Gen AI Specialization
End-to-end GenAI engineering: Transformers → agents, multimodal RAG, diffusion, ViT, VLMs, eval & deployment.
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.
Deploy large language models using vLLM, DeepSpeed, and model serving frameworks across GPU clusters.
Track, debug, and version prompts using PromptLayer, LangSmith, and custom eval stacks.
Speed up inference with QLoRA, INT4/8 quantization, and LoRA-based fine-tuning techniques.
Use LangServe for structured LLM deployment and LangChain for modular chaining logic.
Leverage vLLM for high-throughput LLM inference and support for FlashAttention, paged KV cache.
Implement restricted tool access, guardrails, and secure function calling in real-world apps.
Trace token-level metrics, latency, cost spikes, and hallucinations using LangSmith & Langtrace.
Route queries across OpenAI, Claude, local models, and use fallback strategies via orchestrators.
Get hands-on guidance from engineers deploying high-availability LLM services and RAG pipelines.
Fast LLM Inference Engine
Enable high-throughput, low-latency LLM serving with support for continuous batching.
Optimized LLM Training & Serving
Accelerate large model training, inference, and deployment with memory-efficient techniques.
LLM Tracing & Evaluation
Visualize and debug model runs, prompt templates, and performance metrics for LLMs.
Efficient Fine-Tuning Frameworks
Perform parameter-efficient tuning using LoRA, QLoRA, and PEFT adapters on LLMs.
Modular LLM Orchestration
Connect prompts, chains, agents, and memory to build composable LLM applications.
RAG Pipeline Construction
Connect LLMs to structured and unstructured data via embedding and vector search.
Prompt Lifecycle Management
Track, version, and govern prompt templates for reproducible and scalable deployment.
LLMOps Observability Platform
Monitor latency, cost, prompt inputs, and output correctness with full trace logging.
Fast LLM Inference Engine
Enable high-throughput, low-latency LLM serving with support for continuous batching.
Optimized LLM Training & Serving
Accelerate large model training, inference, and deployment with memory-efficient techniques.
LLM Tracing & Evaluation
Visualize and debug model runs, prompt templates, and performance metrics for LLMs.
Efficient Fine-Tuning Frameworks
Perform parameter-efficient tuning using LoRA, QLoRA, and PEFT adapters on LLMs.
Modular LLM Orchestration
Connect prompts, chains, agents, and memory to build composable LLM applications.
RAG Pipeline Construction
Connect LLMs to structured and unstructured data via embedding and vector search.
Prompt Lifecycle Management
Track, version, and govern prompt templates for reproducible and scalable deployment.
LLMOps Observability Platform
Monitor latency, cost, prompt inputs, and output correctness with full trace logging.
Understand the LLM lifecycle: • Inference, fine-tuning, deployment • Architecture: Transformer, Mamba, MoE • Lab: Setup vLLM for inference • Tools: vLLM, HuggingFace, Python
Deploy large models at scale: • GPU/CPU, quantization, LoRA, QLoRA • Serverless vs persistent inference • Tools: DeepSpeed, Ray, vLLM, Triton
Efficient retrieval workflows: • Chunking, embedding, hybrid retrieval • Pipelines: RAG with FAISS/Qdrant • Tools: LangChain, LlamaIndex, Qdrant
Prompt lifecycle & evaluation: • Prompt templates, chaining, tuning • Metrics: grounding, hallucination rate • Tools: PromptLayer, LangChain
Track, trace, debug LLM workflows: • Metrics: latency, cost, token usage • Guardrails & fallbacks • Tools: LangSmith, LangFuse, Grafana
Build secure GenAI applications: • Identity, sandboxing, request isolation • Governance policies • Tools: MCP, OpenAI key mgmt
Model delivery & deployment: • Versioning, rollback, shadow testing • Integration with MLflow, DVC • Tools: GitHub Actions, Docker, CI/CD
Explore LLMOps tools landscape: • LangServe, BentoML, Hugging Face Hub • Comparison: LLMOps vs MLOps • SDKs: OpenAI, Cohere, Anthropic
Test for quality and drift: • Golden datasets, human evals • Token-level tracking & scoring • Tools: Trulens, Ragas, Promptfoo
End-to-end LLM system ops: • Setup serving + RAG + observability • Add prompt guardrails + monitoring • Tools: vLLM, LangSmith, LangGraph
Understand the LLM lifecycle: • Inference, fine-tuning, deployment • Architecture: Transformer, Mamba, MoE • Lab: Setup vLLM for inference • Tools: vLLM, HuggingFace, Python
Deploy large models at scale: • GPU/CPU, quantization, LoRA, QLoRA • Serverless vs persistent inference • Tools: DeepSpeed, Ray, vLLM, Triton
Efficient retrieval workflows: • Chunking, embedding, hybrid retrieval • Pipelines: RAG with FAISS/Qdrant • Tools: LangChain, LlamaIndex, Qdrant
Prompt lifecycle & evaluation: • Prompt templates, chaining, tuning • Metrics: grounding, hallucination rate • Tools: PromptLayer, LangChain
Track, trace, debug LLM workflows: • Metrics: latency, cost, token usage • Guardrails & fallbacks • Tools: LangSmith, LangFuse, Grafana
Build secure GenAI applications: • Identity, sandboxing, request isolation • Governance policies • Tools: MCP, OpenAI key mgmt
Model delivery & deployment: • Versioning, rollback, shadow testing • Integration with MLflow, DVC • Tools: GitHub Actions, Docker, CI/CD
Explore LLMOps tools landscape: • LangServe, BentoML, Hugging Face Hub • Comparison: LLMOps vs MLOps • SDKs: OpenAI, Cohere, Anthropic
Test for quality and drift: • Golden datasets, human evals • Token-level tracking & scoring • Tools: Trulens, Ragas, Promptfoo
End-to-end LLM system ops: • Setup serving + RAG + observability • Add prompt guardrails + monitoring • Tools: vLLM, LangSmith, LangGraph
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.
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 |
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End-to-end GenAI engineering: Transformers → agents, multimodal RAG, diffusion, ViT, VLMs, eval & deployment.
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