Train neural networks. Serve them. Scale them.
Deep Learning: Train Neural Networks and Deploy with Docker - PyTorch and TensorFlow, experiment tracking, FastAPI serving and Docker, from first network to production.
Deep Learning: Train Neural Networks and Deploy with Docker
By Board Infinity · Offered on Coursera · Updated April 2026
- 4 modules · 15 assignments
- PyTorch & TensorFlow to FastAPI and Docker
- Shareable certificate on completion
- Flexible schedule - 2 weeks at 10 hours a week
Training to production
Train, track, serve and containerize models
Certificate included
Shareable on LinkedIn, your resume or CV
Graded, not just watched
15 assignments across the four modules
Deep learning, engineered for production.
A practical deep-learning engineering course: design, train and deploy neural networks with PyTorch, TensorFlow, FastAPI and Docker.
Start with neural-network fundamentals - feed-forward networks, activations, loss functions and optimization - and implement them in PyTorch and TensorFlow.
Then train and validate, track experiments with TensorBoard and Weights & Biases, serialize the model and serve it with FastAPI - testing inference, latency and throughput - before packaging it with Docker and Docker Compose, with monitoring, logging, scaling and model-version management.
For developers who want models that actually ship.
Basic Python and machine learning concepts are recommended. Prior deep-learning experience is not required.
Python, meet deep learning
Developers with Python experience adding neural networks to what they build.
From classic ML to deep learning
Learners with basic ML knowledge who want practical deep-learning deployment skills.
Interested in serving models
Developers who want trained models behind an API and inside a container.
Building ML-powered apps
Engineers wiring deep learning into applications and services.
Four capabilities, training to scale.
The course's four stated outcomes, each with the workflow it covers. Diagrams are conceptual.
Build neural networks
Build and train feed-forward neural networks using PyTorch and TensorFlow frameworks.
Track experiments
Track experiments and visualize model metrics using TensorBoard and Weights & Biases.
Serve deep-learning models
Deploy trained deep learning models as production REST APIs using FastAPI.
Containerize & scale
Containerize and scale deep learning applications using Docker for production environments.
Learn deep learning with someone in your corner.
An optional Board Infinity add-on, separate from the Coursera course - a coach to talk your training runs, APIs and containers through with.
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 optimizers, overfitting and inference 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.
Four modules, from training to production.
Foundations, then train and track, then serve, then production. Open a module for its objectives and every lesson with its duration.
Covers the foundational concepts of neural networks including architecture, activations, losses, optimizers, and implementation in PyTorch.
- Explain neural-network architecture, activations and loss functions
- Apply optimization concepts such as SGD and Adam
- Work with PyTorch tensors and modules and write training loops with gradients
- Tune learning rate and batch size, and use regularization, dropout, early stopping and checkpoints
Focuses on implementing robust training and validation loops, tracking experiments using TensorBoard or Weights & Biases, and analyzing checkpoints for insights.
- Split data into train/validate/test sets and build training loops from scratch
- Evaluate models with metrics such as accuracy, loss and AUC, using validation splits and K-fold
- Recognise and handle overfitting
- Track and compare runs and hyperparameters with TensorBoard and Weights & Biases
Covers packaging trained deep learning models for API inference, deploying models via FastAPI, and testing and measuring inference performance.
- Export and serialize trained models, with pre- and post-processing
- Design batch inference and create REST endpoints that integrate PyTorch models
- Test endpoints with cURL and Postman
- Measure latency and throughput, profile inference and optimize with TorchScript or ONNX
Covers containerizing deep learning APIs with Docker, integrating logging, error handling, and configuration, and deploying and scaling DL services in production.
Course page description: Duration: 4 hours. The module header shows 3 hours.
- Build Docker images and containers and automate with Docker Compose
- Add runtime logging, error tracking and alerts
- Collect performance metrics and scale APIs with containers
- Manage model versions, dependencies and environments
Module durations are shown as the course page displays them. Module 4's header shows 3 hours while its description states 4 hours; both are shown above.
The tools you work in, and the skills you leave with.
