Astronomer offers Astro, a modern data orchestration platform built on Apache Airflow®, tailored to meet the demands of artificial intelligence and machine learning initiatives. Designed for data engineering and machine learning teams in sectors like finance, healthcare, manufacturing, and retail, it enables seamless pipeline management from data ingestion to model deployment. Astro combines scalability, transparency, and integration to accelerate AI development workflows.

What are the main features of Astronomer?

Unified AI/ML development environment

Astro provides a consistent, collaborative space for data and ML teams to build and run workflows.

• Centralized orchestration of pipelines across environments

• Shared framework for data engineering, science, and ML operations

• Streamlines development, testing, and deployment of AI models

Scalable compute for AI workloads

Astro supports high-performance execution of complex data workflows and AI pipelines.

• Dynamically scalable infrastructure to meet varying computational demands

• Handles large data volumes and parallel tasks for efficient processing

• Optimized for model training, batch predictions, and streaming data

Trusted AI through built-in data lineage

Transparency and traceability are core to the platform, helping ensure reliable AI output.

• Automatic capture of data flow and transformation paths

• Supports auditability and compliance across regulated industries

• Reinforces model integrity and responsible AI practices

Integrations with leading AI tools and services

Astro is compatible with a wide range of AI and vector database tools used in LLM and GenAI development.

• Connects with tools like OpenAI, Cohere, Pinecone, and Weaviate

• Enables orchestration of prompt engineering, fine-tuning, and retrieval workflows

• Reduces time spent configuring external systems for AI model delivery

Why choose Astronomer?

• Accelerates AI deployment: Astro shortens the path from data to production-ready AI models.

• Facilitates cross-functional collaboration: Enables engineers and scientists to work efficiently on shared infrastructure.

• Optimizes performance and costs: Provides scalable resources that match workload demands, avoiding over-provisioning.

• Supports data governance: Built-in data lineage and logging support compliance and model transparency.

• Adapts to AI stack evolution: Integrates easily with the latest tools in the AI and ML ecosystem, keeping teams future-ready.