Deploy ML across AWS, Azure and GCP.
Cloud Platforms for ML: AWS, Azure & GCP Deployment - managed ML services, serverless inference, cloud data pipelines, and how to choose the right platform.
Cloud Platforms for ML: AWS, Azure & GCP Deployment
By Board Infinity · Offered on Coursera
- 39 video lessons · 17 assignments
- AWS, Azure and Google Cloud in one course
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
Three clouds, one course
AWS, then Azure, then Google Cloud, then a platform decision
Certificate included
Shareable on LinkedIn, your resume or CV
Graded, not just watched
17 assignments across the four modules
Three clouds, one way to think about deployment.
A practical cloud-ML course for aspiring cloud ML engineers, data scientists, developers and ML practitioners.
Cloud Platforms for ML: AWS, Azure & GCP Deployment covers enterprise ML deployment across the three major cloud ecosystems: managed ML services, serverless inference and cloud data pipelines.
You work through AWS (SageMaker, Lambda, S3, Glue), Microsoft Azure (ML Studio, Functions, Cognitive Services) and Google Cloud (Vertex AI, BigQuery ML, Cloud Functions), one platform per module.
The last module turns that into a decision: evaluation frameworks for cost, scalability, integration and vendor lock-in, so you can recommend the right platform for an enterprise need.
How a model reaches production in the cloud
From data to a monitored endpoint.
- Store dataAmazon S3, datasets
- PrepareETL with AWS Glue
- TrainSageMaker, ML Studio, Vertex AI
- DeployManaged endpoints
- ServerlessLambda, Functions
- ScaleAutoscaling for inference
- MonitorTesting and monitoring
For people who build ML and need it running in the cloud.
No deep cloud experience is required, but basic familiarity with any cloud platform and ML concepts will help you get the most out of the hands-on activities.
Aspiring cloud ML engineers
Learn enterprise ML deployment across AWS, Azure and Google Cloud in one sequence.
Data scientists
Take trained models into managed services, data pipelines and serving endpoints.
Developers
Build serverless inference with Lambda, Azure Functions and Cloud Functions.
ML practitioners
Compare platforms on cost, scalability and lock-in before committing to one.
Four capabilities, in the order the modules build them.
The first three are the outcomes Coursera lists for this course. The fourth comes from the course description.
Deploy ML models across cloud platforms
Deploy ML models using AWS SageMaker endpoints, Azure Functions, and Google Cloud Vertex AI
Build cloud-scale data pipelines
Build automated data pipelines with AWS S3, Glue, and BigQuery ML for cloud-scale ML
Integrate AI services and serverless inference
Integrate Azure Cognitive Services APIs and serverless inference into production ML workflows
Compare platforms and make the call
Evaluate cost, scalability, and vendor lock-in trade-offs across major cloud ML platforms, and recommend the right one for the business.
Platform comparison matrix
The same job, three ecosystems.
| Capability | AWSAWS | AzMicrosoft Azure | GCGoogle Cloud |
|---|---|---|---|
| Managed training & deployment | SageMaker endpoints | Azure ML Studio | Vertex AI |
| Serverless inference | AWS Lambda | Azure Functions | Cloud Functions |
| Data & pipelines | Amazon S3 and AWS Glue | Dataset management in Studio | BigQuery ML |
| Pre-built AI APIs | Not a focus of this course | Cognitive Services (NLP, Vision) | Not a focus of this course |
Only the services named in this course's modules. Module 4 then compares the platforms on cost, scalability and vendor lock-in.
Learn cloud ML with someone in your corner.
A dedicated coach alongside the course - someone to talk through the hard parts, plan your roadmap and look at your practice work with you.
Book a demo session- A coach matched to your goal in 24 hours
- A written roadmap you keep
- Weekly sessions for as long as you want them
Separate from the Coursera course and its enrollment · 60-minute demo session · no obligation to continue
Get unstuck faster
Talk through the concept that is not clicking with someone who has worked with it.
Your work reviewed
A second pair of eyes on your practice - not just whether it works, but why.
A structured roadmap
A written plan that fits around the course, so every session has a purpose.
Aim it at a role
Which direction fits you, and what to learn next.
Four modules, from AWS to the platform decision.
Two weeks at 10 hours a week, at your own pace. Open a module to see its description, what it covers, and every lesson exactly as it appears on Coursera.
Learners explore AWS’s ML ecosystem, focusing on end-to-end workflows using SageMaker for training and deployment, and S3/Glue for data management.
