OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Architecting Production-ready ML Models Using Google Cloud ML Engine is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Read Reviews → Enroll Active
Read Reviews → Enroll Active
Read Reviews → Enroll Active
Course image
Pluralsight logo

Architecting Production-ready ML Models Using Google Cloud ML Engine

Vitthal Srinivasan
Read more
This course is no longer available. Find something similar by browsing:
ML Engine Distributed Training Hyperparameter Tuning XGBoost scikit-learn TensorFlow

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches distributed training and hyperparameter tuning, which are necessary for building enterprise-grade machine learning models
Emphasizes making machine learning models available to non-data scientists and non-Python users
Focuses on Google Cloud ML Engine, which provides a comprehensive platform for training and deploying machine learning models
Leverages XGBoost, a popular machine learning framework known for its ensemble learning technique
Covers model serialization and porting from on-premise to Google Cloud Platform
Explores distributed training, hyperparameter tuning, and prediction in TensorFlow, a renowned deep learning library

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Career center

Learners who complete Architecting Production-ready ML Models Using Google Cloud ML Engine will develop knowledge and skills that may be useful to these careers:
Cloud Architect
Cloud Architects design and manage cloud computing systems. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Cloud Architects as they learn to deploy and manage machine learning models in the cloud.
See salaries and explore the career path for Cloud Architect
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Quantitative Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Quantitative Analyst
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Statisticians as they learn to apply machine learning to their work.
See salaries and explore the career path for Statistician
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve problems in business and industry. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Operations Research Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use their knowledge of finance to analyze and make recommendations on investments. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Financial Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Financial Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Business Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Business Analyst
Data Scientist
Data Scientists use their knowledge of machine learning and other statistical methods to extract insights from data. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Data Scientists as they dive deeper into the practical applications of ML, especially in distributed settings.
See salaries and explore the career path for Data Scientist
Market Research Analyst
Market Research Analysts use their knowledge of marketing and research to understand consumer behavior and trends. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Market Research Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Market Research Analyst
User Experience Researcher
User Experience Researchers study how users interact with products and services. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine can be useful to User Experience Researchers by providing foundational knowledge of how to create and implement ML solutions that enhance user experience.
See salaries and explore the career path for User Experience Researcher
Data Analyst
Data Analysts use their skills in data analysis and visualization to help businesses make better decisions. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Data Analysts as they learn to apply machine learning to their work.
See salaries and explore the career path for Data Analyst
Technical Writer
Technical Writers create documentation and other materials that explain technical concepts. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Technical Writers as they learn to write about machine learning and its applications.
See salaries and explore the career path for Technical Writer
Software Engineer
Software Engineers design, build, and maintain software systems. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Software Engineers as they learn to integrate machine learning models into their software products.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers oversee the development and launch of new products. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Product Managers as they learn to incorporate machine learning into their products.
See salaries and explore the career path for Product Manager
Product Designer
Product Designers create and develop products that meet the needs of users. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful to Product Designers as they learn to integrate machine learning into their products.
See salaries and explore the career path for Product Designer
Machine Learning Engineer
Machine Learning Engineers design and build scalable machine learning models that solve complex problems. Courses like Architecting Production-ready ML Models Using Google Cloud ML Engine may be useful as they provide foundational training in how to architect and deploy these solutions.
See salaries and explore the career path for Machine Learning Engineer

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Architecting Production-ready ML Models Using Google Cloud ML Engine.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a hands-on introduction to machine learning, using Scikit-Learn, Keras, and TensorFlow as programming libraries. It covers various machine learning algorithms, including supervised and unsupervised learning.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Introduction to Machine Learning, fourth edition
Save
Provides a comprehensive guide to distributed machine learning, using Python as a programming language. It covers various distributed machine learning algorithms and systems, as well as their applications in various domains.
Introduction to Machine Learning, fourth edition
Paperback
Check
Introduction to Machine Learning, fourth edition
Kindle Edition
Check
Cover image
Deep Learning with R, Second Edition
Save
Provides a comprehensive guide to deep learning, using R as a programming language.
Deep Learning with R, Second Edition
Paperback
$$$
Deep Learning with R, Second Edition
Kindle Edition
$$
Cover image
Deep Learning with Python, Second Edition
Save
Provides a hands-on introduction to deep learning, using Python as a programming language. It covers various deep learning architectures, including convolutional neural networks and recurrent neural networks.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
TensorFlow for Deep Learning
Save
Provides a comprehensive guide to TensorFlow, a popular deep learning library. It covers various TensorFlow concepts, including data loading, model building, and training.
TensorFlow for Deep Learning: From Linear...
Kindle Edition
$$
Cover image
Hands-on Machine Learning with JavaScript
Save
Provides a comprehensive guide to machine learning, using JavaScript as a programming language. It covers various machine learning algorithms and techniques, such as supervised and unsupervised learning.
Hands-on Machine Learning with JavaScript: Solve...
Kindle Edition
$$
Cover image
Machine Learning with R
Save
Provides a comprehensive guide to machine learning, using R as a programming language. It covers various machine learning algorithms, including supervised and unsupervised learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Building Machine Learning Systems with Python
Save
Provides a gentle introduction to machine learning, using Python as a programming language. It covers various machine learning basics, such as data preprocessing, model training, and evaluation.
Building Machine Learning Systems with Python -...
Paperback
$$
Building Machine Learning Systems with Python:...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Architecting Production-ready ML Models Using Google Cloud ML Engine.
An Introduction to Practical Deep Learning
Most relevant
Machine Learning with Python: Build & Optimize
Most relevant
Improving Deep Neural Networks: Hyperparameter Tuning,...
Most relevant
NLP - Natural Language Processing with Python
Most relevant
Building End-to-end Machine Learning Workflows with...
Most relevant
Building Features from Numeric Data
Most relevant
Building Machine Learning Models in Python with scikit...
Most relevant
Machine Learning with XGBoost Using scikit-learn in Python
Most relevant
Building Clustering Models with scikit-learn
Most relevant

Pluralsight Buyer Protection & Guarantee

Verified partner policies & purchase protections

Enterprise Technology Platform
Free Trial & Audit

10-Day Free Trial

10-day free trial (up to 200 minutes of content) with access to the Skills video library and skill assessments. Cancel before day 10 to avoid automatic conversion.

Return & Refund Policy

Cancel Anytime (Non-Refundable Once Billed)

Cancel easily online anytime before the 10-day trial expires to prevent billing. Once subscription fees are paid, individual subscriptions are non-refundable (cancellation stops future renewals).

Buyer Protections

Verified Academic & Industry Partner

Skill IQ and Role IQ diagnostic benchmark assessments, hands-on lab environments, and curated learning paths designed by technology leaders.

Key Purchase Protections
10-day free trial on individual Skills plans
Skill IQ assessments to benchmark proficiency
Cancel anytime online before trial ends without fees
Hands-on interactive labs and enterprise-grade tech content
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Time
2 hours
Via
Pluralsight
Instructor
Vitthal Srinivasan
This course belongs to a collection called Google: Professional Cloud Data Engineer. It's comprised of 18 courses:
  1. Architecting Big Data Solutions Using Google Dataproc
  2. Architecting Serverless Big Data Solutions Using Google Dataflow
  3. Architecting Data Warehousing Solutions Using Google BigQuery
  4. Building Machine Learning Models in SQL Using BigQuery ML
  5. Creating and Administering Google Cloud SQL Instances
  6. Architecting Big Data Solutions Using Google Bigtable
  7. Designing and Implementing Solutions Using Google Machine Learning APIs
  8. Designing and Implementing Solutions Using Google Cloud AutoML
  9. Building Pipelines for Workflow Orchestration Using Google Composer
  10. Architecting Stream Processing Solutions Using Google Cloud Pub/Sub
  11. Creating and Administering Google Cloud Spanner Instances
  12. Architecting Schemaless Scalable NoSQL Databases Using Google Datastore
  13. Leveraging Fully Managed Redis Datastores Using Google Cloud Memorystore
  14. Leveraging Google Cloud Firestore for Realtime Database Solutions
  15. Architecting Scalable Web Applications with Firebase on the Google Cloud Platform
  16. Developing on the Google Cloud Using Datalab and Cloud Source Repositories
  17. Architecting Production-ready ML Models Using Google Cloud ML Engine (viewing)
  18. Designing Scalable Data Architectures on the Google Cloud
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches distributed training and hyperparameter tuning, which are necessary for building enterprise-grade machine learning models
Emphasizes making machine learning models available to non-data scientists and non-Python users
Focuses on Google Cloud ML Engine, which provides a comprehensive platform for training and deploying machine learning models
Leverages XGBoost, a popular machine learning framework known for its ensemble learning technique
Covers model serialization and porting from on-premise to Google Cloud Platform
Explores distributed training, hyperparameter tuning, and prediction in TensorFlow, a renowned deep learning library
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
This course is currently discontinued or unavailable. See active alternatives above.
Course Unavailable
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser