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Optimizing and Deploying LLM Systems

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Optimizing and Deploying LLM Systems

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Build NLP workflows using transformer models and Hugging Face tools.

  • Implement RAG systems with LangChain, vector stores, and document loaders.

  • Create and manage multi-agent pipelines with tools and external APIs.

  • Deploy LLM apps with FastAPI, Docker, monitoring, and cloud platforms.

Details to know

Shareable certificate

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Assessments

13 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Building LLMs with Hugging Face and LangChain Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course advances your skills from building working LLM prototypes to scaling, integrating, and deploying production-grade AI systems. You’ll blend system-level concepts with hands-on engineering to profile performance, integrate real-time data and multimodal sources, and ship secure, cloud-deployed applications.

Whether you’re a developer, data scientist, or AI practitioner, this course gives you a clear roadmap to transform optimized LangChain workflows into reliable, observable services that interact with live APIs, structured data, and orchestration frameworks. Through guided lessons, structured demonstrations, and project-based learning, you’ll learn how to profile latency and token usage, design efficient prompts and chains, and evaluate pipelines with LLMOps metrics. You’ll connect external APIs, build hybrid retrieval across text, tables, and images, and orchestrate complex data flows using LlamaIndex and LangGraph. Finally, you’ll containerize and deploy a FastAPI service with authentication, monitoring, and CI/CD, culminating in an end-to-end capstone deployment. By the end of this course, you will be able to: • Profile and optimize LLM pipelines for latency, throughput, and token/cost efficiency. • Design prompt and chain strategies (dynamic templates, caching, auto-tuning) to improve reliability and speed. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Integrate real-world data via secure APIs and hybrid retrieval across structured, unstructured, and multimodal sources. • Orchestrate data and evaluation workflows using LlamaIndex and LangGraph for scalable reasoning. • Build, secure, containerize, and deploy a FastAPI service with JWT/OAuth, monitoring, and CI/CD automation. This course is ideal for AI developers, data scientists, and software engineers ready to move beyond prompt experimentation and deliver production-ready LLM applications. A working knowledge of Python and APIs is recommended; all steps are guided to help you master the deployment stack. Join us to learn the engineering patterns that power modern, scalable generative AI—from optimization and orchestration to secure cloud deployment.

Learn to optimize LLM applications for efficiency, scalability, and performance. This module covers latency profiling, prompt optimization, and caching strategies for faster inference. Master cost control, evaluation frameworks, and performance-tuned pipeline design for production-ready systems.

What's included

11 videos5 readings4 assignments1 discussion prompt

11 videos•Total 54 minutes
  • Specialization Introduction•6 minutes
  • Course Introduction•5 minutes
  • Why Optimization Matters in LLM Systems•6 minutes
  • Demonstration: Profiling Response Latency and Token Usage in LangChain App•3 minutes
  • Demonstration: Implement Async Batching and Caching •4 minutes
  • Efficient Prompts for Reliability and Speed•6 minutes
  • Demonstration: Dynamic Prompts and Templates for Better Control•4 minutes
  • Demonstration: Implement Prompt Caching and Auto-Tuning •5 minutes
  • Evaluating Model Output Quality•6 minutes
  • Demonstration: LangSmith + Weights and Biases Integration•4 minutes
  • Demonstration: Tracking API Costs and Token Usage •4 minutes
5 readings•Total 70 minutes
  • Welcome to Optimizing and Deploying LLM Systems•15 minutes
  • Cost and Latency Optimization Guide•15 minutes
  • Prompt Compression and Evaluation Metrics•15 minutes
  • LLMOps Evaluation Frameworks•15 minutes
  • Summary of Scaling and Optimizing LLM Pipelines•10 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Scaling and Optimizing LLM Pipelines•30 minutes
  • Practice Quiz: Performance Optimization Fundamentals•6 minutes
  • Practice Quiz: Prompt and Chain Optimization•6 minutes
  • Practice Quiz: Evaluating and Monitoring Pipelines•6 minutes
1 discussion prompt•Total 10 minutes
  • Introduce Yourself•10 minutes

Master integration of diverse data sources within LLM-powered systems. This module covers API-driven workflows, secure automation, and hybrid data pipelines. Learn to use LlamaIndex and LangGraph to build intelligent, context-aware retrieval and reasoning systems.

