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.

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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

Traffic lights

Read about what's good

what should give you pause

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

Practical ml pipelines with google cloud

According to learners, this course offers an exceptionally practical and relevant deep dive into building ML pipelines on Google Cloud. It is highly valued by ML engineers and professionals seeking to deploy models at scale. Key strengths include its focus on hands-on labs using technologies like TensorFlow Extended (TFX), Cloud Composer, and MLflow, which are praised for their direct applicability to real-world MLOps challenges. While the course is highly effective for those with the stated advanced prerequisites, some note a fast pace and the need for a solid prior foundation in GCP and TensorFlow. Overall, it's considered up-to-date and helps to solidify understanding of complex production ML workflows.

Instructors provide clear and helpful explanations.

"The instructors did a great job explaining complex concepts, making the material digestible."

"The instructors' explanations were clear and helped connect theory to practice, which I found very beneficial."

"I found the instructors very knowledgeable and engaging throughout the course."

Effectively deepens grasp of complex concepts.

"It really helped solidify my understanding of building robust ML pipelines on GCP."

"This course directly builds on the MLOps Fundamentals and the TensorFlow on GCP specialization. It connected many dots for me."

"The instructors' explanations were clear and helped connect theory to practice, making complex ideas easier to grasp."

Covers current and essential MLOps tools.

"The sections on MLflow and custom TFX components were particularly useful."

"It connected many dots for me regarding productionizing ML models. The focus on CI/CD with pipelines was spot on."

"I appreciated the integration of various tools like Kubeflow and AI Platform Pipelines, very comprehensive."

"The course is very up-to-date with current best practices in ML engineering."

Provides essential practical experience through labs.

"The hands-on labs with TFX and Cloud Composer were incredibly practical and directly applicable to my work."

"As an ML engineer, this course was exactly what I needed. It provided practical knowledge for deploying and managing ML pipelines at scale. The hands-on exercises were crucial."

"I enjoyed the practical approach to ML pipelines... The labs were mostly great, very helpful."

Occasional debugging or environment issues reported.

"Some labs felt a bit rushed, and I wished there was more time for debugging and understanding the code fully."

"The labs were often frustrating to debug without more guidance, leading to some delays in my progress."

"The labs were mostly great, but a couple of them had minor issues that took time to resolve, which was a slight setback."

Requires significant prior ML and GCP knowledge.

"Found this course quite challenging. While the content is relevant, the pace was too fast for me, and I felt like I needed more background in specific Google Cloud services than was stated."

"This course assumes way too much prior knowledge. Even with the stated prerequisites, I struggled."

"Valuable, but definitely advanced. If you're coming in without a strong foundation in TensorFlow and GCP, you'll be lost."

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

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Reviewing course materials will help you get an overview of the course and identify areas where you need additional support.

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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

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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

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Practice data preprocessing techniques

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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

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Six other activities

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Review TFX documentation

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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.

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Build a simple ML pipeline with TFX

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Enhance your hands-on experience by building your own ML pipeline with TFX, applying the concepts you've learned in the course.

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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

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Expand your knowledge of ML pipeline orchestration by following guided tutorials that demonstrate best practices and real-life scenarios.

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Practice creating and deploying ML pipelines with TFX

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Solidify your understanding of TFX by practicing the creation and deployment of ML pipelines multiple times.

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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

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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.

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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

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Apply your knowledge of ML pipelines to a real-world scenario by developing a proposal for a project that leverages Google Cloud's platform.

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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