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Advance your career as a Cloud ML Engineer
Instructor: Google Cloud Training
51,886 already enrolled
Included with
(2,197 reviews)
Recommended experience
Intermediate level
We recommend participants have data engineering or programming experience and are interested in learning how to apply machine learning in practice
(2,197 reviews)
Recommended experience
Intermediate level
We recommend participants have data engineering or programming experience and are interested in learning how to apply machine learning in practice
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
Add to your LinkedIn profile
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.
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.
Describe how to improve data quality and perform exploratory data analysis
Build and train AutoML Models using Vertex AI and BigQuery ML
Optimize and evaluate models using loss functions and performance metrics
Create repeatable and scalable training, evaluation, and test datasets
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.
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.
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
Compare static versus dynamic training and inference
Manage model dependencies
Set up distributed training for fault tolerance, replication, and more
Export models for portability
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.
Develop a high level understanding of TFX standard pipeline components.
Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with KubeFlow and AI Platform Pipelines
Perform continuous training with Composer and MLFlow
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
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.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.
¹ Median salary and job opening data are sourced from Lightcast™ Job Postings Report. Data for job roles relevant to featured programs (2/1/2024 - 2/1/2025)
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