What will you in Complete Generative AI Course With Langchain and Huggingface Course
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Create advanced generative AI applications using the Langchain framework and Huggingface’s state-of-the-art models.
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Understand architecture and design patterns for building robust, production-ready generative AI systems.
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Deploy generative AI models to cloud platforms and on-premise servers, ensuring scalability and reliability.
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Build Retrieval-Augmented Generation (RAG) pipelines to enhance model performance via integrated retrieval mechanisms.
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Seamlessly integrate and fine-tune Huggingface pre-trained models within Langchain applications for custom NLP tasks.
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Optimize and monitor deployed AI systems with best practices for maintenance, updating, and performance tuning.
Program Overview
Module 1: Introduction to Generative AI & Langchain Basics
⏳ 30 minutes
- Explore generative AI fundamentals and set up your Langchain development environment.
- Understand Langchain’s core abstractions: chains, agents, and prompts.
Module 2: Langchain Architecture & Design Patterns
⏳ 45 minutes
- Dive into Langchain design patterns for modular, maintainable AI systems.
- Apply patterns like toolkit chains, map-reduce, and sequential chaining.
Module 3: Huggingface Integration & Pre-trained Models
⏳ 1 hour
- Load and serve Huggingface models within Langchain pipelines.
- Leverage tokenizers, pipelines, and transformers for NLP tasks.
Module 4: Fine-tuning Huggingface Models
⏳ 45 minutes
- Customize pre-trained models on your own datasets using optimum and Trainer APIs.
- Evaluate model performance and mitigate overfitting.
Module 5: Building RAG Pipelines
⏳ 45 minutes
- Integrate vector databases (e.g., FAISS, Pinecone) with Langchain.
- Implement document retrieval, embedding, and generative responses.
Module 6: Deployment Strategies
⏳ 1 hour
- Deploy models to cloud services (AWS, Azure, GCP) and on-premise servers.
- Containerize applications with Docker and orchestrate with Kubernetes.
Module 7: Optimization & Monitoring
⏳ 30 minutes
- Set up logging, performance metrics, and health checks.
- Apply quantization, distillation, and caching to reduce latency.
Module 8: Capstone Projects & Real-World Applications
⏳ 1 hour
- Build end-to-end projects: chatbots, content generators, and data augmentation tools.
- Review best practices and lessons learned from production deployments.
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Job Outlook
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High-Demand Roles: Generative AI Engineer, NLP Engineer, Machine Learning Engineer.
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Salary Potential: ₹8–22 LPA in India; $90K–$140K annually in the U.S.
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Growth Areas: AI-driven applications, LLM integrations, conversational AI, and AI-powered automation.
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Career Impact: Expertise in Langchain and Huggingface positions you for cutting-edge roles in startups, enterprise AI teams, and research labs.
Explore More Learning Paths
Deepen your expertise in generative AI, agentic systems, and large-scale language model development with these advanced programs designed to elevate your technical capabilities and accelerate your AI career.
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