Full Stack Generative AI Career Path- Beginners

Gain a foundational understanding of Generative AI, including its core concepts and objectives. Explore its applications in various industries such as healthcare, automotive, and retail.

• 1.1 Core Concepts:
  • ‍Understand the basic principles and objectives of Generative AI.
• 1.2 Generative Models:
  • Learn about GANs, VAEs, and autoregressive models.
• 1.3 Applications:
  • Explore real-world applications in healthcare, automotive, and BFSI.
• 1.4 Transformers and LLMs:
  • Introduction to Transformer architecture and Large Language Models, their capabilities, limitations, and impact.
    

Explore what foundation models are, their historical development, and their significance in advancing AI research and applications

• 1.1 Definition and Importance:
  • ‍Understand what foundation models are and their role in AI.
• 1.2 Pretraining:
  • Learn about the objectives, processes, and significance of pretraining.
• 1.3 Fine-tuning
  
  • Understand the differences between pretraining and fine-tuning, including techniques and strategies.

• 1.4 Examples:
  
  • Explore prominent foundation models like GPT, BERT, and T5.

Understand the platforms and APIs available for deploying generative AI models, focusing on their features, capabilities, and use cases.

• 1.1 Popular Platforms:
  • ‍Learn about Google Cloud AI, Microsoft Azure AI, AWS AI, OpenAI, and Hugging Face.

• 1.2 API Integration:
  • Understand the role of APIs in integrating generative AI models
• 1.3 Features and Capabilities:
  • Explore key functionalities and support for various generative models.

• 1.4 Platform Comparison:
  • Evaluate platforms based on performance, scalability, cost, and ease of use.

Gain technical insights into LLMs, their architecture, data preparation techniques, and training processes.

• 1.1 Popular Models:
  • ‍Overview of models like GPT-3, BERT, and T5, and their applications.
• 1.2 Architecture:
  • Understand the key components and architecture of LLMs, especially Transformers.

• 1.3 Data Preparation:
  • Learn strategies for data collection, cleaning, and augmentation.
• 1.4 Training and Tuning:
  • Explore hyperparameter tuning, performance metrics, and training workflows.

Learn about prompt engineering, its significance, and how it enhances the quality and relevance of AI-generated responses.

• 1.1 Core Concepts:
  • ‍Understand what prompt engineering is and its historical contex.

• 1.2 Techniques:
  • Explore methods for creating effective prompts.

• 1.3 Applications:
  • Discover practical applications and case studies demonstrating its impact.

• 1.4 Types of Prompting:
  • Learn about zero-shot, few-shot, and chain-of-thought prompting strategies.

Develop practical skills for creating AI-powered applications, especially chatbots, for the BFSI industry.

• 1.1 Chatbot Development:
  • ‍Steps for defining goals, designing flows, selecting technology, and integrating LLMs.

• 1.2 API Integration:
  • Guide for developing chatbots using API integration and deploying LLMs locally.

• 1.3 Domain-Specific Solutions:
  • Design chatbots for finance and e-commerce.

• 1.4 BFSI Projects:
  • Undertake projects like fraud detection, loan approval assistants, and insurance claim processing.

Understand the role of vector databases and LangChain-like frameworks in AI applications, particularly in the BFSI industry.

• 1.1 Vector Databases:
  • ‍Learn about high-dimensional data storage and performance benefits.

• 1.2 LangChain Framework:
  • Explore its components, features, and capabilities.
• 1.3 Integration:
  • Step-by-step guide to integrating LLMs with vector databases using LangChain.

• 1.4 Case Studies:
  • Practical examples demonstrating improved performance and functionality.

Explore the Retrieval-Augmented Generation (RAG) architecture and techniques for integrating local data sources with LLMs.

• 1.1 RAG Overview:
  • ‍Understand the architecture and its components (retriever and generator).

• 1.2 Benefits:
  • Learn about the advantages of RAG, including improved accuracy and context-awareness.
• 1.3 Data Integration:
  • Techniques for grounding LLMs to local training datasets.

• 1.4 Applications:
  • Case studies in customer support, fraud detection, and risk management in the BFSI sector.