Take your machine learning projects from ideation to production with Google Machine Learning.

In this course you will learn how to implement ML pipelines with continuous training and CI/CD practices to increase your ML workflow development and deployment velocity, automation, and ability to scale with your data on Google Cloud.

Overview

In this GCP-MPGC: ML Pipelines on Google Cloud 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.

Skills Covered

  • 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.

Prerequisites

To get the most out of this course, participants should have:

Target Audience

This course is primarily intended for the following participants:

  • 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.

Course Curriculum

Module 1: Introduction to TFX

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

Module 2: Pipeline orchestration with TFX

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

Module 3: Custom components and CI/CD for TFX pipelines

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

Module 4: ML Metadata with TFX

  • Access and analyze pipeline artifacts in ML Metadata store.

Module 5: Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

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

Module 6: Continuous Training with Cloud Composer

  • Perform continuous training with Cloud Composer.

Module 7: ML Pipelines with MLflow

  • Manage Machine Learning lifecycle with MLflow.

Module 8: Summary

  • Summarize the course.

Dates & Locations

Let’s make it work for you

Can’t find a date that fits? Need to train your whole team? Looking for a discount?
Speak to one of our learning experts today.

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July 20, 2026 - July 23, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 842
RM 9600

July 20, 2026 - July 23, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 842
RM 9600

October 19, 2026 - October 22, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 842
RM 9600

October 19, 2026 - October 22, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 842
RM 9600
Trainocate exam and cert

Exam & Certification

Professional Machine Learning Engineer.

Training & Certification Guide

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. ML Engineers consider responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models.

They should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation, as well as familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.

Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance

The Professional Machine Learning Engineer exam assesses your ability to:

  • Frame ML problems
  • Architect ML solutions
  • Design data preparation and processing systems
  • Develop ML models
  • Automate and orchestrate ML pipelines
  • Monitor, optimize, and maintain ML solutions

Length: Two hours

Registration fee: $200 (plus tax where applicable)

Language: English

Exam format: Multiple choice and multiple select

Exam Delivery Method:

a. Take the online-proctored exam from a remote location, review the online testing requirements

b. Take the onsite-proctored exam at a testing center, locate a test center near you

Prerequisites: None

Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Frequently Asked Questions

Google Cloud certifications help you advance your professional skills and demonstrate your value to hiring managers. Also once you become Google Cloud certified, you unlock the following benefits:

  • Distinguish yourself with a digital badge by sharing it on your social profile or resume.
  • Showcase your achievement on a publicly-accessible Google Cloud Certified Directory.
  • Get exclusive Google Cloud Certified swag for Professional certifications.
  • Network and exchange ideas with others in the Google Cloud Certified community.
  • Get access to global cloud virtual and in-person events hosted by Google Cloud.

A skill badge measures one’s knowledge of a specific product or service and tests their ability to apply that knowledge in an interactive hands on environment.

A certification measures an individual’s proficiency at performing a specific job role using Google Cloud technology. A certification exam tests one knowledge of a wide range of products and services needed to perform a job role versus one product/service. In order to prepare for a Google Cloud certification, it is recommended that an individual has multiple years of experience in the role, in addition to completing the recommended online training and skill badges.

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