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Overview

In this course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata. Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.

Duration: 8h

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Objective

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

  • Orchestrate model training and deployment with TFX and Cloud AI Platform.
  • Operate deployed machine learning models effectively and efficiently.
  • Perform continuous training using various frameworks (Scikit Learn, XGBoost, PyTorch) and orchestrate pipelines using Cloud Composer and MLFlow.
  • Integrate ML workflows with upstream and downstream data management workflows to maintain end-to-end lineage and metadata management.
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Audience
  • Data Scientists looking to deliver business impact by quickly converting from Machine Learning prototype to production.
  • Software Engineers looking to develop Machine Learning Engineering skills
  • ML Engineers who want to adopt Google Cloud.
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prerequisite
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OUTLINE

01

Module 1: Introduction to TFX

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Develop a high level understanding of TFX standard pipeline components.
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Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
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Work with the Tensorflow Data Validation (TFDV) library to check and analyze input data.
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Utilize the Tensorflow Transform (TFT) library for scalable data preprocessing and feature transformations.
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Use the KerasTuner library for model hyperparameter tuning.
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Employ the Tensorflow Model Analysis (TFMA) library for model evaluation.

02

Pipeline orchestration with TFX

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Use the TFX CLI and Kubeflow UI to build and deploy TFX pipelines to a hosted AI Platform Pipelines instance on Google Cloud.
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Deploy a TensorFlow model trained using AI Platform Training to AI Platform Prediction.
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Perform advanced distributed hyperparameter tuning using CloudTuner and Cloud AI Platform Vizier.

03

Custom components and CI/CD for TFX pipelines

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Develop a CI/CD workflow with Cloud Build to build and deploy a TFX Pipeline.
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Integrate Github trigger to trigger Cloud Build CI/CD workflow for a TFX pipeline.

04

ML Metadata with TFX

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Access and analyze pipeline artifacts in ML Metadata store.

05

Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

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Perform continuous training with Scikit-learn and AI Platform Pipelines.
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Perform continuous training with PyTorch and AI Platform Pipelines.
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Perform continuous training with XGBoost and AI Platform Pipelines.
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Perform continuous training with TensorFlow and AI Platform Pipelines.

06

Continuous Training with Cloud Composer

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Perform continuous training with Cloud Composer.

07

ML Pipelines with MLflow

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Manage Machine Learning lifecycle with MLflow.

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Summary

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Summarize the course.
Study with
Google Cloud expert

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Student feedback

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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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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.