Production LLM infrastructure training for engineers — serving, observability, evaluation gates, secure releases, and cost control.
A 12-week cohort for engineers who ship LLM features and need production-grade reliability. You build real infra artifacts (load tests, dashboards, eval gates, runbooks) using vLLM, LangServe, LangSmith/Langfuse, MLflow, Kubernetes, and guardrails.
LLMOps (Large Language Model Operations) is the engineering practice of deploying, monitoring, and scaling production LLM systems. It includes inference serving, evaluation gates, prompt and adapter versioning, observability/tracing, security guardrails, and cost controls so teams can ship updates safely and diagnose failures quickly.
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6 production systems — each with deployable infra artifacts you present in interviews and ship at work.
Unified API with latency SLAs, concurrency limits, and fallback routing.
Retrieval-augmented generation with continuous evaluation — not a one-off demo.
LoRA/QLoRA adapter to merged production model with eval gates and version control.
Instrument, monitor, and debug LLM systems under production load.
Agentic workflows with tool allowlisting, circuit breakers, and audit trails.
Route traffic across providers and stay within budget caps.
Every module is designed around what actually breaks in production — and how to prevent, detect, and recover from it.
Deploy models with vLLM and DeepSpeed across GPU clusters — continuous batching, canary rollouts, and automatic rollback on eval gate failure.
Version, trace, and regression-test prompts with LangSmith. Golden-set pass rate is evaluated against an agreed benchmark (for example, 92%+) before promoting a prompt version.
LoRA/QLoRA adapters to merged production models with eval gates. Quantization tradeoff matrix: INT4 vs INT8 vs FP16 on latency, accuracy, and VRAM.
Structured LLM deployment with per-step timeouts, circuit breakers, streaming error recovery, and session-scoped memory with TTL cleanup.
High-throughput serving with PagedAttention and tensor parallelism. KV-cache budget sizing, continuous batching, and p95/p99 latency profiling under load.
Tool allowlisting with schema validation, multi-layer prompt injection defense, and full audit logging — every tool call traced with identity and timestamp.
Token-level cost dashboards via Langfuse, budget caps per team/model with auto-throttle, and semantic drift detection with Slack alerting.
Route queries across OpenAI, Claude, and self-hosted models with cost-aware routing, latency SLA tiers, and auto-failover on provider outages.
PR-style code reviews on every project, simulated ops drills (latency spikes, GPU failures), and twice-weekly office hours for architecture review.
LLMOps (Large Language Model Operations) is the discipline of deploying, monitoring, and scaling production LLM systems. It covers model serving, evaluation gates, prompt and adapter versioning, observability, security guardrails, and cost control.
| Area | MLOps | LLMOps |
|---|---|---|
| Primary workload | Training + batch/online inference for ML models | Real-time LLM APIs with token streaming and tool calls |
| Serving & latency | Model servers, feature stores, predictable payloads | Inference engines (vLLM/TGI/Triton), batching, KV-cache, p95/p99 under load |
| Quality control | Offline metrics, data drift, model monitoring | Golden-set eval gates, prompt regressions, RAG retrieval quality (Ragas/Promptfoo) |
| Versioning | Datasets + model versions | Prompts, adapters (LoRA/QLoRA), chains/agents, and configs (MLflow + Git) |
| Observability | System + model monitoring | Trace-level observability (LangSmith/Langfuse): cost, latency, tool calls, failures |
| Security & governance | PII, access control, data lineage | Prompt injection defense, tool allowlists, audit logs, policy guardrails |
Most failures aren't about prompts — they're operational: serving bottlenecks, missing eval gates, weak observability, and uncontrolled cost. This program teaches the failure modes and the infrastructure patterns to prevent, detect, and recover.
Burst traffic, KV-cache pressure, batching misconfig, cold starts, or upstream dependency failures.
Prompt edits, adapter updates, or RAG changes ship without golden-set regression testing and acceptance gates.
No traces for tool calls, no cost-per-request visibility, and no drift/hallucination alerting.
Prompt injection, weak authN/authZ, missing tool allowlists, and inadequate audit logging.
Stale embeddings, broken indexing jobs, chunking issues, and untested retriever changes.
No token budgets, no caching strategy, no routing tiers, and no team-level caps with throttle/alerts.
