Generative AI Foundations: Advanced Generative AI Techniques for IT





Unlock the transformative potential of advanced generative AI (GenAI) models in site reliability engineering (SRE). This course caters to SRE professionals, IT architects, and those eager to harness the full scope of generative AI in the context of SRE and covers the concepts of advanced generative AI techniques to bolster system reliability, scalability, and efficiency in SRE. In this course, we will begin with an overview of advanced GenAI and SRE. You'll explore how to deploy advanced models from the Azure AI model catalog and transition into testing approaches for applications integrating GenAI. Next, you'll deploy a GenAI-based application to Azure Kubernetes Service (AKS), explore the suitability of GenAI models for SRE, and see which SRE tools incorporate GenAI. With that foundation, you'll experiment with various methods for supporting SRE operations with GenAI including chatbots, implementing a backoff mechanism, evaluating model performance, and configuring log analytics for GenAI models on Azure. Lastly, you'll explore GenAI and SRE advancements, and fine-tune a GenAI model in Azure OpenAI Studio.




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Generative AI Foundations: Advanced Generative AI Techniques for IT

  • outline the use of advanced generative AI techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models
  • identify site reliability engineering (SRE) principles and practices that are used to ensure the reliability and availability of software systems
  • deploy advanced generative AI models from Azure AI model catalog
  • employ robust testing and responsible deployment strategies to ensure reliability of advanced generative AI applications
  • deploy reliable and scalable apps that integrate generative AI on Azure Kubernetes Service (AKS)
  • assess the suitability of advanced generative AI models for enhancing SRE practices using Azure
  • support SRE operations by providing an AI chatbot customized to answer questions regarding SRE best practices
  • identify SRE tools and platforms that incorporate advanced generative AI capabilities for enhanced incident management and monitoring
  • improve site reliability by implementing a backoff mechanism to avoid rate-limiting errors in generative AI applications
  • evaluate a generative AI model using Azure AI Studio’s evaluation feature
  • provide an overview of generative AI and SRE advancements with a focus on how Azure-based technologies can adapt and innovate in the space
  • configure a log analytics workspace and diagnostics settings to enable analytics for generative AI resources
  • optimize and fine-tune advanced generative AI models


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