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Laurence Moroney
and
Robert Crowe
In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
Read more
In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
Read more
In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
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: Model Serving Introduction
Week 2: Model Serving Patterns and Infrastructures
Week 3: Model Management and Delivery
Week 4: Model Monitoring and Logging
Register for this course and see more details by visiting:
OpenCourser.com/course/ckn42i/deploying
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What's inside
Syllabus
Week 1: Model Serving: Introduction
Learn how to make your ML model available to end-users and optimize the inference process
Week 2: Model Serving: Patterns and Infrastructure
Read more
Syllabus
Week 1: Model Serving: Introduction
Learn how to make your ML model available to end-users and optimize the inference process
Week 2: Model Serving: Patterns and Infrastructure
Read more
Learn how to serve models and deliver batch and real-time inference results by building scalable and reliable infrastructure
Week 3: Model Management and Delivery
Learn how to implement ML processes, pipelines, and workflow automation that adhere to modern MLOps practices, which will allow you to manage and audit your projects during their entire lifecycle
Week 4: Model Monitoring and Logging
Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Enhances learning with practical tools and hands-on exercises
Provides hands-on practice, aiding learners in developing strong skills in model serving
Useful for learners interested in deploying ML models and making them accessible to users
Save this course
Save Deploying Machine Learning Models in Production to your list so you can find it easily later:
Save
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Reviews summary
Well-received overview of mlops
Learners say that this course is a largely positive overview of the main tools and techniques used to deploy machine learning models in the real world. It is considered a great starting point for those looking to learn more about MLOps. Students particularly enjoyed the real-life projects and the practical nature of the course. However, some learners noted that the course is heavy on Google Cloud Platform (GCP) and its tools, with less emphasis on other cloud platforms.
The course covers a wide range of tools and techniques used in MLOps.
"Broad overview of the many tools and techniques for real world ML ops"
"pretty helpful broad overview of some of the tools and techniques used in deployment of ML models."
Hands-on labs provide learners with practical experience in deploying machine learning models.
"Relatable and hands-on."
"Excellent instructions. Materials are a little bit overlapped. The labs are really useful."
Engaging assignments include real-life projects that learners can apply directly to business scenarios.
"The part I enjoyed most about this course is its real-life projects which one can apply directly in business scenarios"
"This course is what I think is missing in the market. A machine learning course with much emphasis on the practical aspects of running a machine learning platforms."
Learners reported encountering bugs and issues with the graded labs.
"It would be better if the Google Labs were not graded. The labs keep crashing and are very slow to run."
"Two google cloud labs were broken."
"Exercise (both graded and graded) are buggy and wasted a lot of time on non-essential details."
Some learners found the lectures to be unengaging and superficial.
"Robert's lectures are terribly boring and there was no work to make his slides useful, they are just the words he is going to say."
"There is a disconnect between the complexity of the labs (what is actually done there, not the copy-paste that one has to do technically to pass) and the superficiality of the videos."
Some learners reported that the course content is outdated.
"Outdated, google's oriented tools. Doesn't involve open-source guidance."
"The syllabus is somewhat random at times and sometimes information is outdated."
The course has a strong focus on Google Cloud Platform (GCP) tools and services.
"it's a pretty good overview, only downside is the focus on GCP"
"Great specialization but to much information about google and its platform, would be nice to lear about diffetent platforms"
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 Deploying Machine Learning Models in Production with these
activities:
Review basics of Serving Models
Show steps
Refresh your understanding of the fundamentals of serving models to prepare for this course.
Browse courses on
Model Serving
Show steps
-
Review the concepts of model serving, inference, and deployment.
-
Identify the different types of model serving patterns and their pros and cons.
-
Understand the key components of a model serving infrastructure.
Connect with experts in ML deployment
Show steps
Seeking guidance and advice from experienced professionals will provide valuable insights and accelerate your learning journey.
