Operations | Monitoring | ITSM | DevOps | Cloud

APIs often represent the cutting edge of the technology space. This is especially true with Artificial Intelligence – as AI has evolved from speculative technology to mass adoption, it has shown up significantly in APIs as a modality and mechanism. However, as with all new technologies, using AI APIs comes with significant challenges.

Kubeflow is a cloud-native, open source machine learning operations (MLOps) platform designed for developing and deploying ML models on Kubernetes. Kubeflow helps data scientists and machine learning engineers run the entire ML lifecycle within one tool. Charmed Kubeflow is Canonical’s official distribution of Kubeflow. The key benefits of Charmed Kubeflow include security maintenance of container images, enterprise support, and further tooling integration with Spark, Feast, MLFlow, and others.

When incidents strike, clear communication is crucial — but one size doesn't fit all. Customer support needs to know what users are experiencing and possible workarounds, execs need business impact updates and timelines, and engineers need deep technical details. Manually juggling these different communication needs is time-consuming, error-prone, and frustrating when every minute counts.

The advancement of AI has empowered businesses to incorporate intelligent automation into their applications. A serverless Generative AI (GenAI) API enables developers to harness cutting-edge AI models without the burden of infrastructure management. This guide walks you through building a scalable and cost-effective GenAI API using FastAPI, a high-performance Python framework with built-in async support and seamless AWS integration.