ML Pipelines on Google Cloud from Coursera

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.

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

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

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

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

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.

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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Artificial Intelligence Engineer

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.

See salaries and explore the career path for Machine Learning Researcher

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Cloud Architect

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.

See salaries and explore the career path for DevOps Engineer

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Data Visualization Engineer

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.

See salaries and explore the career path for Technical Writer

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.

See salaries and explore the career path for Project Manager