Show steps
-
Identify individuals in your network or online communities who have expertise in ML deployment.
-
Reach out to them and request a brief meeting or mentorship.
-
Prepare questions to ask about their experiences, best practices, and industry trends.
Form study groups with other learners
Show steps
Engaging with peers in study groups will provide a supportive learning environment, foster collaboration, and enhance your understanding of the course material.
Show steps
-
Identify other learners enrolled in this course or in your network who are interested in forming a study group.
-
Establish regular meeting times and a communication platform.
-
Take turns leading discussions, presenting concepts, and working through practice problems together.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Gather resources on MLOps best practices for model serving
Show steps
Expand your knowledge base by compiling resources on MLOps best practices for model serving.
Browse courses on
MLOps
Show steps
-
Search for articles, blog posts, and documentation on MLOps best practices for model serving.
-
Organize and categorize the resources you find.
-
Share your compilation with your peers or contribute it to an online community.
Review regression models
Show steps
Revisiting regression models will give you a strong foundation for understanding the advanced topics covered in this course.
Browse courses on
Regression Models
Show steps
-
Re-read your notes on regression models.
-
Review online tutorials or articles on regression models.
-
Work through practice problems related to regression models.
Participate in a peer study group on model serving best practices
Show steps
Engage with peers to exchange knowledge and insights on model serving best practices.
Show steps
-
Join or create a study group with other students taking this course.
-
Discuss topics related to model serving, such as deployment strategies, performance optimization, and monitoring techniques.
-
Share ideas, resources, and experiences with your peers.
Revisit Machine Learning Basics
Show steps
Review fundamental machine learning concepts to strengthen your understanding of the material covered in this course.
Browse courses on
Machine Learning
Show steps
-
Review supervised learning algorithms such as linear regression, logistic regression, and decision trees.
-
Familiarize yourself with unsupervised learning techniques like k-means clustering and PCA.
-
Practice applying these algorithms to real-world datasets using a programming language of your choice.
Follow a tutorial on deploying a model using a cloud platform
Show steps
Gain practical experience by deploying a model using a cloud platform.
Browse courses on
Model Deployment
Show steps
-
Choose a cloud platform and follow a tutorial on deploying a simple model.
-
Experiment with different deployment options and configurations.
-
Monitor the performance of your deployed model.
Attend ML deployment workshops or conferences
Show steps
Participating in workshops or conferences will provide opportunities for hands-on learning, networking, and exposure to the latest trends in ML deployment.
Show steps
-
Research upcoming ML deployment workshops or conferences in your area.
-
Register for workshops that align with your learning goals.
-
Actively participate in the workshops, ask questions, and connect with other attendees.
Explore Model Deployment Frameworks
Show steps
Gain practical experience by following tutorials on deploying ML models using industry-standard frameworks, enhancing your understanding of model serving patterns and infrastructure.
Browse courses on
Model Deployment
Show steps
-
Set up a local environment for model deployment using TensorFlow Serving or KServe.
-
Walk through tutorials on serving models in both real-time and batch modes.
-
Experiment with different deployment configurations and optimize for performance.
Explore real-world ML deployment examples
Show steps
Examining how ML models are deployed in real-world scenarios will provide practical insights and help you apply the concepts learned in this course.
Show steps
-
Identify industries or domains that heavily utilize ML models.
-
Search for case studies or articles showcasing ML deployment in those areas.
-
Analyze the deployment strategies, challenges, and outcomes of these real-world examples.
Solve problems related to model monitoring and logging
Show steps
Strengthen your understanding of model monitoring and logging through practice.
Browse courses on
Model Monitoring
Show steps
-
Identify the key metrics for monitoring the performance and health of your deployed model.
-
Set up logging mechanisms to capture relevant data for analysis.
-
Analyze the collected data to identify potential issues and performance bottlenecks.
Attend a Machine Learning Engineering Workshop
Show steps
Expand your knowledge and network by attending a workshop focused on machine learning engineering, gaining insights from experts and learning best practices.
