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LLMOps 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.

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3 hours12 videos37 exercises

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Course Description

An Introduction to LLMOps

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.

Unlock the Essentials

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.

Navigate the Lifecycle

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.
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In the following Tracks

Associate AI Engineer for Developers

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  1. 1

    Introduction to LLMOps & Ideation Phase

    Free

    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.

    Play Chapter Now
    Overview of LLMOps
    50 xp
    LLMOps versus MLOps
    100 xp
    Relevancy of LLMOps
    50 xp
    Lifecycle of LLMs
    50 xp
    The purpose of lifecycle phases
    50 xp
    The application lifecycle
    100 xp
    Ideation phase
    50 xp
    Data sourcing
    50 xp
    Proprietary versus open-source models
    100 xp
    Selecting the right base model
    50 xp
  2. 2

    Development 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.

    Play Chapter Now
    Prompt engineering
    50 xp
    The importance of prompt engineering
    50 xp
    Trying out prompt engineering
    50 xp
    Keeping track of prompts
    50 xp
    Chains and agents
    50 xp
    The difference between agents and chains
    100 xp
    Choosing the right architecture
    50 xp
    RAG versus fine-tuning
    50 xp
    The RAG workflow
    100 xp
    Compare RAG with fine-tuning
    100 xp
    Testing
    50 xp
    Choosing the right metric
    100 xp
    The importance of testing
    50 xp
  3. 3

    Operational 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.

    Play Chapter Now
    Deployment
    50 xp
    The need for CI/CD
    50 xp
    The right scaling strategy
    100 xp
    Monitoring and observability
    50 xp
    Monitoring your application
    100 xp
    Alert handling
    50 xp
    Cost management
    50 xp
    Prompt compression
    50 xp
    Making a cost prognosis
    50 xp
    Governance and security
    50 xp
    Prompt injection
    50 xp
    Mitigation strategies
    100 xp
    Data integrity and poisoning
    50 xp
    Congratulations
    50 xp
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Training 2 or more people?

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In the following Tracks

Associate AI Engineer for Developers

Go To Track

collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
Collaborator's avatar
Francesca Donadoni

audio recorded by

Max Knobbout's avatar
Max Knobbout

prerequisites

Large Language Models (LLMs) Concepts
Max Knobbout HeadshotMax Knobbout

Applied Scientist, Uber

I work as a data scientist with over 10 years of experience in ML and AI. I hold a PhD in AI and have worked with many large companies, now Uber. My main research focus is on AI fairness, synthetic data, and causal inference. If you're in Amsterdam, feel free to reach out!
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