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Google Cloud Training
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants:
Read more
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants:
Read more
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants:
Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact.
Software Engineers looking to develop Machine Learning Engineering skills.
ML Engineers who want to adopt Google Cloud for their ML production projects.
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What's inside
Syllabus
Welcome to the Machine Learning Operations (MLOps): Getting Started
This module provides the overview of the course
Employing Machine Learning Operations
Read more
Syllabus
Welcome to the Machine Learning Operations (MLOps): Getting Started
This module provides the overview of the course
Employing Machine Learning Operations
Read more
ML practitioners’ pain points, The concept of DevOps in ML, The three phases of the ML lifecycle, Automating the ML process
Vertex AI and MLOps on Vertex AI
What is Vertex AI and why does a unified platform matter?, Introduction to MLOps on Vertex AI, How does Vertex AI help with the MLOps workflow? Part 1, How does Vertex AI help with the MLOps workflow? Part 2
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes tools for the deployment, testing, monitoring, and automation of ML systems, which are integral to Machine Learning Engineering
Covers different aspects of MLOps, such as tools, best practices, and a unified platform (Vertex AI), which provides a comprehensive understanding of the field
Taught by Google Cloud Training, a reputable organization specializing in cloud computing and ML systems
Introduces MLOps concepts to beginners, making it accessible to those new to the field
Focuses on the practical aspects of MLOps, preparing learners to apply the concepts directly to their projects
Save this course
Save Machine Learning Operations (MLOps): Getting Started to your list so you can find it easily later:
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Reviews summary
Mlops with google cloud platform
Learners say that this course is an introductory "getting started" course for the Machine Learning Operations (MLOps) discipline offered through Google Cloud Platform (GCP). Based on student feedback, the hands-on elements of the course are well received with some raised concerns that there are various bugs and technical issues which may inhibit learners from completing the course or receiving their certificates. Many learners mention that the course instructors are engaging and knowledgeable, but the theory-to-lab connection could be improved.
The course instructors are engaging and knowledgeable.
"Videos and explanations are really good"
"MLOps fundamentals is a good introduction, great teachers!"
The tech support is slow and doesn't provide helpful answers.
"The Tech Support is slow getting back to you, there's too many of them messaging you asking the same question about your problems."
The course provides a good overview of MLOps, but it tries to balance between being both generic and technical.
"The course gives a nice overview, but either it should be more generic and fun, or more detailed and techy but also longer."
Students are unable to complete the course and receive their certificates due to technical issues.
"Cannot complete the course because the last lab requires you to create a git fork using the qwiklab account, but there is no way to receive the verification email on the account, which results in in inability to complete the course."
The hands-on labs are frustrating and don't always work properly.
"Labs are frustrating because they don't simply work, not because you did something wrong."
"The labs are buggy and setting up the cloud resources takes a lot of time."
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 Machine Learning Operations (MLOps): Getting Started with these
activities:
Seek Guidance from Experienced MLOps Practitioners
Show steps
Accelerate your MLOps learning by connecting with experienced practitioners who can provide valuable insights and guidance.
Browse courses on
Machine Learning
Show steps
-
Attend industry events and meetups to meet MLOps professionals.
-
Reach out to MLOps practitioners on LinkedIn and ask for advice.
-
Join an MLOps community or forum and engage in discussions.
-
Find a mentor who can provide personalized support and guidance.
Join an MLOps Study Group
Show steps
Join an MLOps study group to connect with fellow learners, discuss course concepts, and support each other's progress, fostering a collaborative learning environment.
Show steps
-
Find or create a study group
-
Establish regular meeting times
-
Share knowledge and resources
Review Google Cloud Platform Concepts
Show steps
Review the basics of Google Cloud Platform, including its services, architecture, and pricing.
Browse courses on
GCP
Show steps
-
Read through the Google Cloud Platform documentation, starting with the following links:
-
Familiarize yourself with the Google Cloud Console and create a free trial account.
-
Experiment with the various Google Cloud services, such as Compute Engine, Cloud Storage, and BigQuery.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Attend an MLOps Workshop
Show steps
Attend an MLOps workshop to gain practical insights and hands-on experience from industry experts, accelerating your learning and professional growth.