Browse courses on
MLOps
Show steps
-
Identify and register for a workshop that aligns with your interests and learning goals.
-
Actively participate in the workshop, engage with speakers, and ask questions.
-
Connect with other attendees and industry professionals to expand your network.
Build a scalable model serving architecture
Show steps
Apply your knowledge to design and implement a scalable model serving architecture.
Browse courses on
Architecture Design
Show steps
-
Identify the performance and scalability requirements for your model serving system.
-
Design an architecture that meets these requirements, considering load balancing, fault tolerance, and high availability.
-
Implement the architecture using appropriate tools and technologies.
-
Test and evaluate the performance and scalability of your system.
Practice model deployment challenges
Show steps
Engaging in hands-on practice will reinforce your understanding of model deployment and help you develop proficiency in addressing common challenges.
Show steps
-
Find online coding challenges or exercises related to model deployment.
-
Work through these challenges, focusing on addressing real-world deployment scenarios.
-
Experiment with different deployment techniques and compare their performance.
Build a Simple Model Serving Application
Show steps
Deepen your understanding of model deployment by building a basic application that serves ML models over an API, providing hands-on experience in implementing real-world scenarios.
Browse courses on
Model Serving
Show steps
-
Create a simple Flask or FastAPI application that exposes an endpoint for model inference.
-
Integrate your deployed model with the application and handle inference requests.
-
Test and evaluate the application's performance in serving predictions.
Contribute to open-source ML deployment projects
Show steps
Involving yourself in open-source projects will provide practical experience, expose you to industry-standard practices, and contribute to the ML community.
Show steps
-
Identify open-source ML deployment projects that align with your interests.
-
Review the project documentation and familiarize yourself with its goals.
-
Start contributing by fixing bugs, adding features, or improving documentation.
Write a Blog Post on Model Monitoring Techniques
Show steps
Solidify your understanding of model monitoring techniques by writing a blog post that explains various approaches, best practices, and tools for tracking and maintaining model performance.
Browse courses on
Model Monitoring
Show steps
-
Research different model monitoring techniques, such as drift detection, performance metrics, and data quality checks.
-
Identify and discuss specific tools and frameworks commonly used for model monitoring.
-
Share practical tips and best practices for implementing effective model monitoring.
Build a personal ML deployment project
Show steps
Undertaking a personal project will allow you to apply the concepts learned in this course and showcase your skills in a practical setting.
Show steps
-
Identify a real-world problem that can be solved using ML.
-
Develop an ML model and deploy it using the techniques covered in this course.
-
Create documentation and a presentation to showcase your project.
Contribute to an Open Source MLOps Project
Show steps
Gain practical experience and contribute to the MLOps community by participating in an open-source project that focuses on model management and deployment.
Browse courses on
Open Source
Show steps
-
Identify an open-source project that aligns with your skills and interests.
-
Review the project's documentation and codebase to understand its goals and architecture.
-
Propose and implement improvements or contribute to existing features.
Review basics of Serving Models
Show steps
Refresh your understanding of the fundamentals of serving models to prepare for this course.
Browse courses on
Model Serving
Show steps
Show steps
Show steps
- Review the concepts of model serving, inference, and deployment.
- Identify the different types of model serving patterns and their pros and cons.
- Understand the key components of a model serving infrastructure.
Connect with experts in ML deployment
Show steps
Seeking guidance and advice from experienced professionals will provide valuable insights and accelerate your learning journey.
Show steps
Show steps
Show steps
- Identify individuals in your network or online communities who have expertise in ML deployment.
- Reach out to them and request a brief meeting or mentorship.
- Prepare questions to ask about their experiences, best practices, and industry trends.
Form study groups with other learners
Show steps
Engaging with peers in study groups will provide a supportive learning environment, foster collaboration, and enhance your understanding of the course material.
