Accelerating ML Application Development: Production-Ready Airflow Integrations with Critical AI Tools

Generative AI and operational machine learning play a crucial role in the modern data landscape by enabling organizations to leverage their data to power new products and increase customer satisfaction. These technologies are used for virtual assistants, recommendation systems, content generation, etc. They help organizations gain competitive advantage through data-driven decision making, automation, improved business processes and customer experiences.

Apache Airflow is at the heart of many teams’ ML operations, and with new integrations for Large Language Models (LLM), Airflow enables these teams to build production-quality applications with the latest advances in ML and AI.

Simplify ML development

Too often, machine learning models and predictive analytics are created in silos, far from production systems and applications. Organizations face a perpetual challenge: transforming a single data scientist’s notebook into a production-ready application with stability, scalability, compliance, and more.

Organizations that standardize on a single platform to orchestrate their DataOps and MLOps workflows, however, are able to reduce not only the friction of end-to-end development, but also infrastructure costs and IT sprawl. While it may seem counterintuitive, these teams also benefit from more choice. When the centralized orchestration platform, like Apache Airflow, is open source and includes integrations with almost every data tool and platform, data and ML teams can choose the tools that best suit their needs. needs while enjoying the benefits of standardization, governance and simplified troubleshooting. and reusability.

Apache Airflow and Astro (Astronomer’s fully managed Airflow orchestration platform) is where data engineers and ML engineers meet to create business value from operational ML. With a massive number of data engineering pipelines running on Airflow daily across industries and industries, it is the workhorse of modern data operations, and ML teams can build on this foundation not only for model inference, but also for training, evaluation and monitoring. .

Airflow Optimization for Enhanced ML Applications

As organizations continue to find ways to leverage large language models, Airflow is increasingly at the center of operationalizing things like unstructured data processing, retrieval augmented generation (RAG) , feedback processing and fine-tuning of base models. To support these new use cases and provide a starting point for Airflow users, Astronomer worked with the Airflow community to create Ask Astro, as a public reference implementation of RAG with Airflow for AI conversational.

More broadly, Astronomer has led the development of new integrations with vector databases and LLM providers to support this new generation of applications and the pipelines needed to keep them secure, up-to-date and manageable.

Connect to the most widely used LLM services and vector databases

Apache Airflow, in combination with some of the most widely used vector databases (Weaviate, Pinecone, OpenSearch, pgvector) and natural language processing (NLP) providers (OpenAI, Cohere), provides extensibility thanks to the latest open source developments . Together, they enable a best-in-class RAG development experience for applications such as conversational AI, chatbots, fraud analysis, and more.


OpenAI is an AI research and deployment company that provides an API to access cutting-edge models such as GPT-4 and DALL·E 3. OpenAI vendor Airflow offers modules to easily integrate OpenAI with Airflow. Users can generate data integrations, a fundamental step in NLP with LLM-based applications.

View Tutorial → Orchestrate OpenAI Operations with Apache Airflow


Cohere is an NLP platform that provides an API for accessing cutting-edge LLMs. The supplier Cohere Airflow offers modules to easily integrate Cohere with Airflow. Users can leverage these business-focused LLMs to easily build NLP applications using their own data.

View tutorial → Orchestrate Cohere LLMs with Apache Airflow


Weaviate is an open source vector database, which stores embeddings of large objects such as text, images, audio or video. The Weaviate Airflow provider offers modules to easily integrate Weaviate with Airflow. Users can process high-dimensional vector embeddings using an open source vector database, which offers a rich feature set, exceptional scalability and reliability.

View Tutorial → Orchestrate Weaviate Operations with Apache Airflow


pgvector is an open source extension for PostgreSQL databases that adds the ability to store and query high-dimensional object embeddings. The pgvector Airflow provider offers modules to easily integrate pgvector with Airflow. Users can unlock powerful capabilities for working with vectors in high-dimensional space with this open source extension for their PostgreSQL database.

View tutorial → Orchestrate pgvector operations with Apache Airflow

Pine cone

Pinecone is a proprietary vector database platform designed to drive large-scale vector AI applications. The supplier Pinecone Airflow offers modules to easily integrate Pinecone with Airflow.

View Tutorial → Orchestrate Pinecone Operations with Apache Airflow

Open search

OpenSearch is an open source distributed search and analytics engine based on Apache Lucene. It offers advanced search capabilities on large bodies of text as well as powerful machine learning plugins. The OpenSearch provider Airflow offers modules to easily integrate OpenSearch with Airflow.

View Tutorial → Orchestrate OpenSearch Operations with Apache Airflow

Further information

By making it easier for data-centric teams to integrate data pipelines and data processing into ML workflows, organizations can streamline operational AI development and realize the potential of AI and natural language processing in an operational environment. Ready to dive deeper for yourself? Discover available modules designed for easy integration: Visit the Astro Registry to see the latest AI/ML DAG examples.