Take ML models from containers to scalable production serving.
Docker-based ML serving, FastAPI and model versioning - then scalable inference, latency optimization and zero-downtime deployment.
Model Serving Systems: Containers, APIs & Scalability
By Board Infinity · Offered on Coursera
- 39 video lessons · 17 assignments
- No prior Docker needed - basic Python expected
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
Container to production
Docker, FastAPI, scaling and safe deployment, in order
Certificate included
Shareable on LinkedIn, your resume or CV
Graded, not just watched
17 assignments across the four modules
From a trained model to a service that scales.
Built for ML engineers, MLOps practitioners and backend developers who want to take models from notebooks to production.
Model Serving Systems: Containers, APIs & Scalability takes ML models out of development environments and into scalable serving systems: notebook → container → API → scalable serving → production deployment.
You build Docker containers for ML workloads, design REST APIs with FastAPI with model versioning, validation and observability, then scale with horizontal scaling, async processing, load balancing and batch vs. real-time inference.
The last module covers model serialization, blue-green and canary deployment, and automated rollback - keeping uptime while models change.
Course page: 2 weeks at 10 hours a week, updated May 2026. The specialization page separately lists this course as Course 3, 19 hours.
For people who need models running, not just trained.
No prior Docker experience is required. Basic Python is expected; ML training experience is helpful but not required.
Notebook to service
Take trained models out of notebooks and into containerized, versioned services.
Serving that scales
Docker, Compose, scaling patterns, deployment strategies and rollback in one sequence.
APIs for ML models
FastAPI endpoints with Pydantic validation, versioning, error handling and logging.
Shipping models safely
Technical professionals working with ML deployment who need zero-downtime releases.
Four capabilities, model to production.
Cards 1-3 quote the three outcomes Coursera lists; card 4 quotes the course description's deployment goal.
Containerize ML workloads
Build optimized Docker images and multi-container ML apps using Docker Compose and multi-stage builds
Design production-grade ML APIs
Design scalable REST APIs with FastAPI, Pydantic validation, versioning, and error handling
Scale model serving
Scale ML serving with async queues, load balancing, and latency profiling for production workloads
Deploy and safely roll back
Deploy models with ONNX serialization and zero-downtime blue-green rollbacks
Learn model serving with someone in your corner.
A dedicated coach alongside the course, with guidance shaped around the goal you bring.
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
A separate, optional Board Infinity service - not part of Coursera enrollment or the course certificate. 60-minute demo session · 100% refundable · no obligation to continue
A dedicated coach
One person who follows your progress alongside the course.
Goal-oriented guidance
Sessions shaped around the goal you set, not a fixed script.
Practice beyond the quizzes
Extra practice set for you, then reviewed with you.
Aim it at a role
Role-oriented direction on what to learn next.
Four modules, from container to rollback.
Two weeks at 10 hours a week, fully self-paced. Open a module for what Coursera says it covers, what is inside it, and every lesson title.
- Module 01Containerize
Docker images and Compose for ML.
- Module 02Serve
FastAPI endpoints, versioned and validated.
- Module 03Scale
Queues, load balancing, latency.
- Module 04Deploy
Serialize, release, roll back.
- This module introduces containerization fundamentals and shows learners how to build efficient Docker images for ML workloads, ensuring portability and reproducibility across environments.
- The first module covers Docker fundamentals, image optimization, multi-stage builds, secrets management, and Docker Compose for multi-container ML apps.
- Role of Containers in MLOps Careers9 minutes
- Industry Trends in ML Containerization10 minutes
- Key Tools and Platforms8 minutes
- Understanding Containers vs. VMs11 minutes
- Building a Docker Image for ML Models9 minutes
- Running Containers Locally8 minutes
- Multi-Stage Builds6 minutes
- Managing Environment Variables9 minutes
- Secrets and Credentials in Containers6 minutes
- Introduction to Docker Compose10 minutes
- Running ML APIs and Databases Together9 minutes
- Networking Between Containers10 minutes
- Career Scope in ML Containerization15 minutes
- Understanding Containers vs. VMs15 minutes
- Optimizing Docker15 minutes
- Environment Configuration15 minutes
- Docker for ML60 minutes
- Career Scope in ML Containerization30 minutes
- Container Fundamentals30 minutes
- Optimizing Docker Images30 minutes
- Multi-Container Deployments30 minutes
- Learners develop and refine REST APIs for ML model inference, focusing on reliability, scalability, and real-world best practices.
- The second module focuses on REST API design with FastAPI, model versioning, input validation with Pydantic, structured logging, and production-grade error handling.
- Principles of RESTful API Design9 minutes
- Structuring Endpoints for ML Models8 minutes
- Using FastAPI for ML Endpoints.12 minutes
- Why Version Models7 minutes
- Implementing Versioned Endpoints8 minutes
- Handling Multiple Models in Production10 minutes
- Input Schema Validation10 minutes
- Managing Errors and Exceptions8 minutes
- Logging and Observability8 minutes
- Compose Syntax15 minutes
- Multi-Container Deployment Guide15 minutes
- Structuring Endpoints for ML Models15 minutes
- API Design for ML Serving60 minutes
- REST API Architecture for ML30 minutes
- Model Versioning and Routing30 minutes
- Handling Input Validation30 minutes
- This module emphasizes scalability, concurrency, and optimization for production-grade model serving systems.
- The third module teaches scaling strategies — horizontal scaling, async queues, load balancing, batch vs. real-time inference, and latency optimization for high-throughput serving.
