Generative AI and LLMOps: Building Blocks and Applications

Contents

• Introduction Introduction

  • LLMs for industry

    52s
  • Industry-specific LLMs

    2m 59s
• 1. Introduction to LLMs and Their Applications 1. Introduction to LLMs and Their Applications

  • Understanding LLMs and their evolution

    4m 28s
  • (Locked)
    Real-world applications of LLMs

    2m 37s
• 2. Diving Into LLM Architectures 2. Diving Into LLM Architectures

  • (Locked)
    Overview of LLM architectures

    3m 15s
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    How LLMs process and generate text

    2m 47s
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    The building blocks of LLMs

    3m 18s
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    Using a simple LLM

    3m 19s
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    Challenge: LLM for sentiment analysis

    1m 43s
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    Solution: LLM for sentiment analysis

    1m 27s
• 3. Fine-Tuning Strategies for Custom Tasks 3. Fine-Tuning Strategies for Custom Tasks

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    Introduction to fine-tuning for LLMs

    3m 19s
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    Step-by-step guide to fine-tuning LLMs

    3m 57s
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    Best practices for fine-tuning LLMs

    2m 22s
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    Challenge: Fine-tune a pre-trained LLM

    2m 7s
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    Solution: Fine-tune a pre-trained LLM

    2m 44s
• 4. Compression Techniques for LLMs 4. Compression Techniques for LLMs

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    Why compress LLMs?

    2m 57s
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    Introduction to quantization and pruning

    7m 4s
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    Hands-on: Implementing compression in LLMs

    2m 39s
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    Challenge: Quantize a LLM

    1m 38s
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    Solution: Quantize a LLM

    1m 49s
• 5. Prompt Engineering for Effective LLM Communication 5. Prompt Engineering for Effective LLM Communication

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    What is prompt engineering?

    3m 42s
  • Best practices for effective prompt engineering

    6m 12s
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    Types of prompt engineering

    6m 45s
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    Challenge: Prompting LLMs to generate text

    1m 31s
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    Solution: Prompting LLMs to generate text

    5m 8s
• Conclusion Conclusion

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    Next steps

    44s