Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Advance your career as a Cloud ML Engineer

Instructor: Google Cloud Training

55,989 already enrolled

Included with Coursera Plus

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6 course series

Earn a career credential that demonstrates your expertise

4.5

(2,255 reviews)

Intermediate level

Recommended experience

2 months

at 10 hours a week

Flexible schedule

Learn at your own pace

6 course series

Earn a career credential that demonstrates your expertise

4.5

(2,255 reviews)

Intermediate level

Recommended experience

2 months

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll 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

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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Professional Certificate - 6 course series

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)

Applied Learning Project

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.

Applied Learning Project

This specialization incorporates hands-on labs using Google's Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

Introduction to AI and Machine Learning on Google Cloud

Course 19 hours

What you'll learn

Recognize the data-to-AI technologies and tools offered by Google Cloud.
Use generative AI capabilities in applications.
Choose between different options to develop an AI project on Google Cloud.
Build ML models end-to-end by using Vertex AI.

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)

Category: Google Cloud Platform

Category: Machine Learning

Category: Prompt Engineering

Category: Generative AI

Category: Natural Language Processing

Category: MLOps (Machine Learning Operations)

Category: Application Programming Interface (API)

Category: Cloud Infrastructure

Category: Large Language Modeling

Category: Cloud Platforms

Build, Train and Deploy ML Models with Keras on Google Cloud

Course 213 hours

What you'll learn

Design and build a TensorFlow input data pipeline.
Use the tf.data library to manipulate data in large datasets.
Use the Keras Sequential and Functional APIs for simple and advanced model creation.
Train, deploy, and productionalize ML models at scale with Vertex AI.

Skills you'll gain

Category: Keras (Neural Network Library)

Category: Tensorflow

Category: Google Cloud Platform

Category: Deep Learning

Category: Artificial Neural Networks

Category: Data Pipelines

Category: Application Deployment

Category: Data Processing

Category: Scalability

Category: Machine Learning

Category: MLOps (Machine Learning Operations)

Category: Applied Machine Learning

Category: Data Transformation

Feature Engineering

Course 38 hours

What you'll learn

Describe Vertex AI Feature Store and compare the key required aspects of a good feature.
Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
Discuss how to preprocess and explore features with Dataflow and Dataprep.
Use tf.Transform.

Skills you'll gain

Category: Feature Engineering

Category: Keras (Neural Network Library)

Category: Tensorflow

Category: Data Pipelines

Category: Data Transformation

Category: Dataflow

Category: MLOps (Machine Learning Operations)

Category: Machine Learning

Category: Real Time Data

Category: Data Modeling

Category: Data Processing

Machine Learning in the Enterprise

Course 417 hours

What you'll learn

Describe data management, governance, and preprocessing options
Identify when to use Vertex AutoML, BigQuery ML, and custom training
Implement Vertex Vizier Hyperparameter Tuning
Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI

Skills you'll gain

Category: Google Cloud Platform

Category: Data Pipelines

Category: Workflow Management

Category: Machine Learning

Category: MLOps (Machine Learning Operations)

Category: Feature Engineering

Category: Data Management

Category: Applied Machine Learning

Category: Continuous Monitoring

Category: Predictive Modeling

Category: Data Governance

Production Machine Learning Systems

Course 518 hours

What you'll learn

Compare static versus dynamic training and inference
Manage model dependencies
Set up distributed training for fault tolerance, replication, and more
Export models for portability

Skills you'll gain

Category: Tensorflow

Category: Performance Tuning

Category: Data Pipelines

Category: Google Cloud Platform

Category: Systems Design

Category: Machine Learning

Category: Distributed Computing

Category: MLOps (Machine Learning Operations)

Category: Hybrid Cloud Computing

Category: Applied Machine Learning

Category: Systems Architecture

Category: Scalability

Machine Learning Operations (MLOps): Getting Started

Course 62 hours

What you'll learn

Identify and use core technologies required to support effective MLOps.
Adopt the best CI/CD practices in the context of ML systems.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Implement reliable and repeatable training and inference workflows.

Skills you'll gain

Category: MLOps (Machine Learning Operations)

Category: Google Cloud Platform

Category: Machine Learning

Category: Continuous Deployment

Category: Continuous Monitoring

Category: DevOps

Category: Machine Learning Software

Category: Software Development Methodologies

Category: Continuous Integration

Category: Automation

Earn a career certificate

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Instructor

Google Cloud Training

Google Cloud

1,848 Courses3,459,422 learners

Offered by

Google Cloud

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Frequently asked questions

This specialization is designed to be completed in 6 months at approximately 5 hours per week.

To get the most out of this course, we recommend participants have data engineering or programming experience and are interested in learning how to apply machine learning in practice.

We strongly recommend you take these courses in order, beginning with Big Data and Machine Learning Fundamentals. This is especially important when completing the Qwiklabs projects, as these hands-on labs build upon the work you complete in preceding courses.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.