Operationalize and Scale
AI and ML Initiatives
Accelerate operational AI with a workflow management platform that has the breadth of integrations and scalable compute power essential for delivering next-gen applications.
Build an elastic data foundation for faster development of production-ready AI and ML applications
Astro supports hand-in-hand collaboration between data engineering, data science, and machine learning teams across everything from traditional data pipelines to building AI applications.
Unify development practices
Benefit from a unified AI development environment, offering a consistent framework to streamline the path from prototype to production-ready AI.
Powerful compute capacity
Astro leads the managed-Airflow market with unmatched compute power, enabling scalable AI workloads and efficient, cost-effective operations.
Trusted AI with lineage
Gain clear visibility and transparency in data origins and transformations, enhancing the reliability of AI models and compliance readiness.
Seamless AI integrations
Validated integration with top providers simplifies AI development, allowing teams to focus on creating impactful models and applications without the challenges of interoperability.
Steven Hillion, Astronomer’s SVP of Data and AI, talks about how successful machine learning and AI initiatives come down to repeatable and reliable data processing.
Connect to the most widely-used LLM services and vector databases
Apache Airflow, combined with a comprehensive list of integrations, offers limitless extensibility and interoperability, enabling unified automation across systems through the power of open-source development.
Meet Ask Astro
Ask Astro, an LLM-powered chatbot, harnesses Airflow knowledge from various platforms to deliver Astronomer's extensive expertise on demand and serves as a starting point for operationalizing your applications.
Accelerate your AI workflow development
Discover available modules designed for easy integration with your favorite next generation AI tools.
Use OpenAI LLMs to Embed and Visualize Results
This DAG shows how to use the OpenAI Airflow provider to interact with the OpenAI API.
Generate Vectors with the Airflow Weaviate Provider
This DAG runs a simple MLOps pipeline to import data, generate vectors, and query the vectors.
Query Vectors with Pinecone and Airflow
This DAG uses the Pinecone Airflow Provider to import data, generate vectors, then query based on user-provided inputs.