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Google Cloud Training
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.
Read more
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.
Read more
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.
Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites:
You have a good ML background and have been creating/deploying ML pipelines
You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses)
You have completed the MLOps Fundamentals course.
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Register for this course and see more details by visiting:
OpenCourser.com/course/gk1imk/ml
What's inside
Syllabus
Welcome to ML Pipelines on Google Cloud
This module introduces the course and shares the course outline
Introduction to TFX Pipelines
Read more
Syllabus
Welcome to ML Pipelines on Google Cloud
This module introduces the course and shares the course outline
Introduction to TFX Pipelines
Read more
Pipeline orchestration with TFX
Custom components and CI/CD for TFX pipelines
ML Metadata with TFX
Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
Continuous Training with Cloud Composer
ML Pipelines with MLflow
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
A solid choice for experienced ML practitioners looking to apply their knowledge to real-world problems
Integrates a solid theoretical foundation with hands-on practice through the use of real-world case studies
Taught by industry experts from Google Cloud, ensuring learners stay up-to-date with the latest advancements in ML pipelines
Covers a comprehensive range of topics, from pipeline orchestration and metadata management to continuous training and CI/CD for ML pipelines
Requires a strong foundation in ML, TFX, and related tools, making it unsuitable for complete beginners
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Reviews summary
Mixed reviews for ml course on google cloud
Learners say this course on ML Pipelines on Google Cloud has good content, but many experience problems with the labs. According to reviews, there are monotonous presentations, outdated materials, and insufficient support. However, the course instructors are knowledgeable and provide clear explanations.
Instructors are knowledgeable.
"The Good: The instructors were clear and concise and provided just the right amount of context and justification for the concepts they presented.."
Content quality varies.
"Content was more like monologue, having proper video/animations would certainly help to new users who are new to these concepts."
"Presenters are "talking-heads" reading documentation-like script"
"Slides are detached from what presenter is saying and rather distract because one have double cognitive load: follow monotonic presenters talk and align it with "managerial Comics" shown on the slide 30% of a time. Other 30% are random code pieces, rest 60% is something truly useful."
Labs are problematic.
"The course is fine but guided labs little out of sync for the content. "
"Although insightful, many of the labs have problems which you need to debug on your own."
"Labs are poorly writtenLots of issuesqwiklabs provide only standard solution which doesn't resolve any issueAveragae turnaround time of qwiklabs for tickets are pretty high (more than a week) for any bugs in the lab"
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 ML Pipelines on Google Cloud with these
activities:
Gather and review course resources
Show steps
Reviewing course materials will help you get an overview of the course and identify areas where you need additional support.
Show steps
-
Gather all course materials, including syllabi, textbooks, and lecture notes.
-
Review the syllabus to get an overview of the course content and schedule.
-
Identify any areas where you need additional support, such as prerequisite skills or knowledge.
Review core ML concepts
Show steps
Strengthen your foundation in ML by reviewing the core concepts, ensuring you have a solid understanding before delving into the complexities of ML pipelines.
Browse courses on
Machine Learning
Show steps
Practice data preprocessing techniques
Show steps
Ensure your data preprocessing skills are up-to-date by practicing various techniques, such as data cleaning, feature engineering, and data transformation.
Browse courses on
Data Preprocessing
Show steps
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review TFX documentation
Show steps
Review the official TFX documentation to reinforce your understanding of the core concepts and best practices for building and managing ML pipelines on Google Cloud.
Show steps
Build a simple ML pipeline with TFX
Show steps
Enhance your hands-on experience by building your own ML pipeline with TFX, applying the concepts you've learned in the course.
Show steps
-
Define your ML problem and gather the necessary data.
-
Create a TFX pipeline using the provided templates.
-
Configure the pipeline components, such as the feature engineering and training steps.
-
Run the pipeline and monitor its progress.
-
Evaluate the results and make adjustments as necessary.
Follow tutorials on ML pipeline orchestration
Show steps
Expand your knowledge of ML pipeline orchestration by following guided tutorials that demonstrate best practices and real-life scenarios.
