Model Serving & Deployment: Pro Course — Ship LLMs to Production with Confidence
Master AI model serving & deployment — vLLM, TGI, TensorRT, Triton, Docker, Kubernetes & cloud deployment. Build production model APIs at aimodels.in.
Course Snapshot
| Course Code | AIM-612 |
| Duration | 50+ hours |
| Modules | 11 |
| Projects | 5 |
| Phase | 6 — Inference, Enterprise & Deployment |
| Skill Level | Intermediate to Advanced |
| Format | Self-paced + live cohorts |
| Price | ₹5,999 (early bird ₹4,499) |
| Last Updated | July 2026 |
Course Overview
A model that works in a notebook is not a product. A model that works in a notebook is a prototype. The journey from a working model to a production-grade API that serves thousands of requests per second, scales elastically, monitors itself, and recovers from failure is what this course is about. This is a deep, hands-on dive into every layer of the model serving and deployment stack for LLMs and embedding models. You will start by building model APIs from scratch with FastAPI, learning request handling, streaming, batching, and async patterns. You will containerize models with Docker, creating reproducible deployment artifacts that run anywhere. You will deploy vLLM for high-throughput LLM serving, mastering PagedAttention, continuous batching, and quantized serving. You will work with Hugging Face Text Generation Inference (TGI) as an alternative serving engine and understand when to choose it. You will deploy models to Kubernetes, managing GPU resources, auto-scaling, and rolling updates. You will deploy to cloud platforms including AWS, GCP, and Azure, leveraging their managed AI services and raw GPU instances. You will serve embedding models for RAG pipelines with optimized batch inference. You will implement monitoring and observability with Prometheus, Grafana, and Langfuse, tracking latency, throughput, quality, and cost. You will build production reliability with health checks, circuit breakers, graceful degradation, and load testing. By the end you will have five portfolio projects, including a multi-model serving platform on Kubernetes and a cloud-deployed LLM API with full monitoring. Whether you serve internal models or ship AI APIs to customers, this course gives you the complete deployment toolkit to go from prototype to production.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
ML Engineers & MLOps Engineers
Engineers who can train and fine-tune models but need the deployment skills to serve them reliably in production at scale.
Backend Engineers Building AI APIs
Experienced backend engineers who need to serve ML models behind production APIs with proper streaming, batching, and reliability patterns.
DevOps Engineers Supporting AI
DevOps and platform engineers who manage infrastructure for AI workloads and need to understand GPU scheduling, Kubernetes, and cloud deployment for models.
Startup Founders & CTOs
Technical founders who need to deploy LLMs and embedding models to production without a dedicated ML infrastructure team.
This Course Is NOT For You If:
- Beginners who have never built an API or used Docker — start with our Hugging Face or LLMs Pro courses first.
- Those looking for model training or fine-tuning content — this course focuses on serving and deployment, not training.
