LLMOps in Production

[rlscorner]

LLMOps in Production
Published 10/2026
Created by Vaibhav Kumar Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 45 Lectures ( 5h 16m ) | Size: 4.4 GB

Build a production RAG assistant. Master LLM evals, CI quality gates, LoRA fine-tuning, vLLM serving and observability.

What you'll learn
⚡ Explain how LLMOps differs from classic MLOps
⚡ Manage and version prompts as production assets
⚡ Track LLM experiments with MLflow
⚡ Build golden datasets and evaluate LLM outputs
⚡ Use LLM-as-a-judge, Ragas and DeepEval
⚡ Add eval quality gates to CI/CD with promptfoo
⚡ Red team for prompt injection and add guardrails
⚡ Fine-tune open models with LoRA, QLoRA and DPO
⚡ Serve models with vLLM and quantization
⚡ Route, cache and rate limit with an LLM gateway
⚡ Trace and monitor LLM apps with OpenTelemetry and Langfuse
⚡ Control cost and apply the OWASP Top 10 for LLMs

Requirements
❗ Solid Python skills
❗ Basic machine learning concepts
❗ Familiarity with Docker and git
❗ A GPU is optional: free cloud notebooks work for fine-tuning labs

Description
This course contains the use of artificial intelligence

LLMOps is the practice of running LLM applications in production with reliable quality, safety and cost control. This course teaches it end to end, with one project that grows section by section.

You build SupportBot, a RAG customer-support assistant for a SaaS company, and take it all the way to production. You start by treating prompts as code: templates, structured outputs with Pydantic, versioning, a prompt registry and experiment tracking with MLflow.

Next you learn to measure quality. You build a golden dataset, compare classic and semantic metrics, design LLM-as-a-judge rubrics, and evaluate retrieval and answers with Ragas and DeepEval. You then turn those scores into quality gates in GitHub Actions with promptfoo, red team the bot for prompt injection and jailbreaks, and add guardrails.

Then you fine-tune an open model with LoRA and QLoRA using Hugging Face TRL, align it with DPO, evaluate it against the base model, and register it in MLflow. You serve it with vLLM, compare GPTQ, AWQ and GGUF quantization, and put it behind a LiteLLM gateway with caching, fallbacks and rate limits. You deploy with Docker and Kubernetes.

In production you trace every request with OpenTelemetry and Langfuse, score live traffic with online evaluations and user feedback, and build Prometheus and Grafana dashboards with alerts for latency, cost and quality drift.

You finish with a cost optimization playbook, the OWASP Top 10 for LLM applications, PII handling and data governance, responsible AI practices, and a complete capstone that ties every piece together.

Every lecture includes captions and a transcript, and every command and code sample is real, so you can reuse them in your own projects at work.

Who this course is for
⭐ ML engineers moving into LLM systems
⭐ Backend and platform engineers running AI features in production
⭐ MLOps engineers who want to extend their skills to LLMs
⭐ Data scientists who want their LLM prototypes to ship

Code:

https://nitroflare.com/view/A9D89482DFFD0E4/LLMOps_in_Production.part1.rar
https://nitroflare.com/view/46E3DE23A15E917/LLMOps_in_Production.part2.rar
https://nitroflare.com/view/3B3B875378668F4/LLMOps_in_Production.part3.rar
https://nitroflare.com/view/DACA802260C5902/LLMOps_in_Production.part4.rar
https://nitroflare.com/view/768102E43334731/LLMOps_in_Production.part5.rar

https://rapidgator.net/file/648b22cf68eb4c628799fca56b3a3189/LLMOps_in_Production.part1.rar.html
https://rapidgator.net/file/e0627e4d22b23704669410109ec543c3/LLMOps_in_Production.part2.rar.html
https://rapidgator.net/file/9d6bec65c0d9f8c1ab32c3351af48354/LLMOps_in_Production.part3.rar.html
https://rapidgator.net/file/f87c9d8a67b179839bcdbc915318dbed/LLMOps_in_Production.part4.rar.html
https://rapidgator.net/file/1b269fbc8ded60b6b7adeedade30bdbe/LLMOps_in_Production.part5.rar.html