- Map cloud ML engineer roles, industry trends, skills and certifications
- Use SageMaker capabilities and serverless ML with AWS Lambda
- Deploy models as endpoints, then autoscale, test and monitor them
- Prepare data with AWS Glue and automate dataset updates
Videos
- Cloud ML Engineer Roles and Pathways8 min
- Industry Trends in Cloud ML6 min
- Skills and Certifications9 min
- Overview of AWS AI/ML Services6 min
- SageMaker Capabilities5 min
- Serverless ML with AWS Lambda5 min
- Deploying Models as Endpoints7 min
- Autoscaling for Inference8 min
- Testing and Monitoring Endpoints5 min
- ETL Concepts in AWS5 min
- Using AWS Glue for Data Preparation4 min
- Automating Dataset Updates10 min
Readings
- Reading - Career Scope in Cloud ML Engineering (AWS Focus)15 min
- Reading - Introduction to AWS ML Stack15 min
- Reading - Model Deployment on SageMaker15 min
- Reading - Data Pipelines with S3 and Glue15 min
Assignments
- Practice Quiz : Career Scope in Cloud ML Engineering (AWS Focus)30 min
- Practice Quiz : Introduction to AWS ML Stack30 min
- Practice Quiz : Model Deployment on SageMaker30 min
- Practice Quiz : Data Pipelines with S3 and Glue30 min
- AWS ML Services60 min
This module introduces Azure’s ML platform, highlighting low-code solutions, serverless deployment, and integration with pre-built AI capabilities.
- Navigate Azure ML Studio, manage datasets and train models in Studio
- Create Azure Functions for serverless inference
- Monitor and scale serverless deployments
- Use NLP and Vision APIs, and combine Cognitive Services with custom models
Videos
- Navigating Azure ML Studio7 min
- Dataset Management7 min
- Training Models in Studio6 min
- Introduction to Serverless ML7 min
- Creating Azure Functions for Inference8 min
- Monitoring and Scaling8 min
- Overview of Cognitive Services5 min
- Using NLP and Vision APIs6 min
- Combining Cognitive and Custom Models7 min
Readings
- Reading - Azure ML Studio Overview15 min
- Reading - Deploying with Azure Functions15 min
- Reading - Cognitive Services Integration15 min
Assignments
- Practice Quiz : Azure ML Studio Overview30 min
- Practice Quiz : Deploying with Azure Functions30 min
- Practice Quiz : Cognitive Services Integration30 min
- Graded Quiz : Azure ML Services60 min
Learners dive into Google Cloud’s ML ecosystem, focusing on practical use of Vertex AI, BigQuery ML, and lightweight deployment options.
- Run training and deployment workflows in Vertex AI, with monitoring and metadata
- Build models with SQL and evaluate their performance
- Integrate models with BI tools
- Serve lightweight models with event-driven inference, and test and monitor functions
Videos
- Introduction to Vertex AI6 min
- Training and Deployment Workflows7 min
- Monitoring and Metadata8 min
- Building Models with SQL6 min
- Evaluating Model Performance7 min
- Integrating with BI Tools8 min
- Event-Driven ML Inference6 min
- Deploying Lightweight Models7 min
- Testing and Monitoring Functions7 min
Readings
- Reading - Vertex AI Overview15 min
- Reading - BigQuery ML for Data-Centric Teams15 min
- Reading - Cloud Functions for ML Serving15 min
Assignments
- Practice Quiz : Vertex AI Overview30 min
- Practice Quiz : BigQuery ML for Data-Centric Teams30 min
- Practice Quiz : Cloud Functions for ML Serving30 min
- Graded Quiz : Google Cloud ML Services60 min
Learners synthesize insights from all three cloud platforms and build evaluation frameworks for platform selection, integration, and cost management.
- Define evaluation criteria and analyze feature parity across platforms
- Weigh integration and vendor lock-in
- Understand pricing models, scaling for inference loads and cost simulation tools
- Decide between managed and custom ML services, and present a platform recommendation
Videos
- Defining Evaluation Criteria7 min
- Analyzing Feature Parity6 min
- Integration and Vendor Lock-In6 min
- Understanding Pricing Models6 min
- Scaling for Inference Loads6 min
- Cost Simulation Tools6 min
- Managed vs. Custom ML Services5 min
- Integration Scenarios6 min
- Presenting Platform Recommendations7 min
Readings
- Reading - Platform Comparison Framework15 min
- Reading - Cost and Scalability Analysis15 min
- Reading - Build vs. Buy Decisions15 min
Assignments
- Practice Quiz : Platform Comparison Framework30 min
- Practice Quiz : Cost and Scalability Analysis30 min
- Practice Quiz : Build vs. Buy Decisions30 min
- Graded Quiz : Comparing and Choosing Platforms60 min
Module times are as shown on the course page (5, 4, 4 and 4 hours). The specialization page separately lists this course as Course 2 · 18 hours.
