What are the prerequisites for this course?

Basic knowledge of Python, APIs, and machine learning.

What topics are covered in the course?

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

How long is the course duration?

Around 4–6 weeks across three main modules.

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.

When will I have access to the lectures and assignments?

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.

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

kurs ist nicht verfügbar in Deutsch (Deutschland)

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Optimizing and Deploying LLM Systems

Dieser Kurs ist Teil von Spezialisierung „Building LLMs with Hugging Face and LangChain“

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Continuous Deployment
Kategorie: Performance Analysis
Kategorie: Artificial Intelligence
Kategorie: Authentications
Kategorie: Containerization
Kategorie: Application Deployment
Kategorie: Token Optimization
Kategorie: Large Language Modeling
Kategorie: Continuous Integration
Kategorie: LLM Application
Kategorie: Retrieval-Augmented Generation
Kategorie: Prompt Patterns

Werkzeuge, die Sie lernen werden

Kategorie: LangGraph
Kategorie: Amazon Web Services
Kategorie: Application Programming Interface (API)
Kategorie: Model Deployment
Kategorie: OpenAI
Kategorie: Prompt Engineering
Kategorie: LangChain
Kategorie: Postman API Platform

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

November 2025

Bewertungen

13 Aufgaben

Unterrichtet in Englisch

91% of learners achieved a positive career outcome

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Building LLMs with Hugging Face and LangChain“

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In diesem Kurs gibt es 4 Module

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.

Moduldetails

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.

Das ist alles enthalten

11 Videos5 Lektüren4 Aufgaben1 Diskussionsthema

11 VideosInsgesamt 54 Minuten

Specialization Introduction6 Minuten
Course Introduction5 Minuten
Why Optimization Matters in LLM Systems6 Minuten
Demonstration: Profiling Response Latency and Token Usage in LangChain App3 Minuten
Demonstration: Implement Async Batching and Caching 4 Minuten
Efficient Prompts for Reliability and Speed6 Minuten
Demonstration: Dynamic Prompts and Templates for Better Control4 Minuten
Demonstration: Implement Prompt Caching and Auto-Tuning 5 Minuten
Evaluating Model Output Quality6 Minuten
Demonstration: LangSmith + Weights and Biases Integration4 Minuten
Demonstration: Tracking API Costs and Token Usage 4 Minuten

5 LektürenInsgesamt 70 Minuten

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

4 AufgabenInsgesamt 48 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Introduce Yourself10 Minuten

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.

Das ist alles enthalten

9 Videos4 Lektüren4 Aufgaben

9 VideosInsgesamt 48 Minuten

Power of APIs in LLMs6 Minuten
Demonstration: Connecting Multiple External APIs3 Minuten
Demonstration: Event-Driven Pipeline with Webhooks and Queues 5 Minuten
Combining Structured and Unstructured Data6 Minuten
Demonstration:Natural-Language to SQL with LangChain and OpenAI4 Minuten
Demonstration: Hybrid Retrieval Using LLM and LangChain6 Minuten
Data Indexing and Workflow Orchestration6 Minuten
Demonstration: Complex Data Pipeline with LlamaIndex6 Minuten
Demonstration: Automated Evaluation Workflow with LangGraph and LLM6 Minuten

4 LektürenInsgesamt 55 Minuten

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

4 AufgabenInsgesamt 48 Minuten

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

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.

Das ist alles enthalten

13 Videos3 Lektüren4 Aufgaben

13 VideosInsgesamt 78 Minuten

From Development to Production — API Design6 Minuten
Demonstration: Creating REST Endpoints with FastAPI for LangChain Workflows4 Minuten
Demonstration: Adding Auth (JWT/OAuth) and Rate Limiting7 Minuten
Containerization Essentials for AI Apps6 Minuten
Demonstration: Dockerize LangChain + FastAPI App5 Minuten
Demonstration: Deployment of API on AWS7 Minuten
Capstone Overview: LLM Orchestrator5 Minuten
Demonstration: Capstone Project Overview and Architecture7 Minuten
Demonstration: Building LLM APIs with FASTAPI7 Minuten
Demonstration: Authentication and Analytics Integration6 Minuten
Demonstration: Data Pipeline and Docker Setup5 Minuten
Demonstration: Automating Deployment with CI/CD5 Minuten
Demonstration: Cloud Deployment and Frontend Setup6 Minuten

3 LektürenInsgesamt 45 Minuten

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

4 AufgabenInsgesamt 48 Minuten

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

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.