Six operational pillars — each taught through hands-on projects with measurable infrastructure outcomes, not slides.
01
Deploy LLMs behind production APIs using vLLM, LangServe, and Triton with continuous batching, auto-scaling, and latency SLAs.
▸p95/p99 latency targets with continuous batching and max-batch-wait tuning
▸Concurrency limits, queueing policies, and circuit breakers for upstream failures
▸GPU utilization monitoring with VRAM headroom and KV-cache budget allocation
02
Run parameter-efficient fine-tuning (LoRA, QLoRA, DPO) with automated evaluation, version control, and artifact tracking via MLflow.
▸Evaluation gates block adapter promotion if accuracy drops below acceptance threshold
▸Cost-per-run tracking: GPU-hours, token count, and improvement-per-dollar metrics
▸Safe adapter merging with runtime compatibility checks and safetensors export
03
Instrument every LLM call with LangSmith and Langfuse — trace prompts, measure cost-per-token, detect drift, and set alerts.
▸Structured trace export via OpenTelemetry with session-based token tracking
▸Drift detection: semantic similarity regression across weekly golden-set snapshots
▸Alert rules: p95 latency breach, hallucination spike, budget cap exceeded → Slack/PagerDuty
04
Ship model updates through tested pipelines: shadow deployments, automated benchmarks, golden-set regression, and rollback-safe releases.
▸Evaluation gates in CI: golden-set pass rate must exceed threshold before deploy
▸Canary/blue-green rollout with automatic rollback on eval regression or latency spike
▸Shadow deployment: run new model alongside production, compare outputs without serving traffic
05
Enforce input/output filters, rate-limits, PII masking, prompt injection defense, and policy guardrails for compliance.
▸AuthN/AuthZ: OAuth2 + JWT with RBAC and tenant isolation for multi-team APIs
▸Prompt injection detection pipeline: input sanitization → model-side filter → output scan
▸Audit logs: every request logged with user identity, tool calls, and schema validation results
06
Right-size GPU resources with quantization (GPTQ, AWQ), request batching, caching, token budgets, and model routing to cut inference bills.
▸Budget caps per team/model with alerts at 80% and auto-throttle at 100%
▸Cost-per-request tracking: token burn, GPU time, and provider cost broken down
▸Model routing: classify requests by SLA tier, route cheaper queries to smaller models
6
Operational pillars
13
Course sections
6
Production projects
12
Week cohort
If you've deployed services at scale before and now need to do it for LLMs — this is your course. Production-grade, from day one.
Moving models from notebooks to production serving endpoints
Extending cloud-native skills to GPU-bound AI workloads
Adding LLM capabilities to existing services reliably
Extending traditional ML pipelines to foundation model workloads
Making architecture decisions about LLM infrastructure at org scale
Pivoting into AI infrastructure from software or data roles
Not theory — every skill below is practiced in a hands-on lab or project. If you can't measure it, we don't teach it.
Every skill is assessed during the capstone — serving endpoint latency, eval accuracy, cost budgets, and code quality reviewed by senior engineers.
Every tool is used inside a project — not a logo wall. You'll know when to pick each tool, what it trades off, and how it fails.
