Is this course suitable for beginners?

Ideal for intermediate learners with coding basics.

Will there be hands-on exercises or projects?

Yes, includes demos, quizzes, and graded assignments.

What tools or libraries will I use during the course?

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

Can I access the course content after completion?

Yes, you can revisit materials anytime.

Are there any quizzes or assessments included?

Yes, each module has quizzes and assignments.

Will I receive a certificate after completing the course?

Yes, upon successful completion.

How does this course help in deploying real-world LLM models?

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

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

Optimizing and Deploying LLM Systems

This course is part of Building LLMs with Hugging Face and LangChain Specialization

Instructor: Edureka

Included with

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4 modules

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

4 modules

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.

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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 videosTotal 54 minutes

Specialization Introduction6 minutes
Course Introduction5 minutes
Why Optimization Matters in LLM Systems6 minutes
Demonstration: Profiling Response Latency and Token Usage in LangChain App3 minutes
Demonstration: Implement Async Batching and Caching 3 minutes
Efficient Prompts for Reliability and Speed5 minutes
Demonstration: Dynamic Prompts and Templates for Better Control4 minutes
Demonstration: Implement Prompt Caching and Auto-Tuning 5 minutes
Evaluating Model Output Quality5 minutes
Demonstration: LangSmith + Weights and Biases Integration4 minutes
Demonstration: Tracking API Costs and Token Usage 4 minutes

5 readingsTotal 70 minutes

Welcome to Optimizing and Deploying LLM Systems15 minutes
Cost and Latency Optimization Guide15 minutes
Prompt Compression and Evaluation Metrics15 minutes
LLMOps Evaluation Frameworks15 minutes
Summary of Scaling and Optimizing LLM Pipelines10 minutes

4 assignmentsTotal 48 minutes

Knowledge Check: Scaling and Optimizing LLM Pipelines30 minutes
Practice Quiz: Performance Optimization Fundamentals6 minutes
Practice Quiz: Prompt and Chain Optimization6 minutes
Practice Quiz: Evaluating and Monitoring Pipelines6 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself10 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 videosTotal 47 minutes

Power of APIs in LLMs5 minutes
Demonstration: Connecting Multiple External APIs3 minutes
Demonstration: Event-Driven Pipeline with Webhooks and Queues 4 minutes
Combining Structured and Unstructured Data5 minutes
Demonstration:Natural-Language to SQL with LangChain and OpenAI4 minutes
Demonstration: Hybrid Retrieval Using LLM and LangChain5 minutes
Data Indexing and Workflow Orchestration5 minutes
Demonstration: Complex Data Pipeline with LlamaIndex6 minutes
Demonstration: Automated Evaluation Workflow with LangGraph and LLM5 minutes

4 readingsTotal 55 minutes

Secure API Integration and Governance15 minutes
Multi-Modal Data Fusion15 minutes
Combining Multiple Data Sources for Reasoning15 minutes
Summary of Integrating APIs and External Data Sources10 minutes

4 assignmentsTotal 48 minutes

Knowledge Check: Integrating APIs and External Data Sources30 minutes
Practice Quiz: API-Driven LLM Workflows6 minutes
Practice Quiz: Structured and Multi-Modal Data Integration6 minutes
Practice Quiz: Data Orchestration with LlamaIndex and LangGraph6 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 videosTotal 77 minutes

From Development to Production — API Design5 minutes
Demonstration: Creating REST Endpoints with FastAPI for LangChain Workflows4 minutes
Demonstration: Adding Auth (JWT/OAuth) and Rate Limiting7 minutes
Containerization Essentials for AI Apps5 minutes
Demonstration: Dockerize LangChain + FastAPI App4 minutes
Demonstration: Deployment of API on AWS7 minutes
Capstone Overview: LLM Orchestrator5 minutes
Demonstration: Capstone Project Overview and Architecture6 minutes
Demonstration: Building LLM APIs with FASTAPI7 minutes
Demonstration: Authentication and Analytics Integration6 minutes
Demonstration: Data Pipeline and Docker Setup5 minutes
Demonstration: Automating Deployment with CI/CD5 minutes
Demonstration: Cloud Deployment and Frontend Setup6 minutes

3 readingsTotal 45 minutes

Secure API Architecture15 minutes
Secrets and Environment Configurations in Cloud15 minutes
Summary of Deploying and Managing LLM Applications15 minutes

4 assignmentsTotal 48 minutes

Deployed LLM System Evaluation Report30 minutes
Practice Quiz: Building an LLM API Service6 minutes
Practice Quiz: Containerization and Cloud Deployment6 minutes
End-to-End LLM System Deployment6 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

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

111 Courses112,567 learners

Offered by

Edureka

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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.

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.