Transformers and NLP: Fine-Tuning Models with Hugging Face
Fine-tune transformers. Build NLP systems. Deploy them. From transformer architecture to fine-tuning, evaluation, reproducibility and FastAPI inference in production.
Transformers and NLP: Fine-Tuning Models with Hugging Face
By Board Infinity · Offered on Coursera
- 4 modules · 39 videos · 16 assignments
- Open-source stack - Hugging Face, DVC, Git, FastAPI
- Shareable certificate on Coursera
- Course 3 of 3 - final course of the specialization
Hands-on with Hugging Face
Fine-tune, evaluate and publish transformer models
Shareable certificate
LinkedIn, resume or CV, social media and performance reviews
Graded, not just watched
16 assignments - four in every module
Beyond transformer theory - all the way to production.
Understand → fine-tune → evaluate → version → serve → optimize. One advanced course that takes a transformer model from architecture to a deployed inference API.
Transformers and NLP starts with the architecture: self-attention, positional encoding, embeddings and encoder–decoder designs, across model families such as BERT, RoBERTa, GPT, LLaMA, T5 and BART.
Then you fine-tune pre-trained models with Hugging Face Transformers and Datasets - tokenization, training and evaluation with F1, precision, accuracy and confusion matrices - and make the work reproducible with DVC, Git and the Hugging Face Hub.
It finishes in production: FastAPI inference APIs, batch and asynchronous calls, latency and throughput, quantization and distillation, scaling, drift detection and CI/CD.
For engineers who know deep learning and want transformers in production.
No prior transformer experience is needed, but you should have a solid understanding of deep learning and NLP fundamentals. Familiarity with Python and basic neural network concepts will help.
Applying pretrained models
Fine-tuning transformers for NLP classification and measuring the results properly.
Shipping ML in products
Integrating transformer models into production software behind an API.
From notebook to pipeline
Making training and evaluation reproducible with DVC, Git and model versions.
Serving at scale
Deploying and optimizing transformer inference with FastAPI.
Four capabilities, from fine-tuning to serving.
The four learning outcomes Coursera lists for this course.
Fine-tune transformers
Fine-tune pre-trained transformer models for NLP classification tasks using Hugging Face
Build reproducible pipelines
Build reproducible ML pipelines with DVC and Git for experiment tracking and version control
Deploy inference APIs
Deploy transformer inference APIs using FastAPI with optimized latency and throughput
Evaluate model performance
Evaluate and visualize model performance using standardized metrics and confusion matrices
Ship your first transformer API with someone in your corner.
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course - a separate, optional add-on to your Coursera 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 faster
Talk through the training run or deployment step that keeps failing.
A second pair of eyes
Review your pipeline and code with someone who has shipped models.
A roadmap you keep
Structure what to learn next around your goals.
Aim it at a role
Discuss which NLP or ML path fits you, and what to build next.
Four modules, from attention to production APIs.
Understand, fine-tune, reproduce, deploy. Two weeks at 10 hours a week, fully self-paced - every lesson exactly as titled on Coursera.
The stack you work in, and the skills you leave with.
All tools are open-source and freely available. Official tools and skills are exactly as Coursera lists them.
Hugging Face
Transformers and Datasets for fine-tuning, plus the Hub for publishing models and model cards.
Git (Version Control System)
Tracking code, models and metrics alongside DVC.
Model Deployment
FastAPI inference endpoints, optimized and tested for production readiness.
DVC
Reproducible ML pipelines and version control for data, models, metrics and artifacts.
Libraries named in the course FAQ: Hugging Face Transformers · Hugging Face Datasets · Evaluate · DVC · Git · FastAPI. Only Git (Version Control System), Model Deployment and Hugging Face are Coursera's official “Tools you'll learn”.
The fourteen skills Coursera lists for this course, grouped the way the work splits - understanding the models, training and evaluating them, and running them in production.
Models & NLP
What transformers are and what they can do.
- Natural Language Processing
- Generative Model Architectures
- Large Language Modeling
- LLM Application
- Transfer Learning
Training & evaluation
Getting a model that works, and proving it.
- Fine-tuning
- Model Training
- Model Evaluation
- Data Preprocessing
- Data Pipelines
Production & MLOps
Keeping it fast, versioned and reliable.
- MLOps (Machine Learning Operations)
- Model Optimization
- Performance Tuning
- Version Control
Proof you can link to.
The course offers a shareable certificate on Coursera, with Board Infinity as the provider.
Not a university accreditation, government certification or professional license, and not a guarantee of employer recognition.
Add it to LinkedIn
Put it on your LinkedIn profile.
On your resume or CV
List it alongside your experience.
Share it anywhere
Share it on social media and in your performance review.
Earned, not attended
Backed by 16 assignments across the four modules.
Transformers are the core of modern NLP - and production is where they count.
The course covers transformer architectures, fine-tuning, evaluation frameworks and deployment practices that align with NLP engineering work. These are directions, not guaranteed outcomes.
NLP Engineering
Work with transformer architectures, pretrained models, tokenization, fine-tuning and evaluation.
Transformer / LLM Application Development
Apply pretrained transformer models to NLP classification and related tasks.
ML Engineering
Build reproducible pipelines with DVC and Git and manage model versions and artifacts.
ML / AI Deployment
Serve transformer models through FastAPI and optimize inference performance.
MLOps Foundations
Apply version control, testing, monitoring, scaling and CI/CD practices to transformer-based systems.
ML in Production Software
The specialization's focus: integrating, deploying and maintaining ML models inside production software.
Transformer theory is everywhere. So what is this?
There are excellent free explanations of attention, BERT and fine-tuning. What this course adds is the connection between them - from a fine-tuned model to a versioned, evaluated, deployed NLP system.
The final course of a three-course path.
Machine Learning and Deep Learning for Software Engineers: deploy machine learning in production software. Applied ML → Deep Learning → Transformers & NLP.
Applied Machine Learning Systems with FastAPI for Developers
Applied ML: the first course of the specialization.
View the specialization Course 2 · 15 hoursDeep Learning: Train Neural Networks and Deploy with Docker
Deep learning: the second course of the specialization.
View the specialization Course 3 · You are here · 18 hoursTransformers and NLP: Fine-Tuning Models with Hugging Face
Transformers and NLP: the final course, from fine-tuning to production.
Enroll on CourseraHours shown are the specialization page's listing. Board Infinity runs 270+ courses and 57 specializations on Coursera - browse the full catalogue.
Know exactly what you're signing up for.
Common questions
Answered from the Coursera course page.
No prior transformer experience is needed, but you should have a solid understanding of deep learning and NLP fundamentals. Familiarity with Python and basic neural network concepts will help you succeed.
You'll work with Hugging Face Transformers, Hugging Face Datasets, Evaluate library, DVC for version control, Git, and FastAPI for deployment. All tools are open-source and freely available.
Yes. Module 4 focuses entirely on building inference APIs with FastAPI, optimizing model performance, and integrating CI/CD and monitoring for production-ready deployment.
This course covers core skills that NLP engineers need — transformer architectures, model fine-tuning, evaluation frameworks, and deployment practices used in industry.
The course page lists 2 weeks to complete at 10 hours a week, on a flexible schedule. The specialization page lists this course at 18 hours.
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 take transformers from fine-tuning to production?
Fine-tune → Evaluate → Version → Deploy
Enroll on Coursera