LLMOps: Deploying LLM Applications
Take LLM applications to production: serving, evaluation, observability, cost control and security.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Mon, 07 Dec 2026 · 40 hours · 5 days
LLMOps: Deploying LLM Applications
Duration: 40 hours · 5 days
Next batch: Mon, 07 Dec 2026
Format: Online live · Bengaluru classroom
Modules: 9 · Topics: 42
Level: Intermediate to Advanced
Fee: ₹17,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| LLMOps: Deploying LLM Applications | Mon, 07 Dec 2026 | Bengaluru classroom | Weekday | 5 days | Enquire |
| LLMOps: Deploying LLM Applications | Sat, 23 Jan 2027 | Online live | Weekend (Sat) | 6 Saturdays | Enquire |
| LLMOps: Deploying LLM Applications | Wed, 03 Mar 2027 | Online live | Weekday | 5 days | Enquire |
Soaring demand, and where it takes you
Many organisations have GenAI prototypes; the skills to run them reliably, safely and affordably in production are in short supply.
Become an expert
Roles this programme prepares you for:
Become a LLMOps Engineer
Deploys, monitors and optimises LLM applications in production.
- Choose between hosted APIs and self-hosted models
- Serve open models with a high-throughput inference server
- Build automated evaluation into CI/CD
About the course
How to run LLM applications in production: choosing between hosted APIs and self-hosted models, serving and scaling, evaluation pipelines, tracing and monitoring, cost control and security. Participants deploy and monitor a real application.
Take LLM applications to production: serving, evaluation, observability, cost control and security.
Why should I attend?
ML and MLOps engineers
Backend and platform engineers
Data scientists deploying LLM apps
DevOps engineers supporting AI teams
Prerequisites
Python; Docker basics; familiarity with REST APIs; some experience building an LLM app
Level
Intermediate to Advanced
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 40 hours · 5 days
Course fee (open batch): ₹17,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Choose between hosted APIs and self-hosted models
- Serve open models with a high-throughput inference server
- Build automated evaluation into CI/CD
- Trace, log and monitor LLM requests
- Control token usage, latency and cost
- Secure LLM apps against prompt injection and data leakage
Skills you will gain
Serving
Quality
Operations
Tools and platforms covered
Docker
Kubernetes
vLLM
FastAPI
MLflow
Prometheus
Grafana
GitHub ActionsCourse curriculum
M01 LLM application architecture
- API vs self-hosted
- Latency and cost drivers
- Reference architectures
- Gateway and proxy patterns
- Multi-tenant considerations
🧪 Hands-on: Map an application's request path
M02 Serving models
- Containerising an LLM service
- vLLM and batching
- Autoscaling on Kubernetes
- GPU sizing basics
- Quantised models for serving
🧪 Hands-on: Deploy an open model behind an API
M03 Evaluation pipelines
- Building eval sets
- LLM-as-judge and its limits
- Regression gates in CI
- Human review loops
- Online vs offline evaluation
🧪 Hands-on: Add an eval stage to a pipeline
M04 Observability
- Tracing prompts and responses
- Metrics and dashboards
- Alerting
- OpenTelemetry for LLM apps
- User feedback capture
🧪 Hands-on: Instrument the app with Prometheus and Grafana
M05 Cost and performance
- Caching
- Model routing
- Prompt and context trimming
- Token budgets per feature
- Semantic caching trade-offs
🧪 Hands-on: Reduce cost per request and measure it
M06 Security
- Prompt injection
- Output filtering
- Secrets and data handling
- Jailbreak testing
- PII redaction in logs
🧪 Hands-on: Red-team the deployed app
M07 Prompt and model lifecycle
- Prompt versioning and registries
- A/B testing models
- Rollback strategies
- Change approval
🧪 Hands-on: Version prompts and roll back a bad release behind a feature flag
M08 Reliability engineering for LLM apps
- Rate limits and retries
- Fallback models
- Timeouts and streaming
- SLOs for AI features
🧪 Hands-on: Add fallbacks and an SLO dashboard to a chat service
M09 Capstone: production-grade LLM service
- Architecture review
- CI pipeline with eval gates
- Deploy to Kubernetes
- Runbook and handover
🧪 Hands-on: Deploy an evaluated, observable RAG service with a runbook
Hands-on projects
Production LLM service
Deploy, evaluate and monitor a RAG service with dashboards.
Cost optimisation report
Measure and reduce cost and latency for a workload.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for LLMOps: Deploying LLM Applications. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Do we need GPUs?
Some labs use GPUs for self-hosting; hosted-API options are available where GPUs are not.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹17,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
40 hours · 5 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
How to run LLM applications in production: choosing between hosted APIs and self-hosted models, serving and scaling, evaluation pipelines, tracing and monitoring, cost control and security. Participants deploy and monitor a real application.
What is LLMOps: Deploying LLM Applications?
Take LLM applications to production: serving, evaluation, observability, cost control and security.
Who this course is for
ML and MLOps engineers, Backend and platform engineers, Data scientists deploying LLM apps, DevOps engineers supporting AI teams.
What you will be able to do
Choose between hosted APIs and self-hosted models. Serve open models with a high-throughput inference server. Build automated evaluation into CI/CD. Trace, log and monitor LLM requests. Control token usage, latency and cost. Secure LLM apps against prompt injection and data leakage.
Career paths
Roles this course supports include LLMOps Engineer, ML Platform Engineer, AI Reliability Engineer. Many organisations have GenAI prototypes; the skills to run them reliably, safely and affordably in production are in short supply.
AI & GenAI guides
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