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GenAIOps Foundations

GenAIOps Foundations

1h 18mIntermediate2025-04-11

Authors

Kumaran Ponnambalam

Kumaran Ponnambalam

Working with data for 20+ years

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.

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.

Skills covered

Operations ManagementGenerative AIProject ManagementArtificial Intelligence (AI)Business Analysis and StrategyOne-Off

Concepts

0. Introduction

  • 01 - GenAIOps foundations

1. GenAIOps Fundamentals

  • 02 - The machine learning lifecycle
  • 03 - The advent of generative AI
  • 04 - DevOps, MLOps and GenAIOps
  • 05 - The GenAIOps lifecycle
  • 06 - Planning genAI projects

2. Model Training in Gen AI

  • 07 - Training phase in generative AI
  • 08 - Data for evaluaton and fine-tuning
  • 09 - Generative AI automation pipelines
  • 10 - Model lifecycle management
  • 11 - Evaluation and finetuning tracking

3. Developing Gen AI Applications

  • 12 - The generative AI application stack
  • 13 - Prompt management
  • 14 - Memory and embedding management
  • 15 - Agents management
  • 16 - Agent tools integrations
  • 17 - Testing generative AI applications

4. Model Deployment and Serving

  • 18 - GenAI deployment patterns
  • 19 - Infrastructure planning
  • 20 - Generative AI deployment pipelines
  • 21 - Scaling generative AI deployments
  • 22 - Guardrails deployments

5. Monitoring and Troubleshooting

  • 23 - Generative AI monitoring pipelines
  • 24 - Metrics for generative AI
  • 25 - Generative AI traces
  • 26 - Agent trajectories
  • 27 - Troubleshooting generative AI model behavior

6. Security and Compliance

  • 28 - Ethics and compliance
  • 29 - Protection against vulnerabilities
  • 30 - Toxicity and bias in generative AI
  • 31 - Hallucinations
  • 32 - Privacy protection in genAI

Conclusion

  • 33 - Next steps

Related learn paths

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