Building GenAI applications is difficult due to challenges in model selection, specialized skills, data context, quality evaluation, and overall operationalization.
GenAIOps is a discipline combining people, processes, and platforms to continuously deliver value, differing from MLOps in its focus on consuming pre-built models.
GenAI projects follow a continuous loop of experimentation, building, deployment, and monitoring to ensure ongoing improvement and value delivery.
The GenAIOps maturity model helps teams understand their current capabilities and provides a roadmap for advancing from initial exploration to operational excellence.
The Azure AI platform provides a comprehensive suite of tools, including Azure AI Foundry, to support the entire GenAIOps lifecycle from model discovery to enterprise-scale deployment.
Azure AI Foundry simplifies model selection with a vast catalog and benchmarking tools, while the model inference service provides a unified API for deploying diverse models.
The Azure Developer CLI (AZD) is a high-level tool that streamlines the process of setting up and deploying robust GenAI project templates from a repository to the cloud.
Azure provides an SDK, Application Insights, and Azure Monitor with specialized dashboards to gain full observability into the performance and behavior of AI-infused applications.
To mature your GenAIOps practice, start by assessing your current state, then review your strategy and tactics, and finally select the right tools for the job.
41:45Maxim Salnikov
26:47Alejandro Saucedo
19:27Kapil Gupta
35:16Ankit Patel
23:33Oliver Will
25:41Joy Chatterjee
24:24Damir
59:39Julian Joseph
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Jobs that call for the skills explored in this talk.
Serviceware SE