Deploying Deep Learning: Quantization, Serving, and Edge AI
Take a trained model from compression and INT4/INT8 quantization to high-throughput serving with vLLM and Triton, then CPU and edge deployment with ONNX Runtime and Llama.cpp.
Deploying Deep Learning: Quantization, Serving, and Edge AI
By Board Infinity · Offered on Coursera
- 42 video lessons · 16 assignments
- Course 3 of 3 - Advanced Deep Learning Architectures
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
- Recently updated - May 2026
Model compression & quantization
INT8/INT4 with AWQ and GPTQ
High-throughput serving
vLLM, PagedAttention and Triton
CPU & edge deployment
ONNX Runtime, GGUF and Llama.cpp
Production-ready API capstone
Quantize, serve, benchmark, Docker
Training a model is half the job. This course is the other half.
Compress → serve → run on CPU and edge → build, benchmark and containerize an API.
Deploying Deep Learning: Quantization, Serving, and Edge AI covers the deployment lifecycle for ML engineers and developers - starting with why trained models need inference optimization, and how pruning, distillation and INT8/INT4 quantization with AWQ and GPTQ trade accuracy against latency.
It then moves to high-throughput serving with vLLM's PagedAttention and NVIDIA Triton, scaling inference across GPUs, and on to CPU and edge deployment with ONNX Runtime, GGUF and Llama.cpp, plus multimodal inference with CLIP and LLaVA.
The capstone quantizes a fine-tuned model, builds a production API with vLLM, benchmarks it and containerizes it with Docker for cloud and edge deployment. Coursera lists it as an advanced course.
For engineers taking models from training to production.
A working knowledge of deep learning, PyTorch and LLM fundamentals is recommended. This is an advanced course - Coursera says learners should already understand model training, transformers and basic MLOps concepts.
Training to inference
Moving from model training to inference deployment.
Optimization & quantization
Interested in making models smaller and faster with INT8/INT4 methods.
LLMs at scale
Exploring scalable LLM serving with vLLM and NVIDIA Triton.
CPU, edge and production APIs
Working with CPU and edge inference, and production API workflows.
Four capabilities, in the order they build.
Each card carries one of Coursera's four official outcomes for the course.
Compress models with INT4/INT8 quantization
Apply INT4/INT8 quantization (AWQ, GPTQ, GGUF) to compress LLMs and vision models for production
Serve models with vLLM and Triton
Deploy high-throughput inference servers using vLLM's PagedAttention and NVIDIA Triton
Run models with ONNX Runtime and Llama.cpp
Run optimized LLMs on CPU and edge devices using ONNX Runtime and Llama.cpp
Build, benchmark and containerize an inference API
Build, benchmark, and containerize a production-ready inference API with Docker
Ship your first deployment with someone in your corner.
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course. It 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 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 it runs - whether you would want to maintain it.
Practice beyond the quizzes
Problems set for you, then reviewed, so the concepts turn into habits.
Aim it at a role
Which direction fits you - and what to learn next.
Four modules, from compression to the edge.
Two weeks at 10 hours a week, self-paced. Every lesson is listed exactly as titled on Coursera, including the duplicate assignment title in module two.
- Learn model compression fundamentals, memory profiling, and modern INT8/INT4 quantization techniques including AWQ and GPTQ to optimize models for production inference.
- Master production-grade serving engines including vLLM with PagedAttention and NVIDIA Triton for scaling inference across GPUs and nodes.
- Export models to ONNX for interoperability, deploy LLMs on CPU and edge devices with Llama.cpp and GGUF, and build multimodal pipelines with CLIP and LLaVA.
- Apply all course concepts in a final project to quantize a fine-tuned model, serve it via vLLM, benchmark it, and package it for cloud and edge deployment.
The tools you work with, and the skills you leave with.
Coursera's official tools and skills come first. The broader technology stack covered in the lessons is shown separately below.
Tools you'll learn
The two official tools for this course.
- Docker (Software)
- Model Deployment
Optimizing models
Skills - making models smaller and better understood.
- Model Optimization
- Memory Management
- Model Evaluation
- Fine-tuning
- LLM Application
- Large Language Modeling
Shipping them
Skills - getting models served and running.
- Scalability
- Application Deployment
- Cloud Deployment
- Containerization
- API Design
- MLOps (Machine Learning Operations)
Named in the course description and lesson titles - not Coursera's official tools list.
INT4/INT8, AWQ & GPTQ
Lower-bit quantization methods for LLMs and vision models.
vLLM & PagedAttention
High-throughput LLM serving and the KV cache.
NVIDIA Triton
Production serving patterns, including vision models.
ONNX & ONNX Runtime
Exporting LLMs and vision models, then speeding up inference.
GGUF & Llama.cpp
Running LLMs locally on CPU and edge devices.
CLIP & LLaVA
Connecting text and images in a simple multimodal pipeline.
LoRA, QLoRA & PEFT
Loading a fine-tuned model in the capstone.
Latency, throughput & memory
Measuring the trade-offs of every optimization.
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 degree, university credit or a regulated professional qualification.
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.
Inference is where models meet real users.
Coursera's FAQ names the roles below. They are career directions the course's skills relate to - not guaranteed job outcomes.
Inference Engineer
Quantization, serving engines and benchmarking latency, throughput and memory.
ML Deployment Engineer
Taking a fine-tuned model through quantization, a vLLM API and Docker packaging.
Edge AI Developer
ONNX Runtime, GGUF and Llama.cpp on CPU and edge devices, plus CLIP and LLaVA.
MLOps Engineer focused on generative AI systems
Serving at scale, containerization and deployment patterns for LLMs.
Deployment guides are everywhere. So what is this?
An advanced, production-focused course - not a beginner's introduction to AI, and not a complete replacement for hands-on infrastructure experience. What it adds is the deployment path in one sequence.
Advanced architectures → fine-tuning → deployment.
This is Course 3 of 3 - the final course in the Advanced Deep Learning Architectures Specialization. Course hours below are as listed on the specialization page.
Deep Learning: Advanced Backbones and Efficient GPU Training
ConvNeXt and Vision Transformers, RMSNorm, SwiGLU and RoPE, mixed precision and distributed training, CNN vs ViT benchmarking.
View course Course 2 · 19 hoursGenerative AI: Fine-Tuning LLMs and Diffusion Models
Decoder-only transformers and KV caching, LoRA and QLoRA, diffusion models with ControlNet, domain-specialized LLM adapters.
View course Course 3 · You are here · 21 hoursDeploying Deep Learning: Quantization, Serving, and Edge AI
Quantization, high-throughput serving, CPU and edge deployment, and a benchmarked, containerized API.
View courseSee the full Advanced Deep Learning Architectures Specialization on Coursera.
Know exactly what you're signing up for.
Common questions
Answers follow Coursera's own FAQ for this course.
Yes, a working knowledge of deep learning, PyTorch, and LLM fundamentals is recommended. This course focuses on production deployment rather than training from scratch.
No. This is an advanced course. Learners should already understand model training, transformers, and basic MLOps concepts before starting.
Coursera says it prepares you for roles like Inference Engineer, ML Deployment Engineer, Edge AI Developer, and MLOps Engineer focused on generative AI systems. These are career directions, not employment guarantees.
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.
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