LLMOps Course
Master the infrastructure behind large language models with hands-on training in LangServe, LangSmith, vLLM, Quantization, and LLM evaluation. This LLMOps course prepares you to deploy, trace, optimize, and monitor scalable LLM systems in real-world environments.
Enroll in India’s top LLMOps program with expert mentorship, certification, and job-oriented projects. View syllabus, course fees, or schedule a free consultation today.
Why Choose Our LLMOps Course?
Master LLM Deployment at Scale
Deploy large language models using vLLM, DeepSpeed, and model serving frameworks across GPU clusters.
PromptOps & Evaluation Pipelines
Track, debug, and version prompts using PromptLayer, LangSmith, and custom eval stacks.
Quantization & Fine-Tuning
Speed up inference with QLoRA, INT4/8 quantization, and LoRA-based fine-tuning techniques.
LangChain & LangServe in Production
Use LangServe for structured LLM deployment and LangChain for modular chaining logic.
Inference Optimization with vLLM
Leverage vLLM for high-throughput LLM inference and support for FlashAttention, paged KV cache.
Secure Function Calling & Guardrails
Implement restricted tool access, guardrails, and secure function calling in real-world apps.
LLMOps Observability & Cost Control
Trace token-level metrics, latency, cost spikes, and hallucinations using LangSmith & Langtrace.
Multi-Model & Hybrid Deployments
Route queries across OpenAI, Claude, local models, and use fallback strategies via orchestrators.
Mentorship from LLMOps Engineers
Get hands-on guidance from engineers deploying high-availability LLM services and RAG pipelines.
Who Should Join This LLMOps Course?
Ideal for ML engineers, DevOps professionals, backend developers, and AI infrastructure teams—this course teaches how to fine-tune, deploy, orchestrate, and monitor LLMs in production at scale.
Top Skills You’ll Gain in LLMOps Course
LLMOps Tools & Frameworks You’ll Master
vLLM
Fast LLM Inference Engine
Enable high-throughput, low-latency LLM serving with support for continuous batching.
DeepSpeed
Optimized LLM Training & Serving
Accelerate large model training, inference, and deployment with memory-efficient techniques.
LangSmith
LLM Tracing & Evaluation
Visualize and debug model runs, prompt templates, and performance metrics for LLMs.
QLoRA / PEFT
Efficient Fine-Tuning Frameworks
Perform parameter-efficient tuning using LoRA, QLoRA, and PEFT adapters on LLMs.
LangChain
Modular LLM Orchestration
Connect prompts, chains, agents, and memory to build composable LLM applications.
LlamaIndex
RAG Pipeline Construction
Connect LLMs to structured and unstructured data via embedding and vector search.
PromptOps
Prompt Lifecycle Management
Track, version, and govern prompt templates for reproducible and scalable deployment.
LangFuse
LLMOps Observability Platform
Monitor latency, cost, prompt inputs, and output correctness with full trace logging.
vLLM
Fast LLM Inference Engine
Enable high-throughput, low-latency LLM serving with support for continuous batching.
DeepSpeed
Optimized LLM Training & Serving
Accelerate large model training, inference, and deployment with memory-efficient techniques.
LangSmith
LLM Tracing & Evaluation
Visualize and debug model runs, prompt templates, and performance metrics for LLMs.
QLoRA / PEFT
Efficient Fine-Tuning Frameworks
Perform parameter-efficient tuning using LoRA, QLoRA, and PEFT adapters on LLMs.
LangChain
Modular LLM Orchestration
Connect prompts, chains, agents, and memory to build composable LLM applications.
LlamaIndex
RAG Pipeline Construction
Connect LLMs to structured and unstructured data via embedding and vector search.
PromptOps
Prompt Lifecycle Management
Track, version, and govern prompt templates for reproducible and scalable deployment.
LangFuse
LLMOps Observability Platform
Monitor latency, cost, prompt inputs, and output correctness with full trace logging.
