Linkedin - GenAIOps Foundations

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Free Download Linkedin - GenAIOps Foundations
Released 04/2025
With Kumaran Ponnambalam
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 19m 12s | Size: 133 MB

Leverage GenAIOps in your enterprises for effective development, deployment, and management of GenAI applications.
Course details
Generative AI is transforming the way enterprises deploy applications in production to improve efficiency. As businesses increasingly adopt GenAI tools in their workflows, the need for effective management, monitoring, and optimization of AI models and systems is crucial across the GenAI lifecycle. In this course, instructor Kumaran Ponnambalam helps to familiarize you with the GenAI lifecycle and how GenAIOps can be incorporated into this lifecycle to build, deploy, manage, and monitor GenAI products and artifacts. By the end of this course, you'll be prepared to leverage industry-standard processes and best practices for implementing GenAIOps at scale.
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GenAIOps Foundations
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 19m | 132 MB
Instructor: Kumaran Ponnambalam​

Generative AI is transforming the way enterprises deploy applications in production to improve efficiency. As businesses ly adopt GenAI tools in their workflows, the need for effective management, monitoring, and optimization of AI models and systems is crucial across the GenAI lifecycle. In this course, instructor Kumaran Ponnambalam helps to familiarize you with the GenAI lifecycle and how GenAIOps can be incorporated into this lifecycle to build, deploy, manage, and monitor GenAI products and artifacts. By the end of this course, you'll be prepared to leverage industry-standard processes and best practices for implementing GenAIOps at scale.

Learning objectives

• Define the lifecycle of GenAI projects.
• Enumerate the tenets of GenAIOps and how it applies to the GenAI project lifecycle.
• Implement process, controls and monitoring across the GenAI lifecycle for production-grade projects.
• Leverage tools, technologies, and best practices to ensure robustness and control.

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