• views: 65
• date: 27 March 2025
• posted by: AD-TEAM

LLMOps Concepts

What you'll learn

Understand the Fundamentals of LLMOps: Grasp the core concepts and principles of Large Language Model Operations, from ideation to operational deployment.

Master the LLM Application Lifecycle: Gain insights into the stages of the LLM application lifecycle, including development, deployment, and maintenance

Identify and Overcome LLMOps Challenges: Learn to recognize common challenges in LLM development, such as scalability, performance, and integration

Implement Data Governance and Security: Develop the skills to ensure data governance and security in LLM applications, focusing on privacy, compliance

Apply LLMOps in Real-World Applications: By the end of the course, learners will know how to apply LLMOps concepts and best practices to improve and optimize

Requirements

Large Language Models (LLMs) Concepts

Description

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.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.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.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.

Overview

Section 1: Introduction to LLMOps & Ideation Phase

Lecture 1 Introduction to LLMOps & Ideation Phase

Lecture 2 Lifecycle of LLMs

Lecture 3 Ideation phase

Section 2: Development Phase

Lecture 4 Prompt engineering

Lecture 5 Chains and agents

Lecture 6 RAG versus fine-tuning

Lecture 7 Testing

Section 3: Operational Phase

Lecture 8 Deployment

Lecture 9 Monitoring and observability

Lecture 10 Cost management

Lecture 11 Governance and security

This course is suitable for a wide range of audiences, including: AI and Machine Learning Enthusiasts: Individuals interested in understanding the operational aspects of large language models and how they can be applied in various fields. Data Scientists and Engineers: Professionals who want to enhance their knowledge of LLMOps, focusing on the lifecycle of LLMs from development to deployment and maintenance. Software Developers: Developers seeking to integrate LLMs into their applications and understand the challenges of scaling and deploying these models. Technical Managers and Leaders: Leaders in technology roles who need to grasp the strategic importance of LLMOps for implementing AI-driven solutions in their organizations. Security and Governance Professionals: Those involved in data governance, compliance, and security who want to ensure safe and ethical use of LLMs in production environments.




   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.