Show steps
Show steps
Show steps
- Identify other learners enrolled in this course or in your network who are interested in forming a study group.
- Establish regular meeting times and a communication platform.
- Take turns leading discussions, presenting concepts, and working through practice problems together.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Gather resources on MLOps best practices for model serving
Show steps
Expand your knowledge base by compiling resources on MLOps best practices for model serving.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Search for articles, blog posts, and documentation on MLOps best practices for model serving.
- Organize and categorize the resources you find.
- Share your compilation with your peers or contribute it to an online community.
Review regression models
Show steps
Revisiting regression models will give you a strong foundation for understanding the advanced topics covered in this course.
Browse courses on
Regression Models
Show steps
Show steps
Show steps
- Re-read your notes on regression models.
- Review online tutorials or articles on regression models.
- Work through practice problems related to regression models.
Participate in a peer study group on model serving best practices
Show steps
Engage with peers to exchange knowledge and insights on model serving best practices.
Show steps
Show steps
Show steps
- Join or create a study group with other students taking this course.
- Discuss topics related to model serving, such as deployment strategies, performance optimization, and monitoring techniques.
- Share ideas, resources, and experiences with your peers.
Revisit Machine Learning Basics
Show steps
Review fundamental machine learning concepts to strengthen your understanding of the material covered in this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review supervised learning algorithms such as linear regression, logistic regression, and decision trees.
- Familiarize yourself with unsupervised learning techniques like k-means clustering and PCA.
- Practice applying these algorithms to real-world datasets using a programming language of your choice.
Follow a tutorial on deploying a model using a cloud platform
Show steps
Gain practical experience by deploying a model using a cloud platform.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a cloud platform and follow a tutorial on deploying a simple model.
- Experiment with different deployment options and configurations.
- Monitor the performance of your deployed model.
Attend ML deployment workshops or conferences
Show steps
Participating in workshops or conferences will provide opportunities for hands-on learning, networking, and exposure to the latest trends in ML deployment.
Show steps
Show steps
Show steps
- Research upcoming ML deployment workshops or conferences in your area.
- Register for workshops that align with your learning goals.
- Actively participate in the workshops, ask questions, and connect with other attendees.
Explore Model Deployment Frameworks
Show steps
Gain practical experience by following tutorials on deploying ML models using industry-standard frameworks, enhancing your understanding of model serving patterns and infrastructure.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Set up a local environment for model deployment using TensorFlow Serving or KServe.
- Walk through tutorials on serving models in both real-time and batch modes.
- Experiment with different deployment configurations and optimize for performance.
Explore real-world ML deployment examples
Show steps
Examining how ML models are deployed in real-world scenarios will provide practical insights and help you apply the concepts learned in this course.
Show steps
Show steps
Show steps
- Identify industries or domains that heavily utilize ML models.
- Search for case studies or articles showcasing ML deployment in those areas.
- Analyze the deployment strategies, challenges, and outcomes of these real-world examples.
Solve problems related to model monitoring and logging
Show steps
Strengthen your understanding of model monitoring and logging through practice.
Browse courses on
Model Monitoring
Show steps
Show steps
Show steps
- Identify the key metrics for monitoring the performance and health of your deployed model.
- Set up logging mechanisms to capture relevant data for analysis.
- Analyze the collected data to identify potential issues and performance bottlenecks.
Attend a Machine Learning Engineering Workshop
Show steps
Expand your knowledge and network by attending a workshop focused on machine learning engineering, gaining insights from experts and learning best practices.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Identify and register for a workshop that aligns with your interests and learning goals.
- Actively participate in the workshop, engage with speakers, and ask questions.
- Connect with other attendees and industry professionals to expand your network.
Build a scalable model serving architecture
Show steps
Apply your knowledge to design and implement a scalable model serving architecture.
Browse courses on
Architecture Design
Show steps
Show steps
Show steps
- Identify the performance and scalability requirements for your model serving system.