vLLM
PagedAttention-based high-throughput serving with continuous batching
Production standard for self-hosted LLM inference — handles concurrency, KV-cache, tensor parallelism
LangServe
FastAPI-style LLM API endpoints with streaming support
Fastest path from LangChain chain to production API with health checks and schema validation
Triton Inference Server
Multi-framework model serving on GPUs with dynamic batching
Enterprise-grade when you need multi-model serving with GPU scheduling on K8s
TGI
Hugging Face production text-generation server
Native HF model support with flash-attention, quantization, and token streaming out of the box
PEFT / LoRA / QLoRA
Parameter-efficient adapter fine-tuning at fraction of full-train cost
Only practical approach when you need domain adaptation without retraining full weights
DeepSpeed
Distributed training and ZeRO memory optimization
Required for multi-GPU training when model doesn't fit in single GPU VRAM
Hugging Face Transformers
Model loading, tokenization, and training loops
De facto standard for model access — most LLMOps tooling integrates with HF ecosystem
Weights & Biases
Experiment tracking, hyperparameter sweeps, and artifact versioning
Structured experiment comparison with cost-per-run and loss curve visualization
LangSmith
Prompt tracing, evaluation runs, and dataset management
End-to-end trace visibility for every chain step — latency, cost, and quality in one view
Langfuse
Open-source LLM observability and cost analytics
Self-hosted option with per-request cost tracking and team-level budget dashboards
Ragas / Promptfoo
RAG and prompt evaluation frameworks with golden-set testing
Automated eval gates in CI — block deploy if faithfulness or relevancy drops below threshold
Grafana + Prometheus
Dashboards for latency, throughput, error rates, and GPU metrics
Industry-standard infra monitoring — integrates with existing oncall and alerting stacks
LangChain / LangGraph
Chain prompts, tools, and memory into workflows; build agent DAGs
Most adopted orchestration framework — LangGraph adds stateful multi-agent support
MLflow 3.0
Model registry, prompt versioning, experiment tracking, and deployment tracking
Unified lineage from training → evaluation → deployment with GenAI trace viewer
Docker + Kubernetes
Containerized deployments with auto-scaling and GPU scheduling
Non-negotiable for production — every serving endpoint runs in containers on K8s
GitHub Actions
CI/CD pipelines for model releases, config updates, and eval gates
Automate the full deploy cycle: build → test → eval → canary → promote or rollback
Six phases, each ending with a working deliverable and measurable infra artifact — not just theory checkpoints.
Weeks 1–2
LLM lifecycle: pre-train → fine-tune → serve → monitor → iterate
Python automation for LLM APIs (async requests, retries, error handling)
Git strategies for prompt + model + config versioning
Docker: containerize inference servers with multi-stage builds
Deliverable
Dockerized LLM API with health checks, CI pipeline, and version-controlled prompt configs
Weeks 3–4
vLLM: PagedAttention, continuous batching, tensor parallelism, KV-cache sizing
LangServe: FastAPI-based LLM endpoints with streaming and circuit breakers
Quantization: GPTQ, AWQ, GGUF trade-offs (latency vs accuracy vs VRAM)
Benchmarking: p50/p95/p99 latency, throughput (tok/s) under concurrent load
Deliverable
Load test report with p95/p99 latency at representative concurrency (e.g., 100/500/1000 users) + GPU utilization dashboard
Weeks 5–6
LoRA, QLoRA, DPO adapter workflows with cost-per-run tracking
DeepSpeed ZeRO for memory-efficient multi-GPU training
MLflow: track experiments, register adapters, compare eval scores
Evaluation-driven fine-tuning: block promotion if accuracy drops below threshold
Deliverable
Fine-tuned adapter with MLflow lineage, before/after benchmark, and cost analysis report
Weeks 7–8
LangSmith: trace every chain, prompt, and tool call with cost-per-request
Langfuse: open-source cost analytics, team-level budgets, and drift detection
Golden-set regression testing with Ragas / Promptfoo — acceptance thresholds in CI
Drift detection pipeline: weekly semantic similarity regression + alerting
Deliverable
Observability stack with dashboards, alert rules, drift detection pipeline, and oncall runbook
Weeks 9–10
GitHub Actions pipelines with eval gates: golden-set pass rate blocks bad deploys
Kubernetes deployments: canary rollout, blue-green, automatic rollback on eval regression
Security: prompt injection defense, tool allowlisting, RBAC, audit logs, PII masking
Cost engineering: token budgets, budget caps, model routing, GPU right-sizing
Deliverable
CI/CD pipeline with eval gates + security test suite + cost dashboard with budget alerts
Weeks 11–12
End-to-end system: serve → trace → evaluate → secure → deploy → monitor
Integrate RAG pipeline with retriever evaluation and vector DB orchestration
Security review: prompt injection tests, PII scan, access audit
Ops drill: simulated incident (latency spike, GPU failure, cost overrun) — you triage and respond
Deliverable
Production-ready system with CI/CD, eval gates, security review, cost audit, and ops drill postmortem
Master production-grade LLM operations — from inference serving and fine-tuning to observability, versioning, and secure deployment at scale. All 13 sections include hands-on projects, infra artifacts, and real-world failure mode analysis.
Earned through demonstrated competence — not just course completion. This certificate validates your ability to deploy, monitor, secure, and optimize production LLM systems.
