Build, test and serve machine learning like software.
Applied Machine Learning Systems with FastAPI for Developers - ML algorithms → data pipelines → tested ML code → FastAPI → Docker → deployed models.
Applied Machine Learning Systems with FastAPI for Developers
By Board Infinity · Offered on Coursera
- 40 video lessons · 17 assignments
- Course 1 of 3 - ML and Deep Learning for Software Engineers
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
- Recently updated - April 2026
ML as software
40 lessons from algorithms to FastAPI and Docker
Certificate included
Shareable on LinkedIn, your resume or CV
Assessed, not just watched
17 assignments across the four modules
Applied ML engineering, not ML theory alone.
Build → prepare → evaluate → structure → serve.
Applied Machine Learning Systems with FastAPI for Developers teaches software developers to implement, deploy and maintain machine learning systems using Python, scikit-learn, pandas, FastAPI and Docker.
It starts with ML algorithms and evaluation, moves into data preparation and feature engineering, then modular, tested ML code with pytest, logging and debugging.
It finishes by serving models as REST APIs with FastAPI, containerizing them with Docker and testing deployed inference. It is Course 1 of 3 in the Machine Learning and Deep Learning for Software Engineers Specialization.
You already know how to code. Now learn how ML fits inside real software.
An intermediate course - Coursera recommends prior experience. It is not a zero-programming introduction.
Moving into applied ML
Implement, evaluate and maintain ML models with the engineering habits you already use.
Models behind an API
Serve models as REST APIs with FastAPI and package them with Docker.
ML inside your product
Integrate trained models into the software systems you build and maintain.
Stronger engineering
Add modular code, testing, logging and debugging to your ML work.
Four capabilities, in the order they build.
Data → ML model → evaluation → modular code → FastAPI → Docker → deployed model. Each card carries one of Coursera's four official outcomes.
Implement ML algorithms
Implement core ML algorithms for classification, regression, and clustering tasks.
Build reliable data pipelines
Preprocess and engineer data pipelines for reliable model input.
Evaluate models properly
Evaluate and compare models using metrics, cross-validation, and testing.
Build reusable ML systems
Develop and modularize ML codebases for reuse and reproducibility.
Build ML systems with someone in your corner.
A dedicated Board Infinity coach alongside the course. Coaching is a separate, optional service - not part of the Coursera course or enrollment.
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
Get unstuck in minutes
The pipeline error you have stared at for an hour, talked through with someone who has seen it.
Your code read line by line
Not just whether the model trains - whether you would want to maintain the code.
Practice beyond the quizzes
Problems set for you, then reviewed, so the concepts turn into habits.
Aim it at a role
Which ML direction fits you - and what to learn next.
Four modules, from algorithms to APIs.
01 Learn ML → 02 Prepare data → 03 Build & test → 04 Serve & deploy. Two weeks at 10 hours a week, self-paced. Every lesson is listed exactly as titled on Coursera.
- Differentiate supervised and unsupervised ML paradigms.
- Clean and preprocess data using pandas and scikit-learn.
- Structure ML code for modularity and reuse.
- Expose ML models as REST APIs using FastAPI.
The tools you work with, and the skills you leave with.
Coursera's official "Tools you'll learn" list comes first. The technologies named in the course description are shown separately.
Model Deployment
Serving models as REST APIs and packaging them in containers, in module four.
Scikit Learn (Machine Learning Library)
Classification, regression, clustering, metrics and pipelines.
Application Programming Interface (API)
Endpoint design and input/output validation for model APIs.
Python Programming
The language the whole course is written in.
Used in the course - not part of Coursera's official tools list.
pandas
Cleaning and preprocessing data in module two.
FastAPI
Exposing ML models as REST APIs in module four.
Docker
Building images and testing containers for ML services.
pytest
Testing workflows for ML code in module three.
The sixteen skills Coursera lists for this course, grouped the way the work splits - building models, working with data, and engineering it.
Building models
Choosing and implementing the algorithm.
- Machine Learning
- Machine Learning Algorithms
- Machine Learning Methods
- Supervised Learning
- Unsupervised Learning
- Applied Machine Learning
Working with data
Getting reliable input into the model.
- Data Wrangling
- Data Preprocessing
- Data Processing
- Feature Engineering
- Model Evaluation
Engineering it
Code a second person can test, ship and maintain.
- Software Development
- Unit Testing
- Test Script Development
- Development Testing
- Containerization
A credential you can share.
Finish the graded work and Board Infinity issues a shareable certificate through Coursera.
It is a Coursera course certificate - not a university accreditation, government certification or professional licence, and not a guarantee of employer recognition.
On your LinkedIn profile
Coursera says the credential can be added to your LinkedIn profile.
On your resume or CV
Add it to your resume or CV alongside the work you have done.
Share it anywhere
Coursera also lists sharing it on social media and in your performance review.
Two names on it
Board Infinity as the provider, Coursera as the platform.
ML that ships - not just ML that trains.
The technical areas this course supports. These are capability areas and relevant directions - not guaranteed job outcomes.
Applied ML Engineering
Implement and evaluate ML algorithms inside software systems.
ML / AI Development
Build data pipelines and reusable ML components.
Backend + ML Integration
Serve models through REST APIs using FastAPI.
MLOps Foundations
Testing, modular code, Docker and deployment practices as a foundation for production ML workflows.
Software Engineering for ML
Modularity, testing, logging and debugging practices for ML codebases.
Model evaluation
Metrics, cross-validation and comparing models before they reach production.
ML theory is everywhere. So what is this?
Free ML resources are a good way to learn the ideas. What this course adds is ML taught as software - in order, with testing, and through to a served model.
Applied ML foundations → deep learning → transformers.
This is Course 1 of 3 in the Machine Learning and Deep Learning for Software Engineers Specialization. Course hours below are as listed on the specialization page.
Applied Machine Learning Systems with FastAPI for Developers
ML algorithms, data pipelines, tested ML code, FastAPI and Docker.
View course Course 2 · 15 hoursDeep Learning: Train Neural Networks and Deploy with Docker
Feed-forward networks in PyTorch and TensorFlow, TensorBoard and Weights & Biases, FastAPI, Docker and scaling.
View course Course 3 · 18 hoursTransformers and NLP: Fine-Tuning Models with Hugging Face
Transformer fine-tuning with Hugging Face, DVC and Git, FastAPI latency and throughput, and model evaluation.
View courseSee the full Machine Learning and Deep Learning for Software Engineers Specialization on Coursera.
Know exactly what you're signing up for.
Common questions
Answers are grounded in the Coursera course page.
Coursera lists the course at Intermediate level with recommended experience, and describes it as teaching software developers. It works in Python throughout, so it is not a zero-programming introduction.
Coursera's official "Tools you'll learn" are Model Deployment, Scikit Learn (Machine Learning Library), Application Programming Interface (API) and Python Programming. The course description also covers pandas, FastAPI, Docker and pytest.
Yes - the course description says it teaches software developers how to implement, deploy and maintain machine learning systems, and the specialization is designed for software engineers, backend developers and full-stack professionals.
Yes. It is hosted on Coursera with a flexible schedule, so you learn at your own pace. Coursera estimates 2 weeks at 10 hours a week.
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.
Want someone building this with you?
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.
Ready to build ML systems that behave like real software?
Learn → Test → Serve → Deploy.