Show steps
-
Research and identify relevant workshops
-
Register for the workshop
-
Actively participate and engage with the instructors and attendees
Develop an MLOps Plan
Show steps
Create a comprehensive MLOps plan that outlines your deployment, monitoring, and evaluation strategies, ensuring a structured approach to ML production.
Show steps
-
Define the project scope
-
Identify the tools and technologies to use
-
Establish monitoring and evaluation metrics
Work through Hands-on Labs on Vertex AI
Show steps
Gain hands-on experience with Vertex AI by completing guided labs that cover topics such as model deployment, monitoring, and evaluation.
Browse courses on
Machine Learning
Show steps
-
Sign up for a free Qwiklabs account.
-
Choose a hands-on lab that aligns with your interests and skill level.
-
Follow the lab instructions carefully and complete all the exercises.
Deploy a Machine Learning Model with Vertex AI
Show steps
Follow a guided tutorial to deploy a machine learning model using Vertex AI, allowing you to practice the MLOps workflow and gain hands-on experience.
Show steps
-
Create a Vertex AI project
-
Build a machine learning model
-
Deploy the model using Vertex AI
Assist Students in an Introductory MLOps Course
Show steps
Share your knowledge and help others learn about MLOps by volunteering as a mentor in an introductory course or workshop.
Browse courses on
Machine Learning
Show steps
-
Offer your services as a mentor to an MLOps course or workshop.
-
Provide guidance and support to students who are new to MLOps.
-
Answer questions, share resources, and provide feedback on student projects.
Automate ML Deployment with Argo Workflows
Show steps
Practice automating the ML deployment process using Argo Workflows, enhancing your MLOps capabilities and learning advanced deployment techniques.
Show steps
-
Install Argo Workflows
-
Create a workflow definition
-
Trigger the workflow
Participate in an MLOps Hackathon
Show steps
Put your MLOps skills to the test by participating in a hackathon that challenges you to solve real-world problems using MLOps techniques.
Browse courses on
Machine Learning
Show steps
-
Find an MLOps hackathon that aligns with your interests.
-
Form a team or work individually.
-
Develop an innovative solution to the hackathon challenge.
-
Present your solution to a panel of judges.
Contribute to an MLOps Project
Show steps
Contribute to an open-source MLOps project to gain hands-on experience, collaborate with the community, and enhance your understanding of real-world MLOps applications.
Show steps
-
Identify an open-source MLOps project
-
Review the project's documentation and codebase
-
Identify an area where you can make a contribution
Write a Blog Post on Best Practices for MLOps in the Finance Industry
Show steps
Share your expertise and insights on MLOps best practices in the finance industry by writing a blog post.
Browse courses on
Machine Learning
Show steps
-
Research best practices for MLOps in the finance industry.
-
Write a blog post that outlines these best practices, providing real-world examples and case studies.
-
Publish your blog post on a reputable platform.
-
Promote your blog post on social media and other channels.
Develop an MLOps Pipeline for a Real-World Dataset
Show steps
Apply your MLOps knowledge to a real-world dataset by building a complete pipeline that includes data preprocessing, model training, deployment, and monitoring.
Browse courses on
Machine Learning
Show steps
-
Identify a suitable dataset from Kaggle or another data repository.
-
Preprocess the data and prepare it for modeling.
-
Train and evaluate several machine learning models.
-
Deploy the best-performing model to Vertex AI.
-
Monitor the deployed model and make adjustments as needed.
Mentor Junior MLOps Engineers
Show steps
Mentor junior MLOps engineers to share your knowledge and experience, guiding their professional growth and fostering a sense of community within the field.
Show steps
-
Identify opportunities to mentor
-
Share your knowledge and expertise
-
Provide guidance and support
Seek Guidance from Experienced MLOps Practitioners
Show steps
Accelerate your MLOps learning by connecting with experienced practitioners who can provide valuable insights and guidance.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Attend industry events and meetups to meet MLOps professionals.
- Reach out to MLOps practitioners on LinkedIn and ask for advice.
- Join an MLOps community or forum and engage in discussions.
- Find a mentor who can provide personalized support and guidance.
