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Beginner Course 1 · MLOps Specialization

DevOps for Machine Learning: CI/CD, APIs & Deployment

Move machine learning from notebooks to reproducible, deployable systems - Git, GitHub Actions, automated testing, FastAPI and Docker, in four modules.

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2 weeks · 10 hours a week 4 modules · 16 assignments Beginner · Board Infinity Shareable certificate
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Board Infinity Board InfinityInstructor · 287 courses on Coursera
DevOps - Foundation to ML · 4:03

Notebook to production

Version, test, serve and deploy ML models

Certificate included

Shareable on LinkedIn, your resume or CV

Graded, not just watched

16 assignments across the four modules

6 Lakh+learners on our Coursera courses
10+domains covered
270+courses
57+specializations
01 About the course

A trained model is the start. This is everything after it.

Notebook → pipeline → API → container → deployment. The foundation for moving ML models into reproducible engineering workflows.

4
Modules
Version control to deployment
28
Video lessons
Plus 12 readings
16
Assignments
Four in every module
19h
Total learning time
≈ 2 weeks at 10 hours a week

DevOps for Machine Learning: CI/CD, APIs & Deployment teaches you to automate, test, containerize and deploy ML models using Git, GitHub Actions, Docker and FastAPI - bringing DevOps discipline into machine learning workflows.

It moves in four steps: version control and automation foundations, ML pipeline automation, building and serving ML APIs, then packaging and deployment - covering branching, automated testing, data validation, secrets, Docker Compose, API testing and documentation, and monitoring.

It's a beginner course - basic Python and ML familiarity is recommended - and the first of three in Board Infinity's Machine Learning Operations (MLOps) specialization on Coursera.

Inside the course
01Version control & automation foundations≈ 5h 02ML pipeline automation≈ 5h 03Building & serving ML APIs≈ 5h 04Packaging & deployment≈ 5h
02 Who it's for

For people who build models - and need them to ship.

Basic familiarity with Python and ML - training a model, working with datasets - is recommended. Advanced ML theory isn't required.

Aspiring MLOps engineers

Building the foundations

CI/CD, deployment, automation and reproducible ML workflows from the ground up.

Data scientists

Beyond the notebook

Bring the models you train beyond notebooks and into deployable systems.

Developers

DevOps, applied to ML

Apply familiar DevOps engineering practices to ML applications.

Before you start

Recommended experience

Python, ML concepts, training a model and working with datasets. Advanced ML theory isn't required.

03 What you'll learn

Four capabilities, one production workflow.

The course's four stated outcomes on Coursera.

Modules 1-2

Automate

Build CI/CD pipelines with GitHub Actions to automate ML testing, training, and deployment workflows.

Module 3

Serve

Develop REST APIs for ML models using FastAPI with validation, error handling, and OpenAPI docs.

Modules 1, 2 & 4

Containerize

Containerize ML applications using Docker and optimize multi-stage builds for production.

Modules 1-2

Reproduce

Apply Git, DVC, and automated testing to create reproducible, version-controlled ML projects.

1:1 coaching · optional add-on

Learn MLOps with someone in your corner.

An optional Board Infinity add-on, separate from the Coursera course - a coach to learn, build, review and improve with while you work through it.

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

60-minute demo session · 100% refundable · no obligation to continue

Learn

Talk through concepts like CI/CD, containers and APIs until they click.

Build

Plan practice work to do alongside the course modules.

Review

Go over your approach and your questions with a coach.

Improve

Decide what to practise or learn next.

2,000+Industry coaches
16Domains covered
4.8/5Average coach rating
48,000+Sessions delivered
04 Syllabus

Four modules, from Git to deployment.

Version control, then automation, then serving, then deployment. 2 weeks at 10 hours a week, self-paced - every module lists its objectives, its content, and what you'll walk out with.