Three cloud ecosystems, and the skills that carry across them.
The tools and skills below are the ones Coursera lists for this course. The services named in the lessons are shown separately.
Amazon Web Services
Also listed: AWS SageMaker and Amazon S3. Module 1 adds Lambda and Glue.
Microsoft Azure
Azure ML Studio, Azure Functions and Cognitive Services in Module 2.
Google Cloud Platform
Vertex AI, BigQuery ML and Cloud Functions in Module 3.
Model Deployment
Endpoints, serverless functions and lightweight models across all three clouds.
AWS LambdaAWS GlueAzure ML StudioAzure FunctionsAzure Cognitive ServicesGoogle Cloud Vertex AIBigQuery MLGoogle Cloud Functions
Named in the course description and lesson titles. Coursera's official "Tools you'll learn" list is: AWS SageMaker, Microsoft Azure, Model Deployment, Amazon S3 and Amazon Web Services.
The twelve skills Coursera lists for this course, grouped by the kind of work they describe.
Cloud platforms
Knowing what each provider offers for ML.
- Cloud Computing
- Public Cloud
- Cloud Platforms
- Google Cloud Platform
Deployment & scale
Getting models served, and keeping them up under load.
- Cloud Deployment
- Serverless Computing
- Scalability
- MLOps (Machine Learning Operations)
Data & architecture
Feeding models, wiring in services, choosing the design.
- Data Pipelines
- AI Integrations
- Applied Machine Learning
- Enterprise Architecture
A credential for your cloud ML work.
Complete the course and earn a shareable career certificate from Board Infinity, issued through Coursera.
Not an accreditation, licence or vendor certification, and no guarantee of recognition by any employer.
Shareable certificate
Earned through Coursera when you complete the course.
Add it to LinkedIn
Add the credential to your LinkedIn profile.
Add it to your resume or CV
List it alongside your other credentials.
Share it on social media
Coursera also mentions using it in your performance review.
Skills that sit where ML meets the cloud.
Role areas where cloud deployment skills are relevant. These are directions, not outcomes the course guarantees - no job, placement or employer recognition is implied.
Cloud ML Engineer
Managed ML platforms, endpoints and autoscaling across AWS, Azure and Google Cloud.
MLOps Engineer
Cloud data pipelines, deployment, monitoring and scaling for inference.
Machine Learning Engineer
Model deployment with SageMaker, Azure ML Studio and Vertex AI.
Data Scientist
BigQuery ML, SQL-based models and getting trained models into serving.
ML/AI Developer
Serverless inference and AI service integrations such as Cognitive Services.
Cloud platforms are full of documentation. So what does a structured course add?
Provider documentation is thorough and free, and you will keep using it. What a course adds is one path through all three clouds, a framework to compare them, and assessments that check what you took in.
Ship production-ready ML systems that work.
This is Course 2 of 3 in the Machine Learning Operations (MLOps) Specialization: DevOps automation, then cloud deployment, then production model serving.
DevOps for Machine Learning: CI/CD, APIs & Deployment
The logical step before this one.
View course Cloud deployment · you are here Course 2Cloud Platforms for ML: AWS, Azure & GCP Deployment
Managed services, serverless inference and platform choice.
View on Coursera Next · production serving Course 3Model Serving Systems: Containers, APIs & Scalability
The natural next stage once you understand cloud platforms.
View course3-course series · 12 weeks at 5 hours a week · view the specialization on Coursera.
Know exactly what you're signing up for.
Common questions
Answered as they appear on the Coursera course page.
No deep cloud experience is required, but basic familiarity with any cloud platform and ML concepts will help you get the most out of the hands-on activities.
The course covers AWS (SageMaker, Lambda, S3, Glue), Microsoft Azure (ML Studio, Functions, Cognitive Services), and Google Cloud (Vertex AI, BigQuery ML, Cloud Functions).
To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can't afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you'll find a link to apply on the description page.
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