STAGE 01
LLM Foundations
Understand transformer-based models, tokens, context windows, inference and foundation model concepts.
Build practical Large Language Model Operations capabilities for designing, deploying, evaluating, monitoring, securing and governing production LLM applications and enterprise Generative AI systems.
LLMOps extends modern DevOps, MLOps and AI engineering practices into the operational challenges of large language model applications.
Building an LLM application is only one part of the engineering lifecycle. Production systems also require reliable deployment, evaluation, monitoring, security, governance, cost management and continuous improvement.
This learning path covers LLM deployment, model and prompt management, evaluation, observability, RAGOps, AI gateways, security, governance and performance optimization.
The emphasis is on understanding how LLM applications move from experimentation and prototypes into reliable, measurable and governed production AI systems.
Progress from LLM fundamentals to production-grade operational architectures.
Understand transformer-based models, tokens, context windows, inference and foundation model concepts.
Understand APIs, structured outputs, context management, application patterns and integrations.
Explore hosted APIs, inference services, model serving, deployment architecture and scaling.
Manage prompt versions, testing, experimentation, deployment and controlled prompt changes.
Operationalize retrieval systems including ingestion, indexing, retrieval evaluation and knowledge updates.
Measure response quality, relevance, groundedness, safety, latency and application performance.
Monitor traces, tokens, latency, errors, retrieval, model behavior and production usage.
Apply access control, data protection, responsible AI, policy controls and enterprise governance.
Combine deployment, evaluation, observability, optimization and governance into production systems.
A structured curriculum covering the engineering and operational disciplines required for enterprise LLM systems.
Establish the technical foundation required for understanding LLM-based applications.
Understand the application layer connecting software systems with large language models.
Explore deployment patterns for hosted and self-managed LLM workloads.
Apply software-style lifecycle practices to prompts and LLM application instructions.
Operationalize Retrieval-Augmented Generation pipelines and enterprise knowledge systems.
Build evaluation strategies for measuring the performance and reliability of LLM applications.
Monitor the behavior and operational performance of LLM applications in production.
Understand the infrastructure layer used to manage multiple models and AI providers.
Address security risks associated with enterprise LLM applications and data.
Understand governance controls required for enterprise deployment of Generative AI.
Optimize production LLM systems for latency, reliability and operational cost.
Bring together deployment, evaluation, monitoring, security and governance into an operational framework.
Develop the engineering capabilities required to operate modern LLM applications reliably at scale.
Understand models, APIs, context, inference and application integration.
Deploy and integrate LLM workloads into reliable application environments.
Manage prompt development, testing, versioning and controlled production changes.
Operate retrieval pipelines and enterprise knowledge systems supporting LLM applications.
Measure quality, groundedness, relevance, safety and application performance.
Monitor traces, latency, tokens, failures and production application behavior.
Apply security controls for prompts, models, enterprise data and AI applications.
Establish policies, controls, oversight, auditability and responsible AI practices.
Project-based learning connects LLM concepts with production engineering and enterprise AI operations.
Design an architecture for deploying and exposing LLM capabilities through secure application APIs.
Build a lifecycle pipeline for prompt versioning, testing, evaluation and controlled deployment.
Create an operational RAG pipeline covering document ingestion, indexing, retrieval and evaluation.
Build an evaluation framework for measuring response quality, relevance, groundedness and safety.
Design monitoring for LLM traces, tokens, latency, failures, retrieval and application usage.
Combine deployment, evaluation, observability, security, governance and cost management into an end-to-end LLMOps architecture.
The learning sequence follows the operational lifecycle of an enterprise LLM application.
LLMOps capabilities support engineering, platform, architecture and AI operations roles.
Build and operate deployment, monitoring, evaluation and lifecycle systems for LLM applications.
Develop production applications using LLMs, RAG, agents and modern Generative AI architectures.
Build the cloud, infrastructure and platform services required for enterprise AI workloads.
Operate machine learning and LLM lifecycle platforms, automation, deployment and monitoring.
Design enterprise AI architectures integrating models, applications, data, security and operations.
Architect scalable cloud platforms and operational frameworks for enterprise Generative AI.
Build the broader technical foundation required for modern AI engineering and Generative AI.
Build end-to-end AI engineering capabilities from Python and machine learning to Generative AI.
Understand foundation models, LLMs, prompting and modern generative AI application architectures.
Learn retrieval architectures, embeddings, vector search and enterprise knowledge systems.
Explore AI agents, tool calling, planning, reasoning and multi-step AI workflows.
An LLMOps course in India can provide technology professionals with the operational knowledge required to move large language model applications from experimentation into production environments.
Modern enterprise AI systems require more than access to an LLM API. Production workloads need deployment architecture, prompt management, retrieval pipelines, evaluation, observability, security, governance and cost controls.
LLMOps therefore combines concepts from DevOps, MLOps, cloud engineering, AI engineering and software engineering with the specific operational requirements of generative AI applications.
A practical LLMOps learning path should include hands-on projects involving LLM deployment, PromptOps, RAGOps, evaluation, observability, security and enterprise production architecture. The objective is to understand how AI systems can be operated reliably throughout their lifecycle.
LLMOps is the set of engineering, operational and governance practices used to develop, deploy, evaluate, monitor, secure and manage large language model applications in production.
LLMOps is relevant for AI engineers, machine learning engineers, software developers, data professionals, DevOps and MLOps engineers, cloud professionals and technology leaders working with Generative AI.
Yes. LLMOps includes deployment architectures, inference systems, model APIs, AI gateways, routing and production integration.
PromptOps applies engineering and lifecycle practices to prompts, including versioning, testing, evaluation, deployment and monitoring.
RAGOps applies operational engineering practices to Retrieval-Augmented Generation systems, including ingestion, indexing, retrieval evaluation and monitoring.
Yes. Evaluation is a core LLMOps capability covering response quality, relevance, groundedness, safety, latency and application-specific performance.
LLM observability provides visibility into the behavior and performance of LLM applications, including traces, tokens, latency, errors, retrieval and model interactions.
Yes. Production LLMOps requires security controls for access, data protection, prompt attacks, application security and monitoring.
LLMOps builds on MLOps concepts but addresses additional challenges specific to LLM applications, including prompts, context, retrieval, token usage, generative evaluation and model-provider management.
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