Measuring biodiversity is crucial for understanding global ecosystem health. BIOSCAN-5M features five million specimens from 47 countries with paired high-resolution images and DNA barcodes, offering resources for ecological research and AI model training.

Measuring biodiversity is crucial for understanding global ecosystem health, especially in the face of anthropogenic environmental changes. Rates of data collection are ever increasing, but access to expert human annotation is limited, making this an ideal use-case for machine learning solutions. The newly released BIOSCAN-5M dataset features five million specimens from 47 countries around the world, with paired high-resolution images and DNA barcodes for every sample. The dataset’s hierarchical taxonomic labels, geographic data, and long-tail distribution of rare species offer valuable resources for ecological research and AI model training. BIOSCAN-5M represents a significant advancement in biodiversity informatics, facilitated by the International Barcode of Life and the BIOSCAN project, and is publicly available for download via Hugging Face and PyPI.

The BIOSCAN-5M dataset features five million specimens from 47 countries with paired high-resolution images and DNA barcodes for every sample. The dataset’s hierarchical taxonomic labels, geographic data, and long-tail distribution of rare species offer valuable resources for ecological research and AI model training. BIOSCAN-5M represents a significant advancement in biodiversity informatics, facilitated by the International Barcode of Life and the BIOSCAN project, and is publicly available for download via Hugging Face and PyPI.

What does it take to go from an idea in a notebook to an application handling real-world traffic? The Pinecone and Pulumi teams will explore the infrastructure and service architecture you need in order to scale AI apps in production. We will delve into deploying high-volume AI systems through scalable microservices, efficient data processing, and seamless synchronization between user interfaces and databases. We will examine the nuances of containerization for enhanced portability and Infrastructure as Code (IaC) for streamlined cloud deployments. The workshop will also discuss industry best practices in scalability and security for production-grade AI systems in a cloud-native landscape. This workshop is designed to help developers and engineers gain valuable insights and practical strategies for evolving AI applications into resilient and efficient cloud-native solutions.