Join an MLOps Study Group
Show steps
Join an MLOps study group to connect with fellow learners, discuss course concepts, and support each other's progress, fostering a collaborative learning environment.
Show steps
Show steps
Show steps
- Find or create a study group
- Establish regular meeting times
- Share knowledge and resources
Review Google Cloud Platform Concepts
Show steps
Review the basics of Google Cloud Platform, including its services, architecture, and pricing.
Browse courses on
GCP
Show steps
Show steps
Show steps
- Read through the Google Cloud Platform documentation, starting with the following links:
- Familiarize yourself with the Google Cloud Console and create a free trial account.
- Experiment with the various Google Cloud services, such as Compute Engine, Cloud Storage, and BigQuery.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Attend an MLOps Workshop
Show steps
Attend an MLOps workshop to gain practical insights and hands-on experience from industry experts, accelerating your learning and professional growth.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register for the workshop
- Actively participate and engage with the instructors and attendees
Develop an MLOps Plan
Show steps
Create a comprehensive MLOps plan that outlines your deployment, monitoring, and evaluation strategies, ensuring a structured approach to ML production.
Show steps
Show steps
Show steps
- Define the project scope
- Identify the tools and technologies to use
- Establish monitoring and evaluation metrics
Work through Hands-on Labs on Vertex AI
Show steps
Gain hands-on experience with Vertex AI by completing guided labs that cover topics such as model deployment, monitoring, and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Sign up for a free Qwiklabs account.
- Choose a hands-on lab that aligns with your interests and skill level.
- Follow the lab instructions carefully and complete all the exercises.
Deploy a Machine Learning Model with Vertex AI
Show steps
Follow a guided tutorial to deploy a machine learning model using Vertex AI, allowing you to practice the MLOps workflow and gain hands-on experience.
Show steps
Show steps
Show steps
- Create a Vertex AI project
- Build a machine learning model
- Deploy the model using Vertex AI
Assist Students in an Introductory MLOps Course
Show steps
Share your knowledge and help others learn about MLOps by volunteering as a mentor in an introductory course or workshop.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Offer your services as a mentor to an MLOps course or workshop.
- Provide guidance and support to students who are new to MLOps.
- Answer questions, share resources, and provide feedback on student projects.
Automate ML Deployment with Argo Workflows
Show steps
Practice automating the ML deployment process using Argo Workflows, enhancing your MLOps capabilities and learning advanced deployment techniques.
Show steps
Show steps
Show steps
- Install Argo Workflows
- Create a workflow definition
- Trigger the workflow
Participate in an MLOps Hackathon
Show steps
Put your MLOps skills to the test by participating in a hackathon that challenges you to solve real-world problems using MLOps techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an MLOps hackathon that aligns with your interests.
- Form a team or work individually.
- Develop an innovative solution to the hackathon challenge.
- Present your solution to a panel of judges.
Contribute to an MLOps Project
Show steps
Contribute to an open-source MLOps project to gain hands-on experience, collaborate with the community, and enhance your understanding of real-world MLOps applications.
Show steps
Show steps
Show steps
- Identify an open-source MLOps project
- Review the project's documentation and codebase
- Identify an area where you can make a contribution
Write a Blog Post on Best Practices for MLOps in the Finance Industry
Show steps
Share your expertise and insights on MLOps best practices in the finance industry by writing a blog post.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research best practices for MLOps in the finance industry.
- Write a blog post that outlines these best practices, providing real-world examples and case studies.
- Publish your blog post on a reputable platform.
- Promote your blog post on social media and other channels.
Develop an MLOps Pipeline for a Real-World Dataset
Show steps
Apply your MLOps knowledge to a real-world dataset by building a complete pipeline that includes data preprocessing, model training, deployment, and monitoring.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable dataset from Kaggle or another data repository.
- Preprocess the data and prepare it for modeling.
- Train and evaluate several machine learning models.
- Deploy the best-performing model to Vertex AI.
- Monitor the deployed model and make adjustments as needed.
Mentor Junior MLOps Engineers
Show steps
Mentor junior MLOps engineers to share your knowledge and experience, guiding their professional growth and fostering a sense of community within the field.
