Preparing for Google Cloud Certification: Machine Learning Engineer Certificate Online

Enhance your machine learning skills with Google Cloud certificate courses that teach you about architecting, deploying and managing ML models including Vertex AI.

Franklin University has partnered with Coursera Campus to provide cutting-edge certificates to learners seeking to advance. Courses are open to all learners. No application required.

What You Will Learn

  • Be introduced to the big data capabilities of Google Cloud and learn what skills are required to become a successful machine learning engineer
  • Build, train and deploy machine learning models using cutting-edge Goole AI technology like TensorFlow
  • Learn how Professional ML Engineers adopt Google Cloud for ML production projects
  • Plan and prepare for the Google Cloud Professional Machine Learning Engineer certification exam

About the Google Cloud Professional Machine Learning Engineer certification

As emerging technologies advance so, too, can experienced and credentialed professional machine learning engineers. If you’re an ML engineer that designs, builds, tests and deploys ML models using Google Cloud technologies, and you want to advance in your career, then now is the time to prepare for Google Cloud Professional Machine Learning Engineer certification.

This certificate program is specially designed to help you systematically and sequentially prepare for industry-leading certification while you practice and hone your ML skills. 

Each of the nine courses that make up the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate program is self-paced so you can have the flexibility to study and prepare for certification on a schedule that works for you.

With this Professional Certificate, you’ll learn and practice applying machine learning principles and practices to train, retrain, deploy, schedule, monitor and improve ML models. This specialization also incorporates hands-on projects covering a variety of topics, such as Google Cloud Platform (GCP) products, which you’ll use and configure within Google Qwiklabs.

If you're looking to validate your cloud skills and have more confidence in your machine learning capabilities, this Professional Certificate program is for you.

Required Google Cloud Machine Learning Engineer Certificate Courses

Google Cloud Big Data and Machine Learning Fundamentals

BEGINNER | Data Science | Self-paced | 10 hours

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
How Google does Machine Learning

BEGINNER | Data Science | Self-paced | 13 hours

What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.
Launching into Machine Learning

BEGINNER | Data Science | Self-paced | 17 hours

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
TensorFlow on Google Cloud

INTERMEDIATE | Data Science | Self-paced | 15 hours

This course covers designing and building a TensorFlow 2.x input data pipeline, building ML models with TensorFlow 2.x and Keras, improving the accuracy of ML models, writing ML models for scaled use and writing specialized ML models.
Feature Engineering

INTERMEDIATE | Data Science | Self-paced | 14 hours

Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
Machine Learning in the Enterprise

ADVANCED | Data Science | Self-paced | 24 hours

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model. You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker. The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.
Production Machine Learning Systems

ADVANCED | Data Science | Self-paced | 21 hours

This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
MLOps (Machine Learning Operations) Fundamentals

INTERMEDIATE | Data Science | Self-paced | 16 hours

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 primarily intended for the following participants: 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 for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
ML Pipelines on Google Cloud

ADVANCED | Data Science | Self-paced | 11 hours

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

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Frequently Asked Questions

How much does the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate cost?

When you enroll in this self-paced certificate program, you decide how quickly you want to complete each of the courses in the specialization. To access the courses, you pay a small monthly cost of $35, so the total cost of your Professional Certificate depends on you. Plus, you can take a break or cancel your subscription anytime.

How long does it take to finish the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate?

It takes about 4-5 months to finish all the courses and hands-on projects to earn your certificate.

What prior experience do I need to enroll?

This intermediate-level series is for those with data engineering or programming experience and a strong interest in learning to put machine learning concepts into real-world practice.

What will I be able to do with my Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate?

Share this certificate with your professional network to showcase your cloud skills and readiness to advance your ML engineering career. It also means you've prepared for the top-ranked Google Cloud Professional Machine Learning Engineer certification exam.

Do I need to apply and be accepted as a Franklin University student to take courses offered through the FranklinWORKS Marketplace?

No. Courses offered through the Marketplace are for all learners. There is no application or admission process.



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