- Anyone without basic Python and command-line skills — every module involves hands-on deployment exercises.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to build model serving APIs with FastAPI including streaming, batching, and async patterns
- 2 You will be able to containerize ML models with Docker and create reproducible deployment artifacts
- 3 You will be able to deploy and configure vLLM for high-throughput LLM serving with PagedAttention and continuous batching
- 4 You will be able to deploy and compare Hugging Face TGI as an alternative LLM serving engine
- 5 You will be able to deploy models to Kubernetes with GPU scheduling, auto-scaling, and rolling updates
- 6 You will be able to deploy models to cloud platforms including AWS, GCP, and Azure
- 7 You will be able to serve embedding models for RAG pipelines with optimized batch inference
- 8 You will be able to implement monitoring and observability with Prometheus, Grafana, and Langfuse
- 9 You will be able to build production reliability with health checks, circuit breakers, and load testing
- 10 You will be able to scale model serving horizontally with load balancing and auto-scaling policies
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
11 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Model Serving Fundamentals
4 hours- Understand the gap between a working model in a notebook and a production API
- Map the model serving stack: API layer, serving engine, model runtime, and infrastructure
- Compare serving approaches: REST APIs, gRPC, streaming, and batch endpoints
- Identify production requirements: latency, throughput, reliability, and observability
- Design synchronous vs asynchronous serving patterns for different workloads
- Understand batch vs real-time serving and when to use each
- Implement streaming responses with Server-Sent Events and WebSocket
- Design microservices for model serving: API gateway, model server, and cache
- Calculate GPU memory requirements from model size, batch size, and sequence length
- Plan for KV cache memory and its impact on concurrent request capacity
- Estimate throughput from GPU specs, model size, and batching configuration
- Design capacity planning for peak vs average load
MODULE 2 — FastAPI for Model Serving
5 hours- Create FastAPI endpoints for model inference with request and response models
- Implement input validation, error handling, and HTTP status codes
- Use Pydantic models for type-safe request and response schemas
- Add authentication with API keys and JWT tokens
- Implement Server-Sent Events (SSE) for streaming token generation
- Use async/await for non-blocking model inference and high concurrency
- Handle backpressure and connection management for streaming endpoints
- Build WebSocket endpoints for real-time bidirectional communication
- Implement request batching at the API layer for throughput optimization
- Use Redis and Celery for async task queues and background processing
- Design priority queues and fair scheduling for multi-tenant workloads
- Handle timeouts, retries, and dead letter queues for failed requests
MODULE 3 — Docker for AI Deployment
5 hours- Write Dockerfiles for Python ML models with CUDA and GPU support
- Optimize Docker images with multi-stage builds and layer caching
- Manage model weights: bake-in vs volume mounts vs model registry
- Build minimal images for production with distroless and slim bases
- Configure the NVIDIA Container Toolkit for GPU access in Docker
- Use Docker Compose for multi-container model serving stacks
- Manage GPU memory allocation and multi-GPU containers
- Benchmark containerized vs bare-metal model inference overhead
- Push and pull images from registries: Docker Hub, ECR, and GCR
- Implement image scanning for security vulnerabilities
- Use Docker health checks and restart policies for reliability
- Tag and version images for reproducible deployments and rollback
MODULE 4 — vLLM Production Deployment
5 hours- Deploy the vLLM OpenAI-compatible API server with Docker
- Configure tensor parallelism and pipeline parallelism for multi-GPU serving
- Set up quantized serving with AWQ, GPTQ, and bitsandbytes models
- Enable and tune prefix caching, chunked prefill, and speculative decoding
- Deploy vLLM behind Nginx or a load balancer with SSL termination
- Implement health checks, readiness probes, and graceful shutdown
- Configure rate limiting, authentication, and API key management
- Handle model loading, warm-up, and cold-start optimization
- Deploy multiple models on shared GPUs with vLLM
- Implement model swapping and LRU eviction for memory management
- Design a model router for directing requests to the right model server
- Benchmark multi-model serving overhead and resource utilization
MODULE 5 — Text Generation Inference TGI
4 hours- Understand TGI architecture: router, shards, and continuous batching
- Deploy TGI with Docker for local and production serving
- Configure TGI for quantized models with bitsandbytes and GPTQ
- Benchmark TGI vs vLLM on latency, throughput, and memory
- Use TGI's message format and chat template support
- Configure watermarking, grammar constraints, and structured generation
- Enable TGI's built-in Prometheus metrics endpoint for monitoring
- Implement TGI with flash attention and PagedAttention equivalents
- Compare vLLM and TGI on features, performance, and ecosystem support
- Evaluate when TGI is better: Hugging Face integration, message format, structured output
- Evaluate when vLLM is better: raw throughput, speculative decoding, prefix caching
- Design a serving strategy that can switch between engines based on workload
