Implement machine learning models and deploy the latest artificial intelligence tools.

Data science and Machine Learning are becoming core capabilities for solving complex real-world problems, transforming industries, and delivering value in all domains. Therefore, many businesses are investing in their data science teams and ML capabilities to develop predictive models that can deliver business value to their users.

In this Google Cloud course you will learn MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

Overview

This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals 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.

Skills Covered

  • 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.
  • Identify and use core technologies required to support effective MLOps.

 

Prerequisites

Completed Machine Learning with Google Cloud or have equivalent experience

Target 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.

 

Course Curriculum

Module 1: Why and When do we need MLOps

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

Module 2: Understanding the Main Kubernetes Components

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

Module 3: Introduction to AI Platform Pipelines

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

Module 4: Training, Tuning and Serving on AI Platform

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

Module 5: Kubeflow Pipelines on AI Platform

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

Module 6: CI/CD for Kubeflow Pipelines on AI Platform

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

Module 7: 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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September 9, 2026 - September 9, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 842
RM 2400

September 9, 2026 - September 9, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 842
RM 2400

December 9, 2026 - December 9, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 842
RM 2400

December 9, 2026 - December 9, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 842
RM 2400
Trainocate exam and cert

Exam & Certification

Professional Machine Learning Engineer.

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

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.

Step 1: Get real world experience

Before attempting the Machine Learning Engineer exam, it’s recommended that you have 3+ years of hands-on experience with Google Cloud products and solutions. Ready to start building? Explore the Google Cloud Free Tier for free usage (up to monthly limits) of select products.

Try the Google Cloud Free Tier

Step 2: Understand what’s on the exam

The exam guide contains a complete list of topics that may be included on the exam. Review the exam guide to determine if your skills align with the topics on the exam.

See exam guide

Step 3: Review the sample questions

Familiarize yourself with the format of questions and example content that may be covered on the Machine Learning Engineer exam.

Review sample questions

Step 4: Round out your skills with training

Prepare for the exam by following the Machine Learning Engineer learning path and the recommended courses below:

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.

Speak to a Training Consultant

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