How Fast Can You Deploy an AI Pipeline?

Description

Learn how to build and deploy AI agent and machine learning model pipelines with ZenML! In this demo, we showcase the new pipeline deployments feature with the weather_agent example—a production-ready pipeline that combines LLMs, tool integration, and real-time data fetching. Why ZenML for AI Agents? 🔹 Production-ready: From notebook to deployment in minutes 🔹 Observable: Full lineage tracking, artifact versioning, and metadata 🔹 Scalable: Run locally, on Kubernetes, or any cloud orchestrator 🔹 One pattern for all: Same approach for ML models, classical pipelines, and AI agents 🔗 Resources: 📚 Full Documentation: https://docs.zenml.io/ 🚀 Example Code: https://github.com/zenml-io/zenml/tree/main/examples/weather_agent 💬 Join Our Community: https://zenml.io/slack 🌐 ZenML Website: https://zenml.io 📖 Relevant Guides: - https://docs.zenml.io/getting-started/your-first-ai-pipeline#path-1-build-ai-agents - https://docs.zenml.io/how-to/deployment/deployment - https://sdkdocs.zenml.io/ #ZenML #MLOps #AgenticAI #LLMs #AI #MachineLearning #DevOps #PythonDevelopment