Show steps
Practice creating and deploying ML pipelines with TFX
Show steps
Solidify your understanding of TFX by practicing the creation and deployment of ML pipelines multiple times.
Show steps
-
Follow along with the provided tutorials and exercises.
-
Experiment with different pipeline configurations to see their impact.
-
Troubleshoot any errors or issues that you encounter.
-
Seek support from the TFX community or online forums if needed.
Create a blog post on ML pipelines on Google Cloud
Show steps
Reinforce your understanding and demonstrate your expertise by creating a blog post that shares your learnings about building and deploying ML pipelines on Google Cloud.
Show steps
-
Identify the key concepts and best practices you want to cover.
-
Write a draft of your blog post, outlining the main points.
-
Add examples and code snippets to illustrate your points.
-
Proofread and edit your post carefully.
-
Publish your blog post on a platform of your choice.
Develop a proposal for an ML pipeline project
Show steps
Apply your knowledge of ML pipelines to a real-world scenario by developing a proposal for a project that leverages Google Cloud's platform.
Show steps
-
Identify a business problem that can be solved with ML.
-
Research and select the appropriate ML algorithms and techniques.
-
Design an ML pipeline that incorporates the chosen algorithms and techniques.
-
Develop a budget and timeline for the project.
-
Write a compelling proposal that outlines the project's goals, benefits, and implementation plan.
Gather and review course resources
Show steps
Reviewing course materials will help you get an overview of the course and identify areas where you need additional support.
Show steps
Show steps
Show steps
- Gather all course materials, including syllabi, textbooks, and lecture notes.
- Review the syllabus to get an overview of the course content and schedule.
- Identify any areas where you need additional support, such as prerequisite skills or knowledge.
Review core ML concepts
Show steps
Strengthen your foundation in ML by reviewing the core concepts, ensuring you have a solid understanding before delving into the complexities of ML pipelines.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
Practice data preprocessing techniques
Show steps
Ensure your data preprocessing skills are up-to-date by practicing various techniques, such as data cleaning, feature engineering, and data transformation.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review TFX documentation
Show steps
Review the official TFX documentation to reinforce your understanding of the core concepts and best practices for building and managing ML pipelines on Google Cloud.
Show steps
Show steps
Show steps
Build a simple ML pipeline with TFX
Show steps
Enhance your hands-on experience by building your own ML pipeline with TFX, applying the concepts you've learned in the course.
Show steps
Show steps
Show steps
- Define your ML problem and gather the necessary data.
- Create a TFX pipeline using the provided templates.
- Configure the pipeline components, such as the feature engineering and training steps.
- Run the pipeline and monitor its progress.
- Evaluate the results and make adjustments as necessary.
Follow tutorials on ML pipeline orchestration
Show steps
Expand your knowledge of ML pipeline orchestration by following guided tutorials that demonstrate best practices and real-life scenarios.
Show steps
Show steps
Show steps
Practice creating and deploying ML pipelines with TFX
Show steps
Solidify your understanding of TFX by practicing the creation and deployment of ML pipelines multiple times.
Show steps
Show steps
Show steps
- Follow along with the provided tutorials and exercises.
- Experiment with different pipeline configurations to see their impact.
- Troubleshoot any errors or issues that you encounter.
- Seek support from the TFX community or online forums if needed.
Create a blog post on ML pipelines on Google Cloud
Show steps
Reinforce your understanding and demonstrate your expertise by creating a blog post that shares your learnings about building and deploying ML pipelines on Google Cloud.
Show steps
Show steps
Show steps
- Identify the key concepts and best practices you want to cover.
- Write a draft of your blog post, outlining the main points.
- Add examples and code snippets to illustrate your points.
- Proofread and edit your post carefully.
- Publish your blog post on a platform of your choice.
Develop a proposal for an ML pipeline project
Show steps
Apply your knowledge of ML pipelines to a real-world scenario by developing a proposal for a project that leverages Google Cloud's platform.
Show steps
Show steps
Show steps
- Identify a business problem that can be solved with ML.
- Research and select the appropriate ML algorithms and techniques.
- Design an ML pipeline that incorporates the chosen algorithms and techniques.