MODULE 6 — Kubernetes for AI Workloads
6 hours- Understand Kubernetes concepts: pods, deployments, services, and ingress
- Configure GPU resources with NVIDIA device plugin and time-slicing
- Create Kubernetes manifests for model serving deployments
- Use Helm charts for packaging and deploying model serving stacks
- Schedule GPU workloads with resource requests and limits
- Use node selectors, affinity, and taints for GPU node targeting
- Implement GPU sharing with time-slicing and Multi-Instance GPU (MIG)
- Manage GPU memory fragmentation and pod eviction policies
- Configure Horizontal Pod Autoscaler (HPA) based on GPU and custom metrics
- Implement vertical pod autoscaling for memory-optimized configurations
- Perform rolling updates and rollbacks for zero-downtime model deployments
- Use Kubernetes operators for ML workflows with Kubeflow and KServe
MODULE 7 — Cloud Deployment
5 hours- Deploy models on AWS EC2 with GPU instances (G5, G6, P4, P5)
- Use Amazon EKS for Kubernetes-based model serving
- Leverage AWS SageMaker endpoints for managed model deployment
- Configure AWS Bedrock for managed LLM serving and comparison
- Deploy on GCP GKE with GPU node pools and auto-scaling
- Use Vertex AI endpoints for managed model serving on GCP
- Deploy on Azure AKS with GPU VMs and spot instances
- Leverage Azure ML for managed model deployment and endpoints
- Deploy models on serverless GPU platforms: Modal, Replicate, and RunPod
- Use spot instances and pre-emptible VMs for cost optimization
- Implement cold-start optimization for serverless model serving
- Compare cost-per-token across cloud providers and deployment strategies
MODULE 8 — Embedding Model Serving
4 hours- Deploy embedding models with FastAPI and Hugging Face Transformers
- Implement batch encoding for high-throughput embedding generation
- Use Infinity and TEI (Text Embeddings Inference) for optimized serving
- Benchmark embedding throughput: single requests vs batch vs async
- Connect embedding servers to vector databases: pgvector, Qdrant, and Pinecone
- Implement hybrid retrieval combining dense embeddings and BM25
- Design embedding pipelines for real-time and batch document indexing
- Optimize embedding dimensionality and storage for cost and speed
- Serve CLIP and vision-language models for image and text embeddings
- Implement image preprocessing and batching for vision embedding pipelines
- Build a unified embedding API for text, image, and multi-modal inputs
- Benchmark multi-modal embedding serving on GPU and CPU
MODULE 9 — Monitoring & Observability
5 hours- Instrument model serving with Prometheus metrics: latency, throughput, errors
- Build Grafana dashboards for model serving health and performance
- Track GPU utilization, memory, and temperature metrics
- Implement custom business metrics: tokens per second, cost per request
- Deploy Langfuse for LLM-specific tracing and observability
- Trace full request lifecycles: prompt, model, tokens, latency, and cost
- Implement quality monitoring with user feedback and LLM-as-judge
- Set up alerts for quality drift, latency spikes, and cost anomalies
- Implement structured logging for model serving with JSON logs
- Use OpenTelemetry for distributed tracing across microservices
- Aggregate logs with ELK stack or Loki for search and analysis
- Design audit logs for compliance and debugging in production
MODULE 10 — Production Reliability & Scaling
5 hours- Implement health checks: liveness, readiness, and startup probes
- Design circuit breakers and bulkheads for failure isolation
- Implement graceful degradation and fallback for model serving
- Use retry, timeout, and backpressure patterns for resilience
- Load test model serving with Locust, k6, and wrk
- Identify bottlenecks: GPU, memory, network, and API layer
- Plan capacity for peak load with headroom and auto-scaling policies
- Conduct chaos engineering and failure injection for reliability testing
- Scale horizontally with load balancers and multiple model replicas
- Implement request routing and affinity for stateful model serving
- Use GPU auto-scaling with custom metrics and predictive scaling
- Design multi-region deployment for latency and disaster recovery
MODULE 11 — Projects
4 hours- Build a FastAPI server for an open-source LLM with streaming and batching
- Add authentication, rate limiting, and error handling
- Deploy with Docker and benchmark latency and throughput
- Document the API with OpenAPI and provide client examples
- Deploy vLLM with Docker for a 7B model with quantization
- Configure continuous batching, prefix caching, and tensor parallelism
- Benchmark against a baseline and document the results
- Set up health checks and graceful shutdown
- Deploy a multi-model serving platform on Kubernetes
- Configure GPU scheduling, auto-scaling, and rolling updates
- Implement a model router for directing requests to the right model
- Set up Prometheus and Grafana monitoring dashboards
- Deploy an LLM API to AWS, GCP, or Azure with auto-scaling
- Implement Langfuse observability for tracing and quality monitoring
- Configure load balancing, SSL, and custom domains
- Load test the deployment and document capacity and cost analysis
- Build a complete production stack: FastAPI, vLLM, Kubernetes, and monitoring
- Implement reliability patterns: health checks, circuit breakers, and fallback
- Load test for target throughput and document the architecture
- Present a production deployment guide with all configuration decisions
Real-World Projects & Portfolio Outcomes
5 portfolio-worthy projects that prove your skills to employers.