28 videos 12 readings 16 assignments ≈ 19 hours Download full syllabus
Learning objectives - by the end of this module you can:
  • Explain the DevOps mindset for ML and why it underpins reproducible ML engineering
  • Use Git workflows for ML projects, with Git LFS and DVC basics
  • Set up CI/CD basics with GitHub Actions and read workflow YAML structure
  • Test ML code and data, and manage environment variables and secrets in containers
What's inside
9 videos60 min
3 readings90 min
4 assignments150 min
Topics covered
DevOps foundations for ML Career scope in MLOps Git workflows Git LFS & DVC GitHub Actions Workflow YAML Testing code & data Multi-stage builds Secrets & env variables
You'll leave with: the DevOps foundations for ML - version control, CI/CD basics and automated tests for both code and data.
Learning objectives - by the end of this module you can:
  • Run ML APIs and databases together with Docker Compose, including networking between containers
  • Define pipeline strategies and stages for automating the ML lifecycle
  • Implement GitHub Actions for ML pipelines
  • Test and validate automated pipelines, with attention to test coverage and dependencies
What's inside
7 videos67 min
3 readings90 min
4 assignments150 min
Topics covered
Docker Compose ML APIs + databases Container networking Pipeline strategies GitHub Actions for ML Pipeline validation Test coverage ML test best practices
You'll leave with: an automated approach to the ML lifecycle - Compose-based services and GitHub Actions pipelines you can test and validate.
Learning objectives - by the end of this module you can:
  • Serve an ML model with FastAPI and connect models to APIs
  • Start the API and test endpoints with a manual test command
  • Test and document APIs
  • Use log-based debugging and set up multi-step workflows
What's inside
6 videos38 min
3 readings90 min
4 assignments150 min
Topics covered
FastAPI Model serving Connecting models to APIs REST APIs API testing API documentation Multi-step workflows Log-based debugging
You'll leave with: an ML model served as a tested, documented real-time API with FastAPI.
Learning objectives - by the end of this module you can:
  • Apply Docker fundamentals for ML, and build and run ML containers
  • Use Docker Compose commands to manage services
  • Deploy containers with CI/CD
  • Monitor and verify deployed services
What's inside
6 videos43 min
3 readings90 min
4 assignments150 min
Topics covered
Docker for ML Building ML containers Docker Compose commands Deploying with CI/CD Monitoring & verification Input validation Response time Retraining pipelines
You'll leave with: production-ready containerized ML services with automated deployment, monitoring and verification.
05 Tools & skills

The tools you work in, and the skills you leave with.

The hands-on stack named in the course FAQ. Coursera's official tools list is Restful API, Docker, Model Deployment, GitHub and Application Programming Interface (API).

Where code lives

Git & DVC

Git workflows for ML in module one, with Git LFS and DVC for versioning data alongside code.

What automates it

GitHub Actions & pytest

CI/CD basics, workflow YAML and testing ML code and data - then GitHub Actions for ML pipelines.

How it's served

FastAPI

Module three: model serving, connecting models to APIs, testing and documenting the endpoints.

How it ships

Docker & Compose

Multi-stage builds, Docker Compose, containers, and deploying them with CI/CD.

Skills you leave with

The fifteen skills Coursera lists for this course, grouped the way the work splits - automating it, checking it, and shipping it.

Automating it

Pipelines that run without you.

  • DevOps Tools
  • DevOps
  • Automation
  • Continuous Integration
  • CI/CD

Checking it

Versioned, tested and evaluated.

  • Version Control
  • API Testing
  • Data Validation
  • Model Training
  • Model Evaluation

Shipping it

Packaged and deployed.

  • Containerization
  • Continuous Deployment
  • Application Deployment
  • Cloud Deployment
  • MLOps
Certificate

Proof you can share.

Complete the course and Coursera issues a shareable certificate from Board Infinity - one you can add to your profile and your applications.

A course certificate, issued under Coursera's terms. It isn't an accreditation, and on its own it doesn't qualify you for an MLOps role.

On your LinkedIn profile

Coursera says the credential can be added to LinkedIn.

Anyone can verify it

Its own verification link, so a hiring manager confirms it in seconds.

Earned, not attended

Awarded on a graded quiz in each of the four modules - it certifies what you can do.

Two names on it

Board Infinity as the provider, Coursera as the platform.

06 Career impact

The skills between a model and production.