- Develop a budget and timeline for the project.
- Write a compelling proposal that outlines the project's goals, benefits, and implementation plan.
Career center
Learners who complete ML Pipelines on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are a vital part of the modern workforce, helping businesses make better decisions through the use of data. They design and implement machine learning models, and work with data scientists and software engineers to bring these models to production. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Machine Learning Engineer and help your organization make better use of data.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They develop and implement machine learning models, and work with stakeholders to communicate the results of their analyses. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Data Scientist and help your organization make better use of data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with stakeholders to understand their needs, and then design and implement software solutions. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Software Engineer and help your organization build better software.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. They use statistical techniques to identify trends and patterns in data, and then communicate their findings to stakeholders. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Data Analyst and help your organization make better use of data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop and implement trading strategies, and work with portfolio managers to make investment decisions. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Quantitative Analyst and help your organization make better investment decisions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. They work with stakeholders to understand their needs, and then design and implement AI solutions. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of AI models. With this knowledge, you can become a more effective Artificial Intelligence Engineer and help your organization build better AI systems.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with other researchers to publish their findings in academic journals and conferences. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Machine Learning Researcher and help advance the field of machine learning.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. They work with data scientists and software engineers to ensure that data is available in the right format and at the right time. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Data Engineer and help your organization build better data pipelines.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with stakeholders to understand their needs, and then design and implement cloud solutions. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models in the cloud. With this knowledge, you can become a more effective Cloud Architect and help your organization build better cloud systems.
DevOps Engineer
DevOps Engineer
DevOps Engineers work with software engineers to automate the software development and deployment process. They use tools and techniques to ensure that software is delivered quickly and reliably. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective DevOps Engineer and help your organization build better software.
Product Manager
Product Manager
Product Managers work with stakeholders to define and develop new products. They work with engineers and designers to bring these products to market. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Product Manager and help your organization build better products.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to understand their needs and develop solutions to meet those needs. They use data and analysis to identify opportunities and solve problems. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Business Analyst and help your organization make better decisions.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and develop data visualization tools and applications. They work with data scientists and analysts to communicate the results of their analyses in a clear and concise way. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Data Visualization Engineer and help your organization communicate data more effectively.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and product managers to explain how products work and how to use them. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Technical Writer and help your organization create better documentation.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with stakeholders to define project goals, develop project plans, and track project progress. This course can help you build a foundation in machine learning pipelines, which are essential for managing the development and deployment of machine learning models. With this knowledge, you can become a more effective Project Manager and help your organization deliver successful projects.
For more career information including salaries, visit:
OpenCourser.com/course/gk1imk/ml
Reading list
We've selected six 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
ML Pipelines on Google Cloud.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It is considered a classic in the field and is widely used as a textbook in academic institutions.
Save
Provides a comprehensive overview of the mathematical foundations of machine learning. It covers topics such as linear algebra, calculus, probability, and optimization, and provides clear and concise explanations.
Provides a practical guide to machine learning using Python. It covers the fundamental concepts, algorithms, and applications, and includes hands-on exercises and case studies.
Provides a practical guide to machine learning for programmers. It covers the fundamental concepts, algorithms, and applications, and includes hands-on exercises and case studies.
Provides a practical guide to machine learning using R. It covers the fundamental concepts, algorithms, and applications, and includes hands-on exercises and case studies.
Provides a practical guide to machine learning using Scikit-Learn and TensorFlow. It covers the entire pipeline from data preprocessing to model deployment, and includes hands-on exercises and case studies.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gk1imk/ml
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Effort
3 weeks of study, 6-8 hours/week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
A solid choice for experienced ML practitioners looking to apply their knowledge to real-world problems
Integrates a solid theoretical foundation with hands-on practice through the use of real-world case studies
Taught by industry experts from Google Cloud, ensuring learners stay up-to-date with the latest advancements in ML pipelines
Covers a comprehensive range of topics, from pipeline orchestration and metadata management to continuous training and CI/CD for ML pipelines
Requires a strong foundation in ML, TFX, and related tools, making it unsuitable for complete beginners
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