LLM Foundations: Building Effective Applications for Enterprises

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Kumaran Ponnambalam

1:43:26

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01 - Introduction

01 - Starting your GenAI adoption journey.mp4

00:40

02 - Course content and prerequisites.mp4

01:51

02 - 1. Generative AI in Enterprises

01 - The GenAI revolution.mp4

01:56

02 - How GenAI is impacting enterprises.mp4

02:15

03 - GenAI challenges for enterprises.mp4

02:32

04 - GenAI adoption process.mp4

01:39

03 - 2. GenAI Use Case Selection

01 - Popular GenAI use cases.mp4

02:58

02 - Identifying and evaluating GenAI use cases.mp4

04:07

03 - Creating a GenAI roadmap.mp4

03:35

04 - Building proof-of-concepts for GenAI.mp4

03:05

05 - Course use case GenAI-powered email helpdesk.mp4

02:36

04 - 3. Choosing GenAI Models

01 - The GenAI model landscape.mp4

04:12

02 - Selection criteria for GenAI models.mp4

02:34

03 - Build vs. buy for GenAI.mp4

02:32

04 - GenAI model deployment considerations.mp4

04:33

05 - Impact of training data in GenAI.mp4

02:08

05 - 4. Evaluation and Fine-Tuning for GenAI

01 - Evaluating GenAI models.mp4

04:38

02 - Fine-tuning GenAI models.mp4

03:09

03 - Building datasets for evaluation and fine-tuning.mp4

02:54

04 - Metrics for GenAI.mp4

03:05

05 - Course use case Evaluation and model selection.mp4

03:22

06 - 5. Design Considerations for GenAI Applications

01 - A typical GenAI app.mp4

01:53

02 - Batch generation with GenAI models.mp4

03:06

03 - User and API interfaces.mp4

04:27

04 - Prompt engineering.mp4

02:06

05 - Data collection and monitoring.mp4

02:01

06 - Course use case Architecture.mp4

03:36

07 - 6. Safety and Trust with GenAI

01 - Ethical and legal considerations.mp4

03:44

02 - Protecting against vulnerabilities.mp4

04:23

03 - Toxicity and bias in GenAI.mp4

03:37

04 - Hallucinations.mp4

02:36

05 - Course use case Guardrails.mp4

01:58

08 - 7. Managing GenAI Application Performance

01 - Performance goals for GenAI apps.mp4

02:53

02 - Improving GenAI accuracy.mp4

01:22

03 - Reducing latency for GenAI.mp4

01:32

04 - GenAI cost control.mp4

01:24

05 - Course use case Performance considerations.mp4

01:32

09 - Conclusion

01 - Continue your GenAI journey.mp4

00:55

Description

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.

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Artificial Intelligence

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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