- Design an architecture that meets these requirements, considering load balancing, fault tolerance, and high availability.
- Implement the architecture using appropriate tools and technologies.
- Test and evaluate the performance and scalability of your system.
Practice model deployment challenges
Show steps
Engaging in hands-on practice will reinforce your understanding of model deployment and help you develop proficiency in addressing common challenges.
Show steps
Show steps
Show steps
- Find online coding challenges or exercises related to model deployment.
- Work through these challenges, focusing on addressing real-world deployment scenarios.
- Experiment with different deployment techniques and compare their performance.
Build a Simple Model Serving Application
Show steps
Deepen your understanding of model deployment by building a basic application that serves ML models over an API, providing hands-on experience in implementing real-world scenarios.
Browse courses on
Model Serving
Show steps
Show steps
Show steps
- Create a simple Flask or FastAPI application that exposes an endpoint for model inference.
- Integrate your deployed model with the application and handle inference requests.
- Test and evaluate the application's performance in serving predictions.
Contribute to open-source ML deployment projects
Show steps
Involving yourself in open-source projects will provide practical experience, expose you to industry-standard practices, and contribute to the ML community.
Show steps
Show steps
Show steps
- Identify open-source ML deployment projects that align with your interests.
- Review the project documentation and familiarize yourself with its goals.
- Start contributing by fixing bugs, adding features, or improving documentation.
Write a Blog Post on Model Monitoring Techniques
Show steps
Solidify your understanding of model monitoring techniques by writing a blog post that explains various approaches, best practices, and tools for tracking and maintaining model performance.
Browse courses on
Model Monitoring
Show steps
Show steps
Show steps
- Research different model monitoring techniques, such as drift detection, performance metrics, and data quality checks.
- Identify and discuss specific tools and frameworks commonly used for model monitoring.
- Share practical tips and best practices for implementing effective model monitoring.
Build a personal ML deployment project
Show steps
Undertaking a personal project will allow you to apply the concepts learned in this course and showcase your skills in a practical setting.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using ML.
- Develop an ML model and deploy it using the techniques covered in this course.
- Create documentation and a presentation to showcase your project.
Contribute to an Open Source MLOps Project
Show steps
Gain practical experience and contribute to the MLOps community by participating in an open-source project that focuses on model management and deployment.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source project that aligns with your skills and interests.
- Review the project's documentation and codebase to understand its goals and architecture.
- Propose and implement improvements or contribute to existing features.
Career center
Learners who complete Deploying Machine Learning Models in Production will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning and other data analysis techniques to extract insights from data. They work with data from a variety of sources, including structured data, unstructured data, and big data. This course can help you build a strong foundation in machine learning and can help you develop the skills you need to succeed in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are specialized software engineers who have deep expertise in machine learning and data science. They design, build, and maintain machine learning models that are used to make predictions or decisions. This course can help you build a strong foundation in machine learning engineering and can help you develop the skills you need to succeed in this role.
Data Analyst
Data Analyst
Data Analysts use data analysis techniques to extract insights from data. They work with data from a variety of sources, including structured data, unstructured data, and big data. This course can help you build a strong foundation in data analysis and can help you develop the skills you need to succeed in this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course can help you build a strong foundation in software engineering and can help you develop the skills you need to succeed in this role.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers, to ensure that products meet the needs of users. This course can help you build a strong foundation in product management and can help you develop the skills you need to succeed in this role.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to understand the needs of businesses and to develop solutions to business problems. This course can help you build a strong foundation in data analysis and can help you develop the skills you need to succeed in this role.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They work with a variety of stakeholders, including team members, clients, and stakeholders, to ensure that projects are completed on time and within budget. This course can help you build a strong foundation in project management and can help you develop the skills you need to succeed in this role.