Course Roadmap – Operationalizing Large Language Models
LLMOps Foundations
Understand the LLM lifecycle: • Inference, fine-tuning, deployment • Architecture: Transformer, Mamba, MoE • Lab: Setup vLLM for inference • Tools: vLLM, HuggingFace, Python
Serving & Scaling LLMs
Deploy large models at scale: • GPU/CPU, quantization, LoRA, QLoRA • Serverless vs persistent inference • Tools: DeepSpeed, Ray, vLLM, Triton
RAGOps & Vector DBs
Efficient retrieval workflows: • Chunking, embedding, hybrid retrieval • Pipelines: RAG with FAISS/Qdrant • Tools: LangChain, LlamaIndex, Qdrant
PromptOps & LLM Reasoning
Prompt lifecycle & evaluation: • Prompt templates, chaining, tuning • Metrics: grounding, hallucination rate • Tools: PromptLayer, LangChain
Monitoring & Observability
Track, trace, debug LLM workflows: • Metrics: latency, cost, token usage • Guardrails & fallbacks • Tools: LangSmith, LangFuse, Grafana
Security, Policy & Access
Build secure GenAI applications: • Identity, sandboxing, request isolation • Governance policies • Tools: MCP, OpenAI key mgmt
LLM CI/CD Pipelines
Model delivery & deployment: • Versioning, rollback, shadow testing • Integration with MLflow, DVC • Tools: GitHub Actions, Docker, CI/CD
LLMOps Frameworks & SDKs
Explore LLMOps tools landscape: • LangServe, BentoML, Hugging Face Hub • Comparison: LLMOps vs MLOps • SDKs: OpenAI, Cohere, Anthropic
Evaluation & LLM Benchmarks
Test for quality and drift: • Golden datasets, human evals • Token-level tracking & scoring • Tools: Trulens, Ragas, Promptfoo
Capstone: LLMOps Project
End-to-end LLM system ops: • Setup serving + RAG + observability • Add prompt guardrails + monitoring • Tools: vLLM, LangSmith, LangGraph
LLMOps Foundations
Understand the LLM lifecycle: • Inference, fine-tuning, deployment • Architecture: Transformer, Mamba, MoE • Lab: Setup vLLM for inference • Tools: vLLM, HuggingFace, Python
Serving & Scaling LLMs
Deploy large models at scale: • GPU/CPU, quantization, LoRA, QLoRA • Serverless vs persistent inference • Tools: DeepSpeed, Ray, vLLM, Triton
RAGOps & Vector DBs
Efficient retrieval workflows: • Chunking, embedding, hybrid retrieval • Pipelines: RAG with FAISS/Qdrant • Tools: LangChain, LlamaIndex, Qdrant
PromptOps & LLM Reasoning
Prompt lifecycle & evaluation: • Prompt templates, chaining, tuning • Metrics: grounding, hallucination rate • Tools: PromptLayer, LangChain
Monitoring & Observability
Track, trace, debug LLM workflows: • Metrics: latency, cost, token usage • Guardrails & fallbacks • Tools: LangSmith, LangFuse, Grafana
Security, Policy & Access
Build secure GenAI applications: • Identity, sandboxing, request isolation • Governance policies • Tools: MCP, OpenAI key mgmt
LLM CI/CD Pipelines
Model delivery & deployment: • Versioning, rollback, shadow testing • Integration with MLflow, DVC • Tools: GitHub Actions, Docker, CI/CD
LLMOps Frameworks & SDKs
Explore LLMOps tools landscape: • LangServe, BentoML, Hugging Face Hub • Comparison: LLMOps vs MLOps • SDKs: OpenAI, Cohere, Anthropic
Evaluation & LLM Benchmarks
Test for quality and drift: • Golden datasets, human evals • Token-level tracking & scoring • Tools: Trulens, Ragas, Promptfoo
Capstone: LLMOps Project
End-to-end LLM system ops: • Setup serving + RAG + observability • Add prompt guardrails + monitoring • Tools: vLLM, LangSmith, LangGraph
Industry-Trusted LLMOps Certificate
Industry-Trusted LLMOps Certificate
After completing this LLMOps Course, you’ll earn a globally recognized certificate— proof you can deploy, monitor, and scale large language models in production. Whether you’re managing infrastructure or building AI-powered systems, this certificate validates your expertisein fine-tuning, inference optimization, serving frameworks, observability, and governance.
LLMOps Course vs Free Tutorials & Bootcamps
| Feature | LLMOps Course | Other Courses |
|---|---|---|
| Model Serving & Inference | ✔ Use vLLM, DeepSpeed, and TGI for optimized LLM serving pipelines | ✘ Uses naive APIs; lacks performance tuning & batching |
| Versioning & Checkpointing | ✔ Integrate MLflow/DVC for model lineage, rollback, and reproducibility | ✘ Lacks modular tracking or model lifecycle governance |
| Security & Access Control | ✔ Implements prompt isolation, rate limiting, API key guards, and inference sandboxing | ✘ Basic public endpoints; no granular access enforcement |
| Observability & Tracing | ✔ Built-in tracing with LangSmith, LangFuse, and token-level cost monitoring | ✘ No real-time logs, metrics, or drift diagnostics |
| Fine-tuning & Quantization | ✔ Learn LoRA, QLoRA, PEFT and use Hugging Face PEFT + bitsandbytes for optimization | ✘ Teaches fine-tuning without performance-aware methods |
| Deployment Pipelines | ✔ CI/CD pipelines using GitHub Actions + Docker + Kubernetes + HF Spaces + AWS/GCP | ✘ Deploys via manual scripts or Colab; not scalable |
| Placement & Certification | ✔ Industry-validated certificate + job prep + live mentor feedback on infra projects | ✘ No career support or infrastructure-centric feedback |
LLMOps Course Fees
Included Benefits:
- Mentorship from LLMOps infrastructure experts.
- Hands-on projects in vLLM, LangSmith, TGI, MLflow, and Kubernetes.
- Placement assistance: mock interviews, resume revamp, referrals.
- Lifetime access to session recordings & future tool updates.
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