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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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.For Business
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Associate AI Engineer for Developers
Go To Track- 1
Introduction to LLMOps & Ideation Phase
FreeThis 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.
- 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.
Prompt engineering50 xpThe importance of prompt engineering50 xpTrying out prompt engineering50 xpKeeping track of prompts50 xpChains and agents50 xpThe difference between agents and chains100 xpChoosing the right architecture50 xpRAG versus fine-tuning50 xpThe RAG workflow100 xpCompare RAG with fine-tuning100 xpTesting50 xpChoosing the right metric100 xpThe importance of testing50 xp - 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.
Deployment50 xpThe need for CI/CD50 xpThe right scaling strategy100 xpMonitoring and observability50 xpMonitoring your application100 xpAlert handling50 xpCost management50 xpPrompt compression50 xpMaking a cost prognosis50 xpGovernance and security50 xpPrompt injection50 xpMitigation strategies100 xpData integrity and poisoning50 xpCongratulations50 xp
For Business
Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreIn the following Tracks
Associate AI Engineer for Developers
Go To Trackcollaborators
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prerequisites
Large Language Models (LLMs) ConceptsMax Knobbout
See MoreApplied 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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