Technical Writer
Technical Writer
Technical Writers create documentation for software, hardware, and other products. They work with engineers and other technical experts to ensure that documentation is accurate and easy to understand. This course can help you build a strong foundation in technical writing and can help you develop the skills you need to succeed in this role.
Mobile Developer
Mobile Developer
Mobile Developers design and develop mobile applications. They work with a variety of programming languages and technologies to create mobile applications that are user-friendly and visually appealing. This course can help you build a strong foundation in mobile development and can help you develop the skills you need to succeed in this role.
Database Administrator
Database Administrator
Database Administrators are responsible for the management and maintenance of databases. They work with a variety of database technologies and tools to ensure that databases are reliable and accessible. This course can help you build a strong foundation in database administration and can help you develop the skills you need to succeed in this role.
Web Developer
Web Developer
Web Developers design and develop websites. They work with a variety of programming languages and technologies to create websites that are user-friendly and visually appealing. This course can help you build a strong foundation in web development and can help you develop the skills you need to succeed in this role.
Network Engineer
Network Engineer
Network Engineers design, build, and maintain computer networks. They work with a variety of networking technologies and tools to ensure that networks are reliable and secure. This course can help you build a strong foundation in network engineering and can help you develop the skills you need to succeed in this role.
Systems Analyst
Systems Analyst
Systems Analysts work with businesses to understand their needs and to develop and implement solutions to business problems. They work with a variety of stakeholders, including engineers, designers, and business analysts, to ensure that solutions meet the needs of users. This course can help you build a strong foundation in systems analysis and can help you develop the skills you need to succeed in this role.
IT Architect
IT Architect
IT Architects design and develop IT systems. They work with a variety of stakeholders, including engineers, designers, and business analysts, to ensure that IT systems meet the needs of users. This course can help you build a strong foundation in IT architecture and can help you develop the skills you need to succeed in this role.
Security Analyst
Security Analyst
Security Analysts identify and mitigate security risks. They work with a variety of security technologies and tools to protect organizations from cyberattacks. This course can help you build a strong foundation in security analysis and can help you develop the skills you need to succeed in this role.
For more career information including salaries, visit:
OpenCourser.com/course/ckn42i/deploying
Reading list
We've selected ten 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 Models in Production.
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow. It covers all the essential concepts, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn how to use these tools to build and deploy machine learning models.
Provides a comprehensive overview of deep learning with Python. It covers all the essential concepts, from neural networks to convolutional neural networks. It valuable resource for anyone who wants to learn how to use deep learning to solve real-world problems.
Provides a comprehensive overview of machine learning. It covers all the essential concepts, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn the fundamentals of machine learning.
Save
Provides a comprehensive overview of machine learning. It covers all the essential concepts, from decision trees to neural networks. It valuable resource for anyone who wants to learn the fundamentals of machine learning.
Provides a comprehensive overview of statistical methods for machine learning. It covers all the essential concepts, from probability to Bayesian inference. It valuable resource for anyone who wants to learn how to use statistical methods to build and evaluate machine learning models.
Provides a comprehensive overview of mathematics for machine learning. It covers all the essential concepts, from linear algebra to calculus. It valuable resource for anyone who wants to learn the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning. It covers all the essential concepts, from neural networks to convolutional neural networks. It valuable resource for anyone who wants to learn the fundamentals of deep learning.
Provides a gentle introduction to machine learning. It covers all the essential concepts, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn the basics of machine learning.
Provides a practical introduction to machine learning. It covers all the essential concepts, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems.
Provides a gentle introduction to machine learning. It covers all the essential concepts, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn the basics of machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ckn42i/deploying
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course
Level
Advanced
Via
Coursera
Institution
DeepLearning.AI
Instructors
Laurence Moroney
Robert Crowe
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
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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 (viewing)
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
Good to know
Enhances learning with practical tools and hands-on exercises
Provides hands-on practice, aiding learners in developing strong skills in model serving
Useful for learners interested in deploying ML models and making them accessible to users
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