Generative AI For Advance

   - Introduction to Hugging Face

     - Overview of the Hugging Face ecosystem (Transformers library, model hub)

     - Hugging Face vs. OpenAI: Differences in use cases and flexibility

   - Pretrained Models and Transfer Learning

     - How to use pretrained models from Hugging Face

     - Transfer learning for task-specific models

   - Pipeline Usage in Hugging Face

     - Text classification, summarization, translation, and question answering

     - Handling large datasets with Hugging Face Datasets library

   - Hands-on: Deploying a Hugging Face model for text generation or classification

   - Introduction to LangChain

     - What is LangChain? Why use it for LLM applications?

     - Key components of LangChain (Chains, Agents, Memory)

   - Building Custom Workflows with LangChain

     - Creating custom pipelines for NLP tasks

     - Combining multiple LLMs with LangChain to optimize outputs

   - Memory Management in LangChain

     - Managing conversation history and long-term memory

     - Using memory in real-world chatbot applications

   - Hands-on: Implement a LangChain-based project for conversational agents

- Introduction to RAG

     - What is RAG, and why is it crucial for information retrieval?

     - Comparison of RAG with traditional retrieval techniques

   - Architecture of RAG

     - Overview of the retriever and generator components

     - Fine-tuning RAG for specific use cases

   - Practical Applications of RAG

     - Document retrieval in enterprise search engines

     - Personalized recommendations and dynamic content generation

   - Hands-on: Implementing RAG in an information retrieval application

   - Understanding Fine-Tuning

     - Difference between pretraining, transfer learning, and fine-tuning

     - Why fine-tuning is essential for specific business applications

   - Fine-Tuning Hugging Face Models

     - Preparing datasets for fine-tuning

     - Training with custom datasets using Hugging Face models

     - Evaluation metrics and model optimization

   - Hands-on: Fine-tuning a Hugging Face transformer model for a business problem

   - Introduction to Vertex AI

     - Overview of Vertex AI and Google Cloud’s AI capabilities

     - Key services in Vertex AI (AutoML, Model Registry, Model Monitoring)

   - Deploying Models on Vertex AI

     - Steps for deploying models on Vertex AI

     - Best practices for scalable and robust model deployment

   - Tuning and Optimizing Deployed Models

     - Hyperparameter tuning and automatic retraining

     - Monitoring model performance and drift detection

   - Hands-on: Deploy and tune a custom model using Vertex AI

- Overview of Vertex AI Co-pilot

     - What is Vertex AI Co-pilot and its advantages?

     - How Co-pilot supports developers and data scientists

   - Co-pilot Use Cases

     - Automated model generation

     - Real-time collaboration and troubleshooting with Co-pilot

   - Integrating Co-pilot in the AI Lifecycle

     - Using Co-pilot for end-to-end model creation and deployment

     - Automating repetitive tasks and increasing efficiency

   - Hands-on: Explore real-world scenarios using Vertex AI Co-pilot

   - Introduction to Advanced Prompt Engineering

     - Moving beyond basic prompts: conditional prompts, adaptive responses

     - Creating business-specific prompts for customer service, sales, etc.

   - Optimizing Prompts for Business Outcomes

     - Measuring prompt performance: accuracy, relevance, efficiency

     - Adjusting prompts based on real-time feedback

   - Building Reusable Prompt Libraries

     - Developing domain-specific prompt templates for business tasks

     - Collaborating and sharing prompt libraries across teams

   - Hands-on: Advanced prompt design for a real-world business problem

   - Real-time Processing and AI

     - Why real-time AI is important for business applications

     - Differences between batch processing and real-time inference

   - Integrating AI Models with Live Data Streams

     - Connecting Vertex AI models with data pipelines (e.g., Google Pub/Sub)

     - Managing latency, concurrency, and throughput for real-time AI applications

   - Hands-on: Create a real-time AI application using Vertex AI

- Capstone Overview

     - Define a project that integrates the tools and concepts from the course

     - Example project: Build a conversational AI system integrated with real-time data

   - Project Execution

     - Defining problem statements and objectives

     - Developing the AI solution using OpenAI, Hugging Face, LangChain, and Vertex AI

   - Presentation and Evaluation

     - Presenting the solution to peers and stakeholders

     - Discussing potential improvements and scaling strategies