FastAPI Model Server
Build a FastAPI server for an open-source LLM with streaming responses, request batching, authentication, and rate limiting. Containerize with Docker and benchmark latency and throughput.
vLLM Production Deployment
Deploy vLLM with Docker for a 7B parameter model with quantization, continuous batching, prefix caching, and tensor parallelism. Benchmark against a baseline and set up health checks and graceful shutdown.
Kubernetes Model Platform
Deploy a multi-model serving platform on Kubernetes with GPU scheduling, auto-scaling, rolling updates, and a model router. Set up Prometheus and Grafana monitoring dashboards.
Cloud LLM API
Deploy an LLM API to AWS, GCP, or Azure with auto-scaling, Langfuse observability, load balancing, and SSL. Load test the deployment and document capacity and cost analysis.
Full Production Stack
Build a complete production stack combining FastAPI, vLLM, Kubernetes, and monitoring. Implement reliability patterns including health checks, circuit breakers, and fallback. Load test for target throughput and document the full architecture.
Tools, Technologies & Models Covered
LLM Models Covered
| Llama 3.1 8B | Meta | vLLM, TGI, Docker, Kubernetes deployment |
|---|---|---|
| Mistral 7B | Mistral AI | Quantized serving, cloud deployment |
| Qwen 2.5 7B | Alibaba | Multi-model serving, Kubernetes |
| Gemma 2 9B | FastAPI serving, monitoring | |
| Phi-3 Mini | Microsoft | Edge and CPU deployment |
| BGE & E5 | Various | Embedding model serving for RAG |
Tools & Frameworks Covered
| FastAPI | Model serving API framework | Deep |
|---|---|---|
| vLLM | High-throughput LLM serving | Deep |
| TGI | Hugging Face text generation inference | Deep |
| Docker | Containerization for AI deployment | Deep |
| Kubernetes | Container orchestration for AI workloads | Deep |
| Helm | Kubernetes package management | Intermediate |
| Prometheus + Grafana | Metrics and dashboards | Intermediate |
| Langfuse | LLM observability and tracing | Intermediate |
| Locust / k6 | Load testing for model serving | Intermediate |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Arjun Mehta
Lead ML Infrastructure Engineer
8+ years in ML infrastructure and production deployment
Deployed model serving platforms processing 10M+ daily requests
3,200+ engineers trained
A model is not production-ready until it has been load tested, monitored, and survived a failure. Deployment is engineering discipline applied to ML — every decision should be measured, documented, and reversible.
Course Team & Curriculum Design
Our instructor team includes former ML platform engineers who have deployed model serving infrastructure at startups and enterprises. Teaching assistants are available for Docker, Kubernetes, and cloud deployment support during live cohorts.
