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LLM Foundations: Building Effective Applications for Enterprises

LLM Foundations: Building Effective Applications for Enterprises

1h 44mAdvanced2024-06-21

Authors

Kumaran Ponnambalam

Kumaran Ponnambalam

Working with data for 20+ years

Course details

As generative AI models have become increasingly popular, enterprises have started to build end-to-end applications to integrate their existing workflows with generative AI. In this course, instructor Kumaran Ponnambalam shows you how to get up and running with integration, performance management, trust, and monitoring to deliver effective and trustworthy generative AI applications at scale.

Explore some of the unique characteristics and use cases for generative AI-powered applications in an enterprise setting, including available options, selection criteria, and key deployment considerations for generative AI models. Kumaran covers the basics of evaluating and fine-tuning models as well as patterns and best practices for core application design. By the end of this course, you’ll also be equipped with new skills to manage application performance, maintain safety and trust, and navigate some of the most important ethical and legal challenges of AI.

Skills covered

Natural Language Processing (NLP)PythonSoftware Development ToolsFoundationsArtificial Intelligence (AI)Open SourceSoftware Development

Concepts

0. Introduction

  • 01 - Starting your GenAI adoption journey
  • 02 - Course content and prerequisites

1. Generative AI in Enterprises

  • 03 - The GenAI revolution
  • 04 - How GenAI is impacting enterprises
  • 05 - GenAI challenges for enterprises
  • 06 - GenAI adoption process

2. GenAI Use Case Selection

  • 07 - Popular GenAI use cases
  • 08 - Identifying and evaluating GenAI use cases
  • 09 - Creating a GenAI roadmap
  • 10 - Building proof-of-concepts for GenAI
  • 11 - Course use case - GenAI-powered email helpdesk

3. Choosing GenAI Models

  • 12 - The GenAI model landscape
  • 13 - Selection criteria for GenAI models
  • 14 - Build vs. buy for GenAI
  • 15 - GenAI model deployment considerations
  • 16 - Impact of training data in GenAI

4. Evaluation and Fine-Tuning for GenAI

  • 17 - Evaluating GenAI models
  • 18 - Fine-tuning GenAI models
  • 19 - Building datasets for evaluation and fine-tuning
  • 20 - Metrics for GenAI
  • 21 - Course use case - Evaluation and model selection

5. Design Considerations for GenAI Applications

  • 22 - A typical GenAI app
  • 23 - Batch generation with GenAI models
  • 24 - User and API interfaces
  • 25 - Prompt engineering
  • 26 - Data collection and monitoring
  • 27 - Course use case - Architecture

6. Safety and Trust with GenAI

  • 28 - Ethical and legal considerations
  • 29 - Protecting against vulnerabilities
  • 30 - Toxicity and bias in GenAI
  • 31 - Hallucinations
  • 32 - Course use case - Guardrails

7. Managing GenAI Application Performance

  • 33 - Performance goals for GenAI apps
  • 34 - Improving GenAI accuracy
  • 35 - Reducing latency for GenAI
  • 36 - GenAI cost control
  • 37 - Course use case - Performance considerations

Conclusion

  • 38 - Continue your GenAI journey

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