Show steps
Show steps
Show steps
- Identify opportunities to mentor
- Share your knowledge and expertise
- Provide guidance and support
Career center
Learners who complete Machine Learning Operations (MLOps): Getting Started will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. As the demand for data scientists grows, those with strong knowledge of the Python programming language, statistics, and machine learning algorithms are highly sought after. This course helps build a foundation in the fundamentals of machine learning for those interested in a career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, deploys, maintains, and monitors machine learning systems. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Therefore, this course may be helpful for those looking to work as a Machine Learning Engineer.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud. Thus, this course may be helpful for Software Engineers looking to develop Machine Learning Engineering skills.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. As such, this course may be useful for Data Analysts looking to gain a better understanding of MLOps.
Business Analyst
Business Analyst
A Business Analyst analyzes an organization's business needs and develops solutions to improve efficiency and effectiveness. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Hence, this course may be useful for Business Analysts looking to gain a better understanding of MLOps.
Product Manager
Product Manager
A Product Manager manages the development and launch of new products or features. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. As a result, this course may be useful for Product Managers looking to gain a better understanding of MLOps.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Consequently, this course may be useful for Project Managers looking to gain a better understanding of MLOps.
Data Architect
Data Architect
A Data Architect designs and builds the infrastructure for data storage and management. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Thus, this course may be useful for Data Architects looking to gain a better understanding of MLOps.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Therefore, this course may be useful for Database Administrators looking to gain a better understanding of MLOps.
Systems Analyst
Systems Analyst
A Systems Analyst analyzes and designs computer systems. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Consequently, this course may be useful for Systems Analysts looking to gain a better understanding of MLOps.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects an organization's computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. As such, this course may be useful for Information Security Analysts looking to gain a better understanding of MLOps.
Computer Network Architect
Computer Network Architect
A Computer Network Architect designs, implements, and maintains computer networks. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Therefore, this course may be useful for Computer Network Architects looking to gain a better understanding of MLOps.
Software Developer
Software Developer
A Software Developer designs, develops, and maintains software applications. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Thus, this course may be useful for Software Developers looking to gain a better understanding of MLOps.
Web Developer
Web Developer
A Web Developer designs, develops, and maintains websites. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Hence, this course may be useful for Web Developers looking to gain a better understanding of MLOps.
IT Manager
IT Manager
An IT Manager plans, implements, and manages an organization's IT systems. This course provides an introduction to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems. Consequently, this course may be useful for IT Managers looking to gain a better understanding of MLOps.
For more career information including salaries, visit:
OpenCourser.com/course/3ur8oe/machine
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Operations (MLOps): Getting Started.
Provides a practical introduction to deep learning using PyTorch, offering a hands-on approach to building and training deep learning models.
Offers a comprehensive overview of the principles and practices involved in designing and building data-intensive applications, providing foundational knowledge for MLOps.
Practical guide to deep learning using Fastai and PyTorch, providing hands-on experience with ML modeling techniques.
Provides a hands-on introduction to MLOps, with a focus on real-world applications. It covers topics such as data exploration, model training, and deployment.
Provides a comprehensive introduction to ML using popular libraries such as Scikit-Learn, Keras, and TensorFlow, offering a solid foundation for MLOps.
Save
Provides a solid theoretical foundation for MLOps, covering topics such as machine learning algorithms, model evaluation, and system design.
Provides a probabilistic perspective on machine learning, covering topics such as Bayesian inference and graphical models. It valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Practical guide to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to MLOps, covering topics such as data engineering, model training, and deployment. It valuable resource for anyone who wants to build and deploy machine learning models in production.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3ur8oe/machine
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Effort
3 weeks of study, 6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Emphasizes tools for the deployment, testing, monitoring, and automation of ML systems, which are integral to Machine Learning Engineering
Covers different aspects of MLOps, such as tools, best practices, and a unified platform (Vertex AI), which provides a comprehensive understanding of the field
Taught by Google Cloud Training, a reputable organization specializing in cloud computing and ML systems
Introduces MLOps concepts to beginners, making it accessible to those new to the field
Focuses on the practical aspects of MLOps, preparing learners to apply the concepts directly to their projects
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