What's included

9 videos4 readings4 assignments

9 videos•Total 48 minutes
  • Power of APIs in LLMs•6 minutes
  • Demonstration: Connecting Multiple External APIs•3 minutes
  • Demonstration: Event-Driven Pipeline with Webhooks and Queues •5 minutes
  • Combining Structured and Unstructured Data•6 minutes
  • Demonstration:Natural-Language to SQL with LangChain and OpenAI•4 minutes
  • Demonstration: Hybrid Retrieval Using LLM and LangChain•6 minutes
  • Data Indexing and Workflow Orchestration•6 minutes
  • Demonstration: Complex Data Pipeline with LlamaIndex•6 minutes
  • Demonstration: Automated Evaluation Workflow with LangGraph and LLM•6 minutes
4 readings•Total 55 minutes
  • Secure API Integration and Governance•15 minutes
  • Multi-Modal Data Fusion•15 minutes
  • Combining Multiple Data Sources for Reasoning•15 minutes
  • Summary of Integrating APIs and External Data Sources•10 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Integrating APIs and External Data Sources•30 minutes
  • Practice Quiz: API-Driven LLM Workflows•6 minutes
  • Practice Quiz: Structured and Multi-Modal Data Integration•6 minutes
  • Practice Quiz: Data Orchestration with LlamaIndex and LangGraph•6 minutes

Gain practical skills in deploying and managing LLM systems at scale. This module covers API service design, containerization, and cloud deployment with security and monitoring. Complete a capstone project to deliver a fully deployed, automated, and scalable LLM application.

What's included

13 videos3 readings4 assignments

13 videos•Total 78 minutes
  • From Development to Production — API Design•6 minutes
  • Demonstration: Creating REST Endpoints with FastAPI for LangChain Workflows•4 minutes
  • Demonstration: Adding Auth (JWT/OAuth) and Rate Limiting•7 minutes
  • Containerization Essentials for AI Apps•6 minutes
  • Demonstration: Dockerize LangChain + FastAPI App•5 minutes
  • Demonstration: Deployment of API on AWS•7 minutes
  • Capstone Overview: LLM Orchestrator•5 minutes
  • Demonstration: Capstone Project Overview and Architecture•7 minutes
  • Demonstration: Building LLM APIs with FASTAPI•7 minutes
  • Demonstration: Authentication and Analytics Integration•6 minutes
  • Demonstration: Data Pipeline and Docker Setup•5 minutes
  • Demonstration: Automating Deployment with CI/CD•5 minutes
  • Demonstration: Cloud Deployment and Frontend Setup•6 minutes
3 readings•Total 45 minutes
  • Secure API Architecture•15 minutes
  • Secrets and Environment Configurations in Cloud•15 minutes
  • Summary of Deploying and Managing LLM Applications•15 minutes
4 assignments•Total 48 minutes
  • Deployed LLM System Evaluation Report•30 minutes
  • Practice Quiz: Building an LLM API Service•6 minutes
  • Practice Quiz: Containerization and Cloud Deployment•6 minutes
  • End-to-End LLM System Deployment•6 minutes

Conclude your learning journey with a hands-on final project and assessment. This module reinforces key concepts in LLM optimization, integration, and deployment. Reflect on your progress and prepare for advanced, real-world LLM system development.

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 3 minutes
  • Course Summary•3 minutes
1 reading•Total 60 minutes
  • Practice Project: Containerized AI Pipeline using FastAPI and LlamaIndex•60 minutes
1 assignment•Total 30 minutes
  • Knowledge Check: Optimizing and Deploying LLM Systems•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe your Learning Journey•10 minutes

Earn a career certificate

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Instructor

Edureka
203 Courses•185,285 learners

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Frequently asked questions

Basic knowledge of Python, APIs, and machine learning.

LLM optimization, API integration, data orchestration, and deployment.

Around 4–6 weeks across three main modules.

Ideal for intermediate learners with coding basics.

Yes, includes demos, quizzes, and graded assignments.

LangChain, LangGraph, LlamaIndex, FastAPI, Docker, AWS, and GCP.

Yes, you can revisit materials anytime.

Yes, each module has quizzes and assignments.

Yes, upon successful completion.

It trains you to optimize and deploy LLM apps on the cloud.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,