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

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Optimizing and Deploying LLM Systems

本课程是 Building LLMs with Hugging Face and LangChain 专项课程的一部分

位教师：Edureka

包含在中

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4个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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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本课程是 Building LLMs with Hugging Face and LangChain 专项课程专项课程的一部分

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该课程共有4个模块

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.

涵盖的内容

11个视频5篇阅读材料4个作业1个讨论话题

11个视频总计54分钟

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

5篇阅读材料总计70分钟

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

4个作业总计48分钟

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

1个讨论话题总计10分钟

Introduce Yourself10分钟

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.

涵盖的内容

9个视频4篇阅读材料4个作业

9个视频总计47分钟

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

4篇阅读材料总计55分钟

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

4个作业总计48分钟

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

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.

涵盖的内容

13个视频3篇阅读材料4个作业

13个视频总计77分钟

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

3篇阅读材料总计45分钟

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

4个作业总计48分钟

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

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.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题

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位教师

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Edureka

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常见问题

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.