Building Local AI/LLM using Langchain and CrawAI Training Course
This program offers a practical, hands-on methodology for creating local AI and LLM-driven applications using LangChain and CrawAI. Learners will first examine data analytics, machine learning, and model training strategies before progressing to the development of LLM-based solutions.
The curriculum addresses critical subjects including multi-model AI systems, no-code and low-code AI development, Retrieval-Augmented Generation (RAG), agentic AI, and AI automation via CrawAI. Students will also acquire the skills to construct AI chatbots, AI-enabled knowledge bases, and offline/local AI models, ensuring both privacy and operational efficiency.
Upon completing the course, participants will be able to:
- Grasp the core principles of data analytics, machine learning, and AI models.
- Train, fine-tune, and optimize local AI and LLM models.
- Develop LangChain applications for AI-driven automation.
- Construct multi-model AI and LLM-powered applications.
- Apply no-code and low-code solutions to expedite AI development.
- Create local/offline AI chatbots and intelligent knowledge bases.
- Implement AI agents and automate workflows using CrawAI.
- Develop Retrieval-Augmented Generation (RAG) applications to improve AI response quality.
This course is ideally suited for professionals aiming to develop private, offline, or on-premises AI solutions, automate workflows, and build advanced AI-driven applications leveraging LangChain and CrawAI.
This course is available as onsite live training in Uzbekistan or online live training.Course Outline
- Data Analytics and Machine Learning
- Model Training and Tuning
- Introduction to LLM
- Introduction to LangChain
- Building LangChain Apps
- Building Multi-Model AI Apps
- Building AI, LLM Based Apps
- Using No/Low Code
- Building Local/Offline AI bots
- Building RAG Apps
- Introduction to Agentic AI
- Building AI Agents
- Automating Tasks using CrawAI
- Building AI local Knowledge Base
Participants will gain practical experience with industry-leading AI, LLM, and automation tools, including:
- LangChain – Framework for AI-powered applications.
- CrawAI – AI automation and task execution platform.
- Python (with NumPy, Pandas, Scikit-learn) – Data processing and analytics.
- Hugging Face Transformers – Pre-trained LLM models.
- ChromaDB & FAISS – Vector databases for knowledge bases.
- LLMs (Llama, GPT, Falcon, Mistral, or open-source models) – Model experimentation and deployment.
- No-code/Low-code AI platforms – For rapid AI app development.
- Local/Offline AI setups (like PrivateGPT, Ollama, LM Studio) – For building AI applications that run without internet dependency.
This course provides significant benefits, making it an essential skill-building opportunity for AI professionals and businesses:
- Build Private & Local AI Solutions – Develop AI applications that operate without cloud dependency.
- Hands-on LangChain & CrawAI Experience – Gain expertise in LLM application development and automation.
- Learn to Automate Workflows – Use AI agents to handle repetitive tasks and enhance productivity.
- Develop Secure, On-Premises AI Apps – Create self-hosted AI solutions for privacy and security-sensitive industries.
- Accelerate AI Development with No-Code/Low-Code – Learn to rapidly prototype AI solutions without extensive coding.
- Improve AI Model Performance – Train and fine-tune custom AI models for specialized tasks.
- Master RAG (Retrieval-Augmented Generation) – Build context-aware AI that improves knowledge retrieval.
- Enhance Career Opportunities – AI and automation skills are highly in-demand, offering a competitive edge.
This course is a must-attend for professionals looking to master AI-driven automation, private AI applications, and cutting-edge LLM-powered workflows.
Requirements
To maximize the benefits of this course, participants should possess:
- Basic Python programming proficiency.
- A foundational understanding of machine learning and AI concepts.
- Some experience with data processing, APIs, or cloud platforms (recommended, though not mandatory).
- Familiarity with SQL or NoSQL databases (optional but beneficial for constructing knowledge bases).
- Accounts on platforms such as Hugging Face and Github/Gitlab.
For those focusing on fully local/offline AI applications, the following are also required:
- A local machine with adequate GPU or CPU capabilities to run AI models.
- Offline model storage solutions (e.g., HF Hub, Ollama, LM Studio) for utilizing LLMs locally.
This course is tailored for developers, data scientists, AI engineers, and professionals interested in building local AI and LLM-powered applications. It is particularly advantageous for:
- Software Engineers & AI Developers – To build AI-powered applications using LangChain and CrawAI.
- Data Scientists & ML Engineers – To fine-tune models and create intelligent knowledge-based AI.
- Enterprise AI Professionals – To develop secure, private, on-premises AI solutions.
- Automation Experts – To automate tasks using AI-powered agents.
- Business Intelligence Professionals – To integrate AI and knowledge bases for analytics.
- Tech Enthusiasts & AI Innovators – To explore new methods of leveraging local AI for automation and efficiency.
Need help picking the right course?
uzbekistan@nobleprog.com or +919818060888
Building Local AI/LLM using Langchain and CrawAI Training Course - Enquiry
Building Local AI/LLM using Langchain and CrawAI - Consultancy Enquiry
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