Assessment Components
Minimum Bar to Certify
Verification
CERTIFICATE
OF ACHIEVEMENT
THIS IS TO CERTIFY THAT
SHWETA SHARMA
Date : 07/08/2024
Has Successfully Completed The
Comprehensive LLMOps Engineering Program
Conducted By The School Of Core AI.
This program included hands-on training in vLLM, LangServe, TGI, DeepSpeed, LangSmith & Langfuse Observability, LoRA/QLoRA Fine-Tuning, Model Quantization (GPTQ, AWQ, GGUF), MLflow Versioning, Kubernetes Orchestration, LLM Security & Guardrails, PromptOps, RAG Pipeline Orchestration, Multi-Agent Systems, and Production-Grade LLMOps Infrastructure Deployment.
Certification was awarded after passing capstone review, PR-style code review, and simulated ops drill with verified performance against minimum production-readiness bar.
Aishwarya Pandey
Founder and CEO
Certification ID :
DAA1392
No marketing fluff — here is exactly how we back up every claim on this page.
Live instruction, async reviews, and always-on support — designed so working engineers don't have to pause their day jobs to level up.
Two weekly live sessions scheduled across IST evening and US-morning windows. All sessions are recorded — miss a class, watch the replay within 12 hours.
Submit PRs on your project repos anytime. Mentors review within 48 hours with inline comments on production readiness — latency, error handling, security, cost.
Drop in with debugging questions, architecture decisions, or career guidance. One slot covers IST, the other covers US/EU time zones.
Private cohort Slack/Discord with channels for each curriculum section, #infra-help for debugging, and #career for placement prep. Mentors respond within 24 hours on weekdays.
Every lecture, demo, and ops drill is recorded. Project repos with starter code, Dockerfiles, Helm charts, and CI configs remain accessible permanently.
Work alongside ML engineers, platform engineers, and backend developers from across India, Southeast Asia, Middle East, and North America. Peer code reviews are part of the workflow.
The difference isn't content volume — it's whether you practice production failure modes or just follow along.
Model Serving & Inference
This Course
vLLM with continuous batching, KV-cache tuning, tensor parallelism — benchmarked at p95/p99 under concurrent load
Others
Single-request inference with no batching, no latency SLAs, no concurrency testing
Deployment & Rollout
This Course
Canary/blue-green deploys with eval gates — automatic rollback when golden-set regression or latency spike detected
Others
Manual deploys, no rollback path, no pre-deploy evaluation gates
Observability & Tracing
This Course
LangSmith + Langfuse tracing: cost-per-request, drift detection, hallucination rate alerts, oncall runbooks
Others
Print-statement logging, no structured traces, no drift detection pipeline
Security & Guardrails
This Course
Prompt injection defense, tool allowlisting, schema validation, RBAC, audit logs — validated using a lab library of prompt-injection test cases (50+ in course labs)
Others
No input/output validation, public endpoints, no access control or audit trail
Fine-tuning & Quantization
This Course
LoRA/QLoRA with MLflow tracking, before/after benchmark, cost-per-run analysis, adapter merge validation
Others
Fine-tuning in Colab without experiment tracking or production deployment path
Cost Engineering
This Course
Token budgets, cost-per-request dashboards, budget caps with auto-throttle, model routing by SLA tier
Others
No cost visibility, no budget alerts, no routing — monthly invoice surprise
Evaluation & Quality
This Course
Golden-set regression in CI, Ragas/Promptfoo eval harness, acceptance thresholds block bad releases
Others
Manual spot-checking, no regression testing, no eval-gated deployments
Certification & Support
This Course
Capstone review + PR-style code review + ops drill — min bar: p95 target, eval pass rate, budget cap adherence
Others
Auto-generated completion certificate, no production-readiness validation
Three tracks, one goal — production-ready AI. Pick the depth that matches where you are.
End-to-end ML pipelines
LLM deployment & operations
MLOps + LLMOps + AgentOps combined
One flat fee — no hidden charges, no upsells.
One-time Payment
₹35,000
~$420 USD
Already on LLMOps? Level up with a specialization — bundle any two and save more.
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View Course3Deep dive into LLMs — tokenization, attention, fine-tuning (LoRA/QLoRA), RLHF, and inference stacks.
View CourseCommon questions about the LLMOps course — prerequisites, format, and certification.