- Vertical vs. Horizontal Scaling11 minutes
- Async Processing and Queues8 minutes
- Load Balancing Basics9 minutes
- When to Use Batch Serving11 minutes
- Building Batch Pipelines6 minutes
- Real-Time Inference with Queues9 minutes
- Profiling Inference Performance10 minutes
- Latency Reduction Techniques8 minutes
- Monitoring Throughput and Cost7 minutes
- Why Version Models15 minutes
- Model Registry Integration15 minutes
- API Error Codes15 minutes
- Scaling Model Serving60 minutes
- Scaling Strategies30 minutes
- Batch vs. Real-Time Serving30 minutes
- Performance Optimization30 minutes
- The final module demonstrates how to save, deploy, and safely roll back production models while maintaining uptime and integrity.
- The final module covers model serialization formats (ONNX, pickle, SavedModel), blue-green and canary deployments, automated rollback, and disaster recovery.
- Common Serialization Techniques10 minutes
- Converting Between Formats8 minutes
- Storing and Loading Models7 minutes
- Zero-Downtime Deployments8 minutes
- Blue-Green and Canary Patterns9 minutes
- Staging and Validation8 minutes
- Detecting Failed Deployments8 minutes
- Automated Rollback Workflows9 minutes
- Validating Restored Versions12 minutes
- Debugging Production API Issues15 minutes
- Load Balancing Basics15 minutes
- Building Batch Pipelines15 minutes
- Model Serialization and Deployment60 minutes
- Model Serialization Formats30 minutes
- Deployment Strategies30 minutes
- Rollback and Recovery30 minutes
Titles, order and durations exactly as listed on the Coursera course page.
The tools you work with, and the skills you leave with.
The four official "Tools you'll learn" on the Coursera course page.
Application Programming Interface (API)
The interface your model is served through, designed in Module 2.
Model Deployment
Zero-downtime, blue-green and canary patterns, with automated rollback in Module 4.
Restful API
RESTful design principles, endpoint structure and versioned routes.
Docker (Software)
Images, multi-stage builds, environment variables, secrets and Compose in Module 1.
Named in the course FAQ. Covered in the course, but not part of Coursera's official tools list.
The ten skills Coursera lists for this course, grouped by the stage of serving they belong to.
Packaging
Getting a model into something that runs anywhere.
- Containerization
- Microservices
- MLOps (Machine Learning Operations)
Serving
An API other systems can depend on.
- API Design
- Data Validation
- Software Versioning
Scaling & shipping
Keeping it fast, and releasing changes safely.
- Scalability
- Performance Tuning
- Application Deployment
- Continuous Deployment
Proof you can serve models.
Complete the course on Coursera and earn a shareable career certificate from Board Infinity.
A course certificate issued through Coursera - not university accreditation, a government certification or a professional licence, and not a guarantee of employer recognition.
LinkedIn, resume or CV
Coursera says you can add it to your LinkedIn profile, resume, or CV.
Share it anywhere
Coursera also lists sharing it on social media and in your performance review.
Earned on completion
You earn it when you complete the course work on Coursera.
Two names on it
Board Infinity as the provider, Coursera as the platform.
The work that happens after a model is trained.
Containerized services, REST APIs, versioning, scaling and safe releases - capabilities, not job guarantees.
ML engineering
Packaging trained models as containerized, versioned services.
MLOps
Deployment strategies, rollback and production reliability for ML systems.
Backend / API development for ML
REST APIs with validation, versioning, error handling and logging.
Model serving
Multiple models in production, routing and model versioning.
ML platform engineering
Scaling, async processing, load balancing and throughput monitoring.
Production inference
Batch and real-time inference, profiling and latency optimization.
Docker and FastAPI docs are free. So what is this?
Official documentation and tutorials are excellent, and nobody is pretending otherwise. What a course adds is the order you learn the serving lifecycle in, the checking that makes it stick, and something to show at the end.
The final stage of the MLOps Specialization.
Machine Learning Operations (MLOps) Specialization - "Ship Production-Ready ML Systems That Work." A 3-course series, intermediate level, 12 weeks at 5 hours a week.
DevOps for Machine Learning: CI/CD, APIs & Deployment
Automated ML training pipelines with GitHub Actions, models served through FastAPI, and CI/CD workflows using Docker.
View course Course 2 · Cloud deploymentCloud Platforms for ML: AWS, Azure & GCP Deployment
Cloud ML platforms across AWS, Azure and GCP - SageMaker, Vertex AI or Azure ML Studio, and build-vs-buy decisions.
View course You are here Course 3 · Production model servingModel Serving Systems: Containers, APIs & Scalability
Dockerized ML services, multi-model serving APIs with versioning and A/B testing, latency optimization, batch and real-time inference.
View courseThis is the final course of the specialization; the specialization page lists it at 19 hours. See the specialization.
Know exactly what you're signing up for.
Common questions
Everything learners ask before enrolling, answered from the Coursera course page.
No prior Docker experience is required. Module 1 starts with container fundamentals and guides you through building ML-optimized images from scratch.
The course covers Docker, Docker Compose, FastAPI, Pydantic, ONNX, pickle, TensorFlow SavedModel, load balancers, and message queues for real-time inference.
Yes, basic Python is expected since you'll be writing FastAPI endpoints and model serialization scripts. ML training experience is helpful but not required.
According to Coursera, 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, which lets you access course materials, submit required assessments, and receive a final grade, but not earn or purchase a Certificate. Check the course page for the options available to you.
Coursera says that 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 is added to your Accomplishments page, from where you can print it 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 financial aid or a scholarship is available for your learning program selection, you'll find a link to apply on the description page.
Yes - this page is Board Infinity's own overview of the course. Enrollment, lessons, grading and the certificate all happen on Coursera, where the course is hosted. Optional 1:1 coaching is a separate Board Infinity service.
Want someone building this with you?
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.
Ready to take your ML models into production?
Containerize. Serve. Scale. Deploy.