LLMOps Concepts
42:01
Description
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
Who is this for?
What You Need to Know?
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DescriptionEmbark 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.
Who this course is for:
- 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.
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.
Who this course is for:
- 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.
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Udemy
View courses Udemy- language english
- Training sessions 11
- duration 42:01
- Release Date 2025/01/24
