Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
- Application of Data Analytics and Machine Learning Algorithms
- Optimization of Model Training and Hyperparameter Tuning
- Foundational Principles of Large Language Models (LLMs)
- Core Concepts of the LangChain Framework
- Development of LangChain-Based Applications
- Integration of Multi-Model AI Architectures
- Construction of AI and LLM-Driven Systems
- Utilization of No-Code and Low-Code Development Environments
- Deployment of Localized and Offline AI Assistants
- Implementation of Retrieval-Augmented Generation (RAG) Systems
- Foundational Concepts of Agentic AI
- Engineering of Autonomous AI Agents
- Workflow Automation via CrawAI
- Establishment of Secure Local AI Knowledge Repositories
Enrollees will acquire practical proficiency with industry-standard artificial intelligence, large language model, and automation technologies, specifically including:
- LangChain – A structural framework for developing AI-centric applications.
- CrawAI – A platform for executing automated AI tasks and workflows.
- Python (incorporating NumPy, Pandas, and Scikit-learn) – Tools for data processing and analytical modeling.
- Hugging Face Transformers – Libraries containing pre-trained large language models.
- ChromaDB & FAISS – Vector database solutions for managing structured knowledge bases.
- Large Language Models (including Llama, GPT, Falcon, Mistral, and other open-source variants) – Resources for model experimentation and deployment.
- No-Code and Low-Code AI Platforms – Tools for expedited application development.
- Local and Offline AI Configurations (such as PrivateGPT, Ollama, and LM Studio) – Infrastructure for deploying AI applications independent of external internet connectivity.
This curriculum provides substantial value, serving as a critical capacity-building initiative for technical specialists and organizational stakeholders for government and other public sector entities:
- Development of Private and Localized AI Solutions – Engineering of AI applications that operate without reliance on external cloud infrastructure.
- Practical Proficiency in LangChain and CrawAI – Acquisition of specialized expertise in LLM application architecture and process automation.
- Automation of Operational Workflows – Application of AI agents to manage routine tasks and enhance operational efficiency.
- Creation of Secure, On-Premises AI Systems – Establishment of self-hosted AI solutions suitable for sectors with stringent privacy and security requirements.
- Expedition of AI Development via No-Code and Low-Code Tools – Rapid prototyping of AI solutions without extensive programming effort.
- Optimization of AI Model Performance – Training and fine-tuning of custom AI models for specialized operational objectives.
- Implementation of Retrieval-Augmented Generation (RAG) – Construction of context-aware systems that enhance the accuracy of knowledge retrieval.
- Expansion of Professional Competencies – Acquisition of high-demand AI and automation skills that provide a strategic advantage for government and public sector roles.
This curriculum is essential for professionals seeking to master AI-driven automation, secure private AI applications, and advanced LLM-powered workflows for government operations.
Requirements
To derive maximum benefit from this curriculum, participants are expected to possess:
- Proficiency in basic Python programming.
- A foundational understanding of machine learning principles and AI concepts.
- Prior experience with data processing, API integration, or cloud platforms (recommended but not mandatory).
- Knowledge of SQL or NoSQL database systems (optional but beneficial for the construction of knowledge bases).
- Active accounts on Hugging Face and GitHub or GitLab.
For the implementation of fully local and offline AI applications, participants will also require:
- A local computing environment with sufficient GPU or CPU processing power to execute AI models.
- Local storage of offline models (such as those via Hugging Face Hub, Ollama, or LM Studio) for local LLM deployment.
This curriculum is designed for software developers, data scientists, AI engineers, and other professionals seeking to build local AI and LLM-powered applications. It is particularly relevant for:
- Software Engineers and AI Developers – For the construction of AI-powered applications utilizing LangChain and CrawAI.
- Data Scientists and ML Engineers – For the fine-tuning of models and the development of intelligent knowledge-based AI systems.
- Enterprise AI Professionals – For the development of secure, private, and on-premises AI solutions for government and public sector compliance.
- Automation Specialists – For the automation of operational tasks using AI-powered agents.
- Business Intelligence Professionals – For the integration of AI and knowledge bases to enhance analytical capabilities.
- Technical Innovators – For the exploration of new methods to leverage local AI for automation and organizational efficiency.
28 Hours
