This chapter provides an introduction to LLMOps and the ideation phase of LLM application development. It explains the basics of LLMOps, highlights the lifecycle stages of LLMs, and covers key activities in the ideation phase, such as data sourcing and selecting between open-source and proprietary LLMs.

Overview of LLMOps

Options

LLMOps

MLOps

LLMOps versus MLOps

Relevancy of LLMOps

Lifecycle of LLMs

The purpose of lifecycle phases

Ideation phase

Development phase

Operational phase

The application lifecycle

Data sourcing

Proprietary

Open-source

Proprietary versus open-source models

Selecting the right base model

Introduction to LLMOps & Ideation Phase

This chapter focuses on the development phase of LLM application creation. It covers prompt engineering, agents and chains, RAG versus fine-tuning techniques, and testing methods. 

Prompt engineering

The importance of prompt engineering

Trying out prompt engineering

Keeping track of prompts

Chains and agents

The difference between agents and chains

Choosing the right architecture

RAG versus fine-tuning

The RAG workflow

Compare RAG with fine-tuning

Testing

Choosing the right metric

The importance of testing

Development Phase

This chapter focuses on the operational phase of LLM application deployment and management. We will cover deployment strategies like CI/CD, scaling techniques, monitoring practices, cost management strategies, and governance and security considerations. Mastering these concepts will help you efficiently manage LLM applications in operational environments.

Deployment

The need for CI/CD

The right scaling strategy

Monitoring and observability

Monitoring your application

Alert handling

Cost management

Prompt compression

Making a cost prognosis

Governance and security

Prompt injection

Mitigation strategies

Data integrity and poisoning

Congratulations

Operational Phase

Embark on a journey into Large Language Model Operations (LLMOps). This course, designed for enthusiasts and professionals alike, guides you through the basics, from ideation to operational deployment.

Delving into the LLM application lifecycle will reveal its pivotal role in organizations. You will gain insights into the challenges and considerations at each stage and how to refine development and ensure smooth deployment while embracing data governance and security. By the end of the course, you'll have a solid grasp of LLMOps concepts and know how to apply them to your applications.

<h2> An Introduction to LLMOps </h2>

Embark on a journey into Large Language Model Operations (LLMOps). This course, designed for enthusiasts and professionals alike, guides you through the basics, from ideation to operational deployment.

<h2> Unlock the Essentials </h2>

Begin your exploration by unraveling the fundamentals of LLMOps. Understand its pivotal role within organizational landscapes and grasp the core concepts of LLMs and LLMOps. The primary focus of this course isn't on constructing your own foundational LLM. Instead, it's geared towards leveraging the capabilities of a foundational model for application within your organization.

<h2> Navigate the Lifecycle </h2>

Delving into the LLM application lifecycle will reveal its pivotal role in organizations. You will gain insights into the challenges and considerations at each stage and how to refine development and ensure smooth deployment while embracing data governance and security. 

After completing this course, you'll not only gain a solid grasp of LLMOps, but also be able to confidently apply these concepts in your work. Whether you're just starting out or eager to deepen your understanding, this course will boost your skills and guide you toward mastering LLMOps.

Large Language Models (LLMs) Concepts

Learn about LLMOps from ideation to deployment, gain insights into the lifecycle and challenges, and learn how to apply these concepts to your applications.

LLMOps Concepts | From Ideation to Deployment

LLMOps Concepts

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Models with high unpredictability due to size and complexity

General-purpose models capable of handling a wide range of tasks and domains

Using prompt engineering and fine-tuning to enhance model performance

Dealing with lightweight task-specific models

Using model architectures tailored to specific data characteristics and tasks

LLMOps versus MLOps

LLMOps Concepts

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