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This professional certificate incorporates hands-on labs using Qwiklabs platform. These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Learn How to Enroll


Difficulty Level: INTERMEDIATE
Estimated Learning Time: 7 Months - Approximately 5 hours of study a week
IT tech using keyboard

Skills You Will Gain

Google Cloud

Machine Learning

Feature Engineering


Cloud Computing


Google Cloud Platform

Application Programming Interfaces (API)

Inclusive ML

Data Cleansing

Python Programming

Build Input Data Pipeline

About this Professional Certificate

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.

Here's what you have to do

1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate

2) Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam

3) Review the Professional Machine Learning Engineer exam guide

4) Complete Professional Machine Learning Engineer sample questions

5) Register for the Google Cloud certification exam (remotely or at a test center)

What you will learn

  • Learn the skills needed to be successful in a machine learning engineering role
  • Prepare for the Google Cloud Professional Machine Learning Engineer certification exam
  • Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies
  • Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications


Courses in this Professional Certificate




Google Cloud Big Data and Machine Learning Fundamentals

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

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

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

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




Feature Engineering

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

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.




Production Machine Learning Systems

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

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.




ML Pipelines on Google Cloud

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.