Learning Path & Prerequisites
Prerequisites
- Intermediate Python and basic command-line skills
- Familiarity with REST APIs and HTTP (request/response, status codes, JSON)
- Basic Docker knowledge (build, run, pull images) — we cover GPU Docker in depth
- Familiarity with Hugging Face Transformers (load model, tokenizer, generate)
- Access to a GPU for labs (Colab, Kaggle, or local with 16GB+ VRAM)
Recommended Learning Paths
ML engineer deploying first model to production
- Complete Modules 1–4 for FastAPI, Docker, and vLLM fundamentals
- Use Module 9 for monitoring and Module 10 for reliability
- Build the FastAPI Model Server and vLLM Production Deployment projects
- Finish with the Full Production Stack project for end-to-end experience
DevOps engineer supporting AI workloads
- Focus on Modules 3, 6, 7, and 10 for Docker, Kubernetes, cloud, and scaling
- Use Module 9 for monitoring and observability setup
- Complete the Kubernetes Model Platform and Cloud LLM API projects
Startup founder shipping an AI API
- Take Modules 1–4 for FastAPI, Docker, and vLLM deployment
- Use Module 7 for cloud deployment and Module 9 for monitoring
- Complete the vLLM Production Deployment and Cloud LLM API projects
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹5,999 | All 11 modules, 5 projects, community Discord, lifetime access |
|---|---|---|
| Early Bird | ₹4,499 | Same as Self-Paced — limited time before cohort launch |
| Cohort Live | ₹11,999 | Live sessions, TA support, code review, and certificate |
What Is Included
- 11 modules with 35+ hands-on deployment labs
- 5 capstone projects with code review and feedback
- Pre-configured Docker, Kubernetes, and cloud deployment templates
- Private Discord community with instructors and TAs
- Lifetime access including future updates as deployment tools evolve
- Certificate of completion with project portfolio
30-day money-back guarantee. If you complete the first 4 modules and feel the course is not for you, get a full refund — no questions asked.
Frequently Asked Questions
Do I need my own GPU to take this course?
No. Most labs can be completed on Google Colab (free T4 GPU) or using cloud GPU instances. For Kubernetes and cloud deployment modules, we provide templates for AWS, GCP, and Azure with free tier or low-cost options. If you have a local GPU, you can run Docker and vLLM locally for faster iteration.
Do I need prior Kubernetes or Docker experience?
Basic Docker knowledge (building and running images) is recommended. We cover GPU Docker and production Docker patterns in depth in Module 3. For Kubernetes, we start from fundamentals in Module 6 — no prior K8s experience is required, though familiarity with containers is helpful.
Which serving engine should I use — vLLM or TGI?
Both are excellent. vLLM generally delivers higher raw throughput and has better speculative decoding and prefix caching support. TGI has tighter Hugging Face integration, better structured output support, and a mature message format. Module 4 covers vLLM in depth, Module 5 covers TGI, and we provide a decision framework for choosing between them based on your workload.
Which cloud provider does the course focus on?
We cover AWS, GCP, and Azure equally in Module 7. You can complete the cloud deployment project on any of the three. We also cover serverless GPU platforms like Modal, Replicate, and RunPod for cost-effective deployment without managing infrastructure.
Is this course about training models or serving them?
This course is specifically about serving and deployment — taking a trained or fine-tuned model and making it available as a production API. If you want to learn about training and fine-tuning, see our Fine-Tuning LLMs course (AIM-607). This course pairs naturally with that one: train your model, then deploy it with what you learn here.
Will I get a certificate?
Yes. Complete all 5 capstone projects and submit them for code review. Upon passing, you receive a certificate with your project portfolio links, suitable for sharing on LinkedIn and with employers.
How is this course different from the Inference Optimization course (AIM-610)?
The Inference Optimization course (AIM-610) focuses on making inference faster and cheaper — quantization, attention, KV cache, batching, and speculative decoding. This course (AIM-612) focuses on deployment infrastructure — Docker, Kubernetes, cloud platforms, monitoring, and production reliability. They complement each other: optimization makes the engine faster, deployment builds the vehicle around it.
What to Learn Next
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