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Best Practices for Scaling Vector Embeddings and Shipping Reliable AI Products

· 2 min read
Ieva Urbaite

Panel Discussion

In May, Team Weaviate assembled in New York City for another stop on the Weaviate roadshow tour! We had 100+ AI enthusiasts join us for a full-day event at the Google offices in Manhattan, packed with talks from experts and hands-on training for building AI applications.

Our CTO, Etienne Dilocker, hosted a panel discussion with Kerry Chang, Head of Product Engineering at Instabase, and Julian LaNeve, CTO of Astronomer, on leveraging AI technologies in production to enhance data processing and extraction capabilities while maintaining data security and user trust.

Instabase leverages AI to streamline operations and enhance efficiency across various industries, including banking, insurance, and ride-sharing platforms like Uber. Astronomer empowers data teams to enhance their data availability with Astro, a modern data orchestration platform powered by Apache Airflow.

Here are three key takeaways from the discussion:

  • Integration of AI for data processing: Enhancing data processing capabilities with Weaviate has led to significant improvements in accuracy and efficiency within Instabase and Astronomer’s products.

  • Importance of trust and reliability: Both panelists emphasized the critical role of trust and reliability in AI applications. Ensuring data accuracy, maintaining up-to-date information, and providing transparency through open-source solutions are vital for building and retaining user trust.

  • Adaptability and continuous improvement: Flexibility and continuous improvement of AI systems is necessary. Organizations should be prepared to adopt new technologies, experiment with different models, and adjust their solutions to meet evolving customer needs and industry standards.

Watch the video for the full conversation and check out this case study for a deep dive into Instabase’s use of Weaviate.

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