The framework stack named in the course FAQ. Logos identify the tools only - none of these providers endorses the course.
PyTorch
Tensors and modules, training loops and gradients - then integrating PyTorch models behind an API.
TensorFlow & TensorBoard
Building feed-forward networks, and TensorBoard setup for visualizing metrics.
FastAPI
REST endpoints for inference, tested with cURL and Postman, measured for latency and throughput.
Docker & Compose
Building images and containers, automating with Docker Compose and scaling APIs with containers.
The eleven skills Coursera lists for this course, exactly as written - grouped by where they show up: building, evaluating and shipping.
Building networks
The models themselves.
- Deep Learning
- Artificial Neural Networks
- Network Architecture
- Model Training
Measuring them
Knowing whether they work.
- Model Evaluation
- Performance Testing
- MLOps (Machine Learning Operations)
Shipping them
Running in production.
- Application Deployment
- Containerization
- Configuration Management
- Scalability
Proof you can share.
Complete the course and Coursera issues a shareable certificate from Board Infinity.
A course certificate issued under Coursera's terms - not a university accreditation, government certification or professional licence.
On your LinkedIn profile
Add the credential straight to LinkedIn.
On your resume or CV
List it next to the skills it covers.
On social media
Share it when you finish.
In a performance review
Show the engineering skills you've added.
Where deep learning meets software engineering.
Career directions the course's skills are relevant to - not guaranteed outcomes. The course FAQ describes it as suitable if you're transitioning toward a deep-learning engineering role.
Deep Learning Engineering
Build and train feed-forward neural networks using modern deep-learning frameworks.
Model Deployment
Serve trained models through REST APIs.
ML Platform / MLOps Foundations
Track experiments, manage model versions, monitor performance and work with containerized services.
Production ML Development
Apply Docker, logging, error tracking, configuration management and scaling strategies.
AI / ML Software Engineering
Bridge deep-learning development with software engineering and deployment practices.
Moving toward DL engineering
The full pipeline in one course - from building networks to production deployment. A direction, not a guarantee.
Deep-learning theory alone, or deep-learning engineering?
Plenty of good deep-learning material exists, much of it free. What it rarely does is connect training to serving to containers in one path - that connection is what this course is built around.
Applied ML, deep learning, then transformers.
This is Course 2 of 3 in the Machine Learning and Deep Learning for Software Engineers Specialization - built to deploy machine learning in production software.
Applied Machine Learning Systems with FastAPI for Developers
The first course in the specialization. Specialization listing: 21 hours.
View course Course 2 · You are hereDeep Learning: Train Neural Networks and Deploy with Docker
Neural networks in PyTorch and TensorFlow, experiment tracking, FastAPI and Docker. Specialization listing: 15 hours.
View course Course 3 · Transformers & NLPTransformers and NLP: Fine-Tuning Models with Hugging Face
Fine-tune pretrained transformer models for NLP classification with Hugging Face, with DVC, Git, FastAPI, latency and throughput optimization and model evaluation. Specialization listing: 18 hours.
View courseDesigned for software engineers, backend developers and full-stack professionals - see the full specialization.
Know exactly what you're signing up for.
Common questions
Answered from the course's Coursera page. Coursera is the authority for current pricing, access and certificate terms.
About the course
Basic knowledge of Python and machine learning concepts is recommended. Prior deep-learning experience is not required, although familiarity with data-science fundamentals can help learners progress faster.
You'll work with PyTorch, TensorFlow, FastAPI, Docker, TensorBoard, and Weights & Biases.
The course FAQ says 4 weeks at 3-5 hours per week, totaling approximately 16 hours of content including videos, readings, quizzes, and hands-on activities. The course overview on the same page lists 2 weeks at 10 hours a week.
According to the course FAQ, yes: the course covers the full pipeline from building neural networks to production deployment. It builds relevant skills; it does not guarantee a role.
Access, specialization & aid
To access course materials, assignments, and earn a Certificate, you need to purchase the Certificate experience when you enroll. Eligible learners may have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option.
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.
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.
Want someone building this with you?
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.
Ready to take deep learning from training to production?
Train → Track → Serve → Scale.