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Overview

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.

Duration: 8h 

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Objective

After completing the course, students will have the following knowledge:

  • Identify and use core technologies required to support effective MLOps.
  • Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
  • Implement reliable and repeatable training and inference workflows.
  • Adopt the best CI/CD practices in the context of ML systems.
  • Operate deployed machine learning models effectively and efficiently.
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Audience
  • 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.
prerequisite
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OUTLINE

01

Why and When do we need MLOps

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Discuss Data Scientists' pain points.
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Identify ML Engineering characteristics and challenges.
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Define how Google Cloud can help with MLOps.
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Recognize how MLOps differs from manual ML management.
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Compare and contrast DevOps vs MLOps.

02

Understanding the Main Kubernetes Components (Optional)

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Define what is a Docker container.
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Create Docker containers.
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Identify the architecture of Kubernetes: pods, namespaces.
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Create Docker containers using Google Container Builder.
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Store container images in Google Container Registry.
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Create a Kubernetes Engine cluster.
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Manage Kubernetes deployments.

03

Introduction to AI Platform Pipelines

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Identify the benefits and opportunities of AI Pipelines.
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Define Access Controls within AI Pipelines.
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Recognize pipeline components.
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List pipeline workflows.
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Set up AI Platform Pipelines.
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Create a machine learning pipeline.
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Run a machine learning pipeline.
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Connect to AI Platform Pipelines using the Kubeflow Pipelines SDK.
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Configure a Google Kubernetes Engine cluster for AI Platform Pipelines.

04

Training, Tuning and Serving on AI Platform

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Identify the main concepts of MLOps on AI Platform.
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Create a reproducible dataset.
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Implement a tunable model.
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Build and push a training container.
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Train and tune a model.
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Serve and query a model.

05

Kubeflow Pipelines on AI Platform

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Recognize how Kubeflow Pipelines fits in MLOps.
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Describe a Kubeflow Pipeline with KF DSL.
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Use the various Kubeflow components.
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Compile, upload, and run a pipeline build in Kubeflow Pipelines.

06

CI/CD for Kubeflow Pipelines on AI Platform

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Create Cloud Build Builders.
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Configure pipelines with Cloud Build.
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Create triggers for training models using Cloud Build Triggers.
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Adopt the best CI/CD practices in the context of ML systems.

07

Summary

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Summarize the course.
Study with
Google Cloud expert

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Student feedback

Cloud Ace Training
Bringing great experiences to students

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Trần Tuấn Anh

IT

After completing the Associate Cloud Engineer course, I knew how to operate and deploy projects on Google Cloud and confidently took the Google Cloud certification exam.

Nguyễn Ngọc Minh Thy

Data Engineer

After completing the Professional Data Engineer course, I have enough knowledge and confidence to take the Google Cloud certification exam to prepare for my upcoming job.

Trương Quốc Thắng

Data Engineer

I learned how to choose tools and apply them to businesses to process data effectively through the Professional Data Engineer course.

Phạm Văn Hùng

IT

Khóa học rất chi tiết và đầy đủ, sau khi học xong khóa học Associate Cloud Engineer, mình rất muốn có cơ hội học thêm các khóa học khác để hiểu rõ hơn về Google Cloud

Dương Minh Phương

Engineer

Sau khi học xong khóa học Associate Cloud Engineer, mình đã hiểu rõ về Google Cloud và có thể đưa ra các giải pháp cho doanh nghiệp triển khai các dự án trên GCP
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REGISTER NOW

TO BECOME " GOOGLE CLOUD EXPERT"

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    câu hỏi thường gặp

    Cloud Ace is a Google Cloud training unit, so it does not organize exams and provide Google Cloud certifications. Cloud Ace only supports providing certificates of course completion for students while waiting for the Google Cloud certification exam

    In addition, if you want to take the Google Cloud certification exam, Cloud Ace will guide you to register for the Online or Offline exam at the authorized Google Cloud test centers in Vietnam.

    Of course, during the learning process, you will constantly be solving quizzes, simulated mock tests that are similar to Google Cloud's actual exam questions. In addition, Cloud Ace also provides Dump questions that are constantly updated with question types, exam questions from Google Cloud to help you have the best preparation for the exam.

    Of course. You will be supported by Cloud Ace during the learning process and even at the end of the course. You can interact with the Trainer via Slack, email hoặc qua Group Google Cloud Plartform User HCM để được các Trainer hỗ trợ nhé.

    After completing the course, if you have any questions about the knowledge or have difficulties in implementing the project on Google Cloud, you can contact the Trainer for answers.

    The Google Cloud course is not only suitable for software engineers or system development engineers, but also suitable for data processing engineers such as Data Analytics, Data Engineer, Data Scientist.

    In addition, if you are a Marketer or working in the field of finance, banking, e-commerce, logistics .... constantly faced with big data to solve, then you can refer to the courses Big Data Machine Learning Fundamental or From Data to Insight on Google Cloud Platform courses to refer to simple data processing and create professional reports on Google Cloud.