Accelerating ML application development: Production-ready Airflow integrations with critical AI tools

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Generative AI and operational machine studying play essential roles within the fashionable information panorama by enabling organizations to leverage their information to energy new merchandise and enhance buyer satisfaction. These applied sciences are used for digital assistants, suggestion methods, content material technology, and extra. They assist organizations construct a aggressive benefit by data-driven determination making, automation, enhanced enterprise processes, and buyer experiences.

Apache Airflow is on the core of many groups’ ML operations, and with new integrations for Massive Language Fashions (LLMs), Airflow allows these groups to construct production-quality functions with the newest developments in ML and AI.

Simplifying ML Growth

All too often, machine studying fashions and predictive analytics are created in silos, far faraway from manufacturing methods and functions. Organizations face a perpetual problem to show a lone information scientist’s pocket book right into a production-ready software with stability, scaling, compliance, and so forth.

Organizations that standardize on one platform for orchestrating each their DataOps and MLOps workflows, nevertheless, are in a position to cut back not solely the friction of end-to-end improvement but additionally infrastructure prices and IT sprawl. Whereas it might appear counterintuitive, these groups additionally profit from extra alternative. When the centralized orchestration platform, like Apache Airflow, is open-source and contains integrations to almost each information software and platform, information and ML groups can choose the instruments that work greatest for his or her wants whereas having fun with the advantages of standardization, governance, simplified troubleshooting, and reusability.

Apache Airflow and Astro (Astronomer’s absolutely managed Airflow orchestration platform) is the place the place information engineers and ML engineers meet to create enterprise worth from operational ML. With an enormous variety of information engineering pipelines working on Airflow day by day throughout each business and sector, it’s the workhorse of recent information operations, and ML groups can piggyback off of this basis for not solely mannequin inference but additionally coaching, analysis, and monitoring.

Optimizing Airflow for Enhanced ML Functions

As organizations proceed to seek out methods to leverage massive language fashions, Airflow is more and more entrance and middle for the operationalization of issues like unstructured information processing, Retrieval Augmented Technology (RAG), suggestions processing, and fine-tuning of basis fashions. To help these new use-cases and to offer a place to begin for Airflow customers, Astronomer has labored with the Airflow Neighborhood to create Ask Astro—as a public reference implementation of RAG with Airflow for conversational AI.

Extra broadly, Astronomer has led the event of recent integrations with vector databases and LLM suppliers to help this new breed of functions and the pipelines which might be wanted to maintain them protected, recent, and manageable.

Hook up with the Most Broadly Used LLM Companies and Vector Databases

Apache Airflow, together with a few of the most generally used vector databases (Weaviate, Pinecone, OpenSearch, pgvector) and pure language processing (NLP) suppliers (OpenAI, Cohere), provides extensibility by the newest in open-source improvement. Collectively, they allow a first-class expertise in RAG improvement for functions like conversational AI, chatbots, fraud evaluation, and extra.


OpenAI is an AI analysis and deployment firm that gives an API for accessing state-of-the-art fashions like GPT-4 and DALL·E 3. The OpenAI Airflow supplier provides modules to simply combine OpenAI with Airflow. Customers can generate embeddings for information, a foundational step in NLP with LLM-powered functions.

View tutorial → Orchestrate OpenAI operations with Apache Airflow


Cohere is an NLP platform that gives an API to entry cutting-edge LLMs. The Cohere Airflow supplier provides modules to simply combine Cohere with Airflow. Customers can leverage these enterprise-focused LLMs to simply create NLP functions utilizing their very own information.

View tutorial → Orchestrate Cohere LLMs with Apache Airflow


Weaviate is an open-source vector database, which shops high-dimensional embeddings of objects like textual content, pictures, audio, or video. The Weaviate Airflow supplier provides modules to simply combine Weaviate with Airflow. Customers can course of high-dimensional vector embeddings utilizing an open-source vector database, which offers a wealthy set of options, distinctive scalability, and reliability.

View tutorial → Orchestrate Weaviate operations with Apache Airflow


pgvector is an open-source extension for PostgreSQL databases that provides the aptitude to retailer and question high-dimensional object embeddings. The pgvector Airflow supplier provides modules to simply combine pgvector with Airflow. Customers can unlock highly effective functionalities for working with vectors in a high-dimensional house with this open-source extension for his or her PostgreSQL database.

View tutorial → Orchestrate pgvector operations with Apache Airflow


Pinecone is a proprietary vector database platform designed for dealing with large-scale vector-based AI functions. The Pinecone Airflow supplier provides modules to simply combine Pinecone with Airflow.

View tutorial → Orchestrate Pinecone operations with Apache Airflow


OpenSearch is an open-source distributed search and analytics engine based mostly on Apache Lucene. It provides superior search capabilities on massive our bodies of textual content alongside highly effective machine studying plugins. The OpenSearch Airflow supplier provides modules to simply combine OpenSearch with Airflow.

View tutorial → Orchestrate OpenSearch operations with Apache Airflow

Extra Info

By enabling data-centric groups to extra simply combine information pipelines and information processing with ML workflows, organizations can streamline the event of operational AI, and notice the potential of AI and pure language processing in an operational setting. Able to dive deeper by yourself? Uncover out there modules designed for simple integration—go to the Astro Registry to see the newest AI/ML pattern DAGs.

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