Framed around skills, not guarantees: this is a beginner foundation course, and completing it doesn't by itself qualify you as an MLOps engineer.

Where these skills apply

Aspiring MLOps engineers

The CI/CD, automation and container base the rest of the specialization builds on.

Data scientists

Version, test, serve and package the models you train, instead of handing over a notebook.

Developers

Git, pipelines, APIs and containers - familiar practice applied to ML apps.

CI/CD & automation

Pipelines that test, train and deploy on every change, built with GitHub Actions.

API development & serving

Real-time APIs with FastAPI, tested and documented.

Containerization & deployment

Docker images, Compose and deploying containers with CI/CD - then monitoring them.

The honest comparison

A trained model is only the beginning. Production needs an engineering workflow.

Notebooks are the right place to explore and train. What changes on the way to production is everything around the model.

What you get
This course
Notebook-only ML workflow
Version controlCode and data, together
Git and DVC from module one
Notebook files saved by hand
Automated testing & CI/CDNothing run by hand
pytest and GitHub Actions on every change
Re-running cells to check
APIsHow others use the model
A tested, documented FastAPI REST API
Predictions inside the notebook
Docker & deploymentSame environment everywhere
Multi-stage builds, deployed via CI/CD
Depends on one machine
MonitoringAfter it ships
Monitoring and verification
Not observed after training
07 The bigger path

Automate first. Then deploy, then scale.

This is Course 1 of the 3-course Machine Learning Operations (MLOps) Specialization - from DevOps foundations to cloud deployment and production model serving.

You're here Course 1

DevOps for Machine Learning: CI/CD, APIs & Deployment

Git, GitHub Actions, CI/CD, FastAPI, Docker, DVC and automated testing.

≈ 19 hours · 4 modules
Back to top
Course 2

Cloud Platforms for ML: AWS, Azure & GCP Deployment

AWS SageMaker, Azure ML, Google Cloud Vertex AI, serverless inference and managed ML services.

≈ 18 hours
View course
Course 3

Model Serving Systems: Containers, APIs & Scalability

Multi-model APIs, model versioning, A/B testing, batch and real-time inference, latency optimization.

≈ 19 hours
View course

Beginner level · 12 weeks at 5 hours a week · flexible schedule - see the full MLOps Specialization.

Know exactly what you're signing up for.

4 modules · 28 videos · 16 assignments · free PDF

08 FAQ

Common questions

Answered from the course's Coursera page. Coursera is the authority for current pricing, access and certificate terms.

About the course

Yes. Basic familiarity with Python and machine learning concepts - such as training a model and working with datasets - is recommended. Advanced ML theory is not required, because the course focuses on engineering and deployment workflows.

You work hands-on with Git, GitHub Actions, Docker, FastAPI, pytest and DVC, which the course FAQ describes as industry-standard tools used by MLOps and ML engineering teams. Coursera's official "Tools you'll learn" list for the course is Restful API, Docker (Software), Model Deployment, GitHub and Application Programming Interface (API).

Access, specialization & aid

To access the course materials and assignments, and to earn a Certificate, you purchase the Certificate experience when you enroll. You may be able to start with a Free Trial if you are eligible. Some courses also offer a "Full Course, No Certificate" option, which gives access to the materials and graded assessments but not a Certificate. The Coursera course page shows the options and pricing currently available.

According to Coursera, enrolling in the course gives you access to all courses in the Machine Learning Operations (MLOps) Specialization, and you earn a certificate when you complete the work. Your electronic Certificate is added to your Accomplishments page, where you can print it or add it to your LinkedIn profile.

Coursera states that in select learning programs you can apply for financial aid or a scholarship if you cannot afford the enrollment fee. Eligibility is not guaranteed; if aid is available for this program, a link to apply appears on the Coursera description page.

Want someone building this with you?

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.

See 1:1 coaching

Move your ML models from notebooks to production.

CI/CD with GitHub Actions, APIs with FastAPI, containers with Docker - and deployments you can reproduce.

Enroll on Coursera

DevOps for Machine Learning: CI/CD, APIs & Deployment · by Board Infinity · offered on Coursera

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