As organizations adopt generative AI and large language models (LLMs), observability is becoming essential for managing experimentation, ensuring reliability, and optimizing performance across the AI lifecycle. This guide highlights key insights from a discussion between Google and Datadog leaders on how organizations can successfully implement AI initiatives on Google Cloud. It emphasizes building strong evaluation practices, starting with simple use cases before scaling to complex systems, and maintaining transparency to build trust in AI outputs. The eBook also stresses the importance of monitoring compute costs, model performance, and infrastructure such as GPUs and clusters. By combining robust observability with clear metrics and governance, organizations can balance innovation with

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