and close sales leads. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered sales tools. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to identify and target customers. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered marketing campaigns. You will learn how to design and architect production ML systems, how to make cost-conscious engineering decisions, and how to debug the causes of observed model behavior.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to write clear and concise documentation for ML-powered products and services.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers get the most value from their products or services. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to develop ML-powered customer success tools.
User Experience Designer
User Experience Designer
User Experience Designers design products and services with a focus on the user experience. This course can help you build a foundation in the principles of machine learning and provide you with the skills you need to design ML-powered products and services that are user-friendly and effective.
For more career information including salaries, visit:
OpenCourser.com/course/lt1e5z/production
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Production Machine Learning Systems.
Presents a collection of design patterns for machine learning systems. These patterns can help architects and developers to design and implement ML systems that are scalable, maintainable, and efficient. This book valuable resource for anyone interested in learning about best practices for designing and implementing ML systems.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers topics such as text classification, sequence modeling, and natural language generation. This book would be a valuable resource for those interested in learning about the latest advances in NLP.
Provides a hands-on introduction to machine learning using Scikit-Learn and TensorFlow. It covers topics such as data preprocessing, model training, and evaluation. This book valuable resource for anyone interested in getting started with machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers topics such as Bayesian inference, graphical models, and reinforcement learning. This book valuable resource for anyone interested in learning about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone interested in learning about the latest advances in deep learning.
Provides a comprehensive overview of machine learning using Python. It covers topics such as data preprocessing, model training, and evaluation. This book valuable resource for anyone interested in using Python for machine learning.
Provides a comprehensive overview of machine learning using R. It covers topics such as data preprocessing, model training, and evaluation. This book valuable resource for anyone interested in using R for machine learning.
Provides a comprehensive overview of machine learning with big data. It covers topics such as data preprocessing, model training, and evaluation. This book valuable resource for anyone interested in learning about the latest advances in big data machine learning.
Save
Provides a comprehensive overview of machine learning for software engineers. It covers topics such as data preprocessing, model training, and evaluation. This book valuable resource for anyone interested in learning about the latest advances in software engineering machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lt1e5z/production
Share
Similar courses
Here are nine courses similar to
Production Machine Learning Systems.
Java: Using Maps (Interactive)
OpenCourser.com/course/98qy0z/java
Java: Using Maps (Interactive)
Most relevant
Introduction to Machine Learning on AWS
OpenCourser.com/course/6a7ltk/introduction
Introduction to Machine Learning on AWS
Most relevant
Rust for Large Language Model Operations (LLMOps)
OpenCourser.com/course/0yqjj5/rust
Rust for Large Language Model Operations (LLMOps)
Most relevant
Introduction to Machine Learning on AWS
OpenCourser.com/course/asrot3/introduction
Introduction to Machine Learning on AWS
Hands-on Machine Learning with AWS and NVIDIA
OpenCourser.com/course/9zfcnr/hands
Hands-on Machine Learning with AWS and NVIDIA
Managing Machine Learning Projects
OpenCourser.com/course/i2mcec/managing
Managing Machine Learning Projects
Nitrogen-related field measurements
OpenCourser.com/course/1qbtb1/nitrogen
Nitrogen-related field measurements
Computer Vision with Embedded Machine Learning
OpenCourser.com/course/wfp4qo/computer
Computer Vision with Embedded Machine Learning
Generative AI with Large Language Models
OpenCourser.com/course/vbz51v/generative
Generative AI with Large Language Models
Effort
5 - 7 hours per week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Develops and refines skills and knowledge that are core for those engaged with ML system building
Provides architects, engineers, and data scientists with the specific knowledge and skills needed to build and refine high-performing production-ready ML systems
Taught by Google Cloud Training, a recognized organization known for their work in ML
Emphasizes the practical aspects of creating an ML system in a production environment
Offers hands-on labs and interactive materials
Relies on the outdated version of TensorFlow 2.4
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser