Machine Learning on Google Cloud
Coursera · Google Cloud · Updated
Platform rating
4.4/5
AI Tutor Rating
8.2/10
Duration
Multi-course specialization
Classes
8
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code? When should you use automated machine learning or custom training? This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker); use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; use Vertex Vizier hyperparameter tuning to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems, write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <
Machine Learning on Google Cloud is a multi-course specialization on Coursera authored by Google Cloud. It teaches practical machine learning implementation on Google's platform, focusing on building, training, and deploying models at scale. The curriculum covers key services like Vertex AI for AutoML and custom training, BigQuery ML for model building with SQL, and tools for feature engineering and hyperparameter tuning. This specialization serves professionals who want to apply ML solutions using Google Cloud's managed services, with a strong emphasis on practical deployment and MLOps practices without requiring extensive coding.
What you'll learn in Machine Learning on Google Cloud
Our Review of Machine Learning on Google Cloud
This specialization offers a structured, product-centric path into applied machine learning, leveraging Google Cloud's managed services to lower the barrier to entry. The teaching format, delivered by the platform creator, focuses on translating ML theory into actionable skills using Vertex AI, BigQuery ML, and TensorFlow. The progression from no-code AutoML to custom containerized training jobs suggests a curriculum designed to build competency incrementally, culminating in the ability to deploy and manage models in a production cloud environment. The learning outcomes are concrete and oriented towards implementation, promising the ability to build specific model types and utilize key platform features for model improvement.
The $49 monthly subscription price for Coursera, combined with a sharable certificate, positions this as a cost-effective credential for professionals seeking to validate Google Cloud ML skills. However, the value is intrinsically tied to access to Google Cloud Platform (GCP) for hands-on labs, which likely involves separate usage costs. The structure assumes a learner is motivated to engage with the specific GCP ecosystem. For those targeting this platform, the specialization provides a coherent roadmap from foundational concepts to more advanced custom training, effectively bridging the gap between ML knowledge and cloud-native deployment.
Pros and cons of Machine Learning on Google Cloud
Pros
- Authored and taught by Google Cloud, ensuring direct platform expertise and current best practices.
- Covers a practical spectrum from no-code AutoML to custom TensorFlow training, offering scalable learning paths.
- Focus on deployment and MLOps skills like using Feature Store, which are critical for production readiness.
- Outcomes are highly actionable, teaching specific tasks like building BigQuery ML models with SQL and deploying jobs on Vertex AI.
- The certificate provides a recognized credential for a relatively low monthly subscription cost on Coursera.
Things to consider
- Heavily product-specific to Google Cloud, limiting transferable value for those using other cloud providers.
- While prerequisites are listed as none, working with TensorFlow and containers implies intermediate technical comfort is needed for later courses.
- True hands-on learning requires a Google Cloud Platform account, which may incur additional costs beyond the course fee.
Who should take Machine Learning on Google Cloud?
This specialization is best for data analysts, engineers, or developers who are committed to the Google Cloud ecosystem and need to operationalize machine learning models. It fits those who want to leverage managed services like Vertex AI and BigQuery ML to build and deploy models efficiently, from prototyping with AutoML to scaling with custom training. It's ideal for practitioners seeking a vendor-specific, production-oriented curriculum that prioritizes deployment skills over deep theoretical ML research.
Machine Learning on Google Cloud at a glance
| Provider | Coursera |
|---|---|
| Instructor | Google Cloud |
| Level | Beginner |
| Time to complete | Multi-course specialization |
| Pricing | $49 |
| Certificate | Certificate |
| Prerequisites | None |
Fit
Best for
Not ideal for
The bottom line on Machine Learning on Google Cloud
Machine Learning on Google Cloud is a focused, practical specialization that delivers significant value for professionals aiming to implement ML solutions within the Google Cloud Platform. It smartly balances accessibility through no-code tools with depth via custom training, though its utility is confined to the GCP ecosystem. For the right learner targeting this platform, it's a cost-effective way to gain credible, applied skills and a certificate directly from the source.
Machine Learning on Google Cloud: frequently asked questions
What is the Machine Learning on Google Cloud specialization primarily about?
The Machine Learning on Google Cloud specialization teaches you how to build, train, and deploy machine learning models at scale using Google Cloud services like Vertex AI and BigQuery ML, covering both automated solutions and custom training workflows.
Do I need to know how to code to take this machine learning course?
The course is designed to start without coding, teaching Vertex AI AutoML models first. However, later sections on custom training with TensorFlow and containers will require some programming knowledge, despite the stated 'none' for prerequisites.
What is the cost and value of the certificate for this Coursera specialization?
The specialization costs $49 per month on Coursera and offers a certificate upon completion. This provides a cost-effective, platform-endorsed credential for professionals seeking to validate their Google Cloud ML skills for career advancement.
How does this Google Cloud ML course compare to a general machine learning course?
Unlike a general ML theory course, this specialization is intensely practical and platform-specific, focusing on implementation tools like BigQuery ML and Vertex AI. It trades broad algorithmic depth for hands-on skills in deploying and managing models within the Google Cloud ecosystem.
How can I get the most out of the Machine Learning on Google Cloud specialization?
To get the most from this specialization, have an active Google Cloud Platform account ready for the hands-on labs, and be prepared to engage deeply with the specific services taught, like Vertex AI and BigQuery, to translate the lessons into practical experience.