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 part of the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
MLOps (Machine Learning Operations) Fundamentals
About this Course
What you will learn
Identify and use core technologies required to support effective MLOps.
Adopt the best CI/CD practices in the context of ML systems.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Implement reliable and repeatable training and inference workflows.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Welcome to MLOps Fundamentals
This module provides the overview of the course
Why and When do we need MLOps
In this module, we take a look at machine learning from an operations perspective. This means taking a whole-system view: from defining the problem to the solution.
Understanding the Main Kubernetes Components (Optional)
This module is optional
Introduction to AI Platform Pipelines
In this module, we’ll be discussing a Google Cloud product, AI Platform Pipelines, that makes MLOps easy, seamless, and scalable with Google Cloud Services.
Training, Tuning and Serving on AI Platform
In this module, we will learn how to train, tune, and serve a model manually from the Jupyter notebook on AI Platform.
Kubeflow Pipelines on AI Platform
In this module, we will automate the training and tuning process we described before using a Kubeflow pipeline. Instead of having to trigger every single step of the process manually from the Jupyterlab notebook, we can trigger the entire process with a single click after we have expressed the various steps as a Kubeflow pipeline.
CI/CD for Kubeflow Pipelines on AI Platform
In this module, we will be talking about CI/CD for Kubeflow pipelines. We know how to build an automated Kubeflow pipeline, but how can we integrate this pipeline in a continuous integration stack? The goal is to rebuild pipeline assets immediately when new training code is pushed to the corresponding repository.
This module is a recap of what was covered in the course
- 5 stars47.68%
- 4 stars25.77%
- 3 stars14.94%
- 2 stars4.89%
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TOP REVIEWS FROM MLOPS (MACHINE LEARNING OPERATIONS) FUNDAMENTALS
Great course to start for learning about MLOps. However, I hope there will have more videos to explain details on LABs.
excellent experience. thank you very much coursera and google to give the oppurtunity to get certificate free.
The whole process of building the Kubeflow pipelines for MLOPs including the configuration part (what does into the Dockerfile, cloud build) has been explained fully.
Videos are good, a lab was initially broken but fixed in a few days.
About the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.
Here's what you have to do 1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate 2) Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam 3) Review the Professional Machine Learning Engineer exam guide 4) Complete Professional Machine Learning Engineer sample questions 5) Register for the Google Cloud certification exam (remotely or at a test center) Applied Learning Project This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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Last Updated: September 1, 2015
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