Gen AI - Advanced
  • AI

Data Analyst

  • Breitskills By Breitskills
  • 01/11/2024
  • 90

Course Description

​Generative AI (Advanced Level) focuses on leveraging and enhancing AI models capable of creating human-like content, such as text, images, audio, and video. It involves a deep understanding of advanced concepts like transformer architectures (e.g., GPT and DALL-E), fine-tuning pre-trained models for specific tasks, and training custom models using large datasets.​

Mastery of Transformer Architectures (e.g., GPT, BERT, DALL-E). Custom Model Training and Fine-Tuning. Advanced Optimization Techniques. Ethical AI and Bias Mitigation. Applications in Content Creation and Automation. Handling Large-Scale Datasets. Real-World Deployment and Performance Tuning.

Course Syllabus

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  1. Overview of Generative AI: Define generative AI, its significance, and applications.
  2. History of Language Models: From symbolic NLP to neural NLP and state-of-the-art models.

  1. What Are LLMs?
    o     Basics of LLMs, their applications, and how they work.
    o     Comparing human thought with LLM processing.
  2. How LLMs Work
    o     Tokenization, patterns, and repetitions.
  3. Model Behavior
    o     Hallucinations in LLMs.
    o     Temperature and probabilities in output generation

  1. Basics of Prompt Engineering: Crafting effective prompts for desired outputs.
  2. Techniques and Strategies

  1. Designing LLM Applications
            Identifying user problems and converting them to model domains.
            Using LLMs to complete prompts and transform back to user space.
  2. Quality Evaluation
            Online and offline evaluation of LLM application quality.
            Reinforcement Learning from Human Feedback (RLHF).
  3. Incorporating Frameworks and Tools
            Programming

  1. Programming Frameworks:
            LangChain, LLMAIndex: Using open-source tools for building LLM applications.
            Anarchy: Another open-source tool for development.
  2. GUI Frameworks:
            Flowise: Open-source GUI framework for LLM applications.
            Stack AI: Commercial GUI framework for advanced applications.
  3. Monitoring Tools:
            Autoblocks, Helicone, HoneyHive, LangSmith, Weights & Biases: For monitoring  and managing LLM applications.
  4. Caching Tools:
            GPTCache, Redis: For efficient data handling and storage.
            Validation Tools:
            Guardrails AI, Rebuff: Ensuring quality and reliability of LLM outputs.

  1. Overview
  2. Key Components of LangChain
            Agents: Execute tasks and actions based on model outputs.
            Chains: Sequences of tasks or prompts executed in a defined order.
            Memory: Enables context retention across interactions.
            Tools: Various tools for data retrieval, transformation, and more.

  1. Agents, Multi Agents, RAG, Vector Databases

  1. Strategies to mitigate and manage incorrect or nonsensicaloutputs.

  1. Techniques for improving efficiency and effectiveness of LLM applications.

  1. Best practices for deploying LangChain applications, including CI/CD pipelines and monitoring frameworks for observability and maintenance.

  1. Fine-tuning
            Fine-tuning models for specific tasks.
  2. Model Evaluation
            Evaluating the performance and effectiveness of fine-tuned models

  1. Observability Frameworks:
            Implement monitoring to gain insights into model performance and        behavior.
            Use tools like NVIDIA Guardrails, Weights & Biases, LangSmith, Helicone for observability.
  2. Testing Strategies:
            Employ robust testing practices for LLM applications.
            Tools like LangSmith for comprehensive testing and validation.

  1. Deployment Strategies:
            Best practices for deploying LLM applications.
            Use tools like LangServe to manage model deployment and scaling.
  2. Operationalization (LLMOPs):
            Implementing continuous integration and deployment (CI/CD) pipelines.
            Utilize tools for automated deployment and scaling.

  1. Reinforcement Learning with Human Feedback (RLHF)
        Implementing RLHF in LLMs for improved performance.
  2. Model Optimization for Deployment
        Techniques for optimizing models for real-world applications.
  3. Using LLMs in Applications
        Practical applications of LLMs in various domains.
Breitskills

Breitskills

Tutor has an experienced in Gen AI with a passion for teaching and practical industry insights, they excels in helping students grasp complex AI concepts through hands-on learning and real-world case studies.

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Course includes:
  • Lessons 12
  • Duration 60 hours
  • Language English - Tamil - Telugu
  • Certifications Yes
  • Resource Access Breitskills
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