How SeerAI Delivers Spatiotemporal Data and Analytics with Pachyderm

• Platform as a Service (PaaS) - Data Management Platforms
• Analytics & Modeling - Big Data Analytics
• Analytics & Modeling - Machine Learning

• Data Science Services
• Cloud Planning, Design & Implementation Services

SeerAI’s flagship offering, Geodesic, is the world’s first decentralized platform optimized for deriving insights and analytics from planetary-scale spatiotemporal data. Working with spatiotemporal data is a challenge. Because it concerns planetwide questions, the data sets are massive in scale – often entailing petabytes of imagery. The data itself can come from different sources, requiring the ability to load and manage from a decentralized data model. Finally, that data is generally heterogeneous and unstructured, and thus notoriously complex and difficult to deal with. SeerAI designed Geodesic to constantly grow in knowledge and data relationships so that it can eventually answer most any question. Controlling the data ingest, ML job scheduling, model interaction, and data versioning can be extremely complex at this scale.

SeerAI is a company that applies artificial intelligence and machine learning to spatiotemporal data to allow its customers to gain insights and competitive advantage. The company's flagship offering, Geodesic, is the world’s first decentralized platform optimized for deriving insights and analytics from planetary-scale spatiotemporal data. The company is based in New Rochelle, NY. SeerAI is designed to constantly grow in knowledge and data relationships so that it can eventually answer most any question. The company deals with a broad range of data sets, with complex inter-relationships and at a massive scale.

SeerAI chose Pachyderm as a core component in delivering global data fusion at scale. Pachyderm is cloud-native and highly scalable, which allows SeerAI to easily create and work with multiple pipelines and repositories for its data science workflows. In addition, Pachyderm automatically takes care of triggering transformations, data sharing, data versioning, parallelism, and resource management allowing the data to be delivered more efficiently. Pachyderm’s ability to provide automatic incremental processing saves compute by only processing differences and automatically skipping duplicate data. Since the pipeline and data are all managed by Pachyderm it can autoscale with parallel processing without writing any code. Pachyderm works with the core microservices in Geodesic for heterogeneous data search and preparation. The team uses Pachyderm within Blackhole to handle processing and formatting so the data is readily queryable. Pachyderm also allows the team to better control machine learning job management.

• Scalability across massive data sets
• Managing massive and complex ML workflows
• Data versioning of large data sets

• Efficient data delivery through automatic incremental processing
• Ability to autoscale with parallel processing without writing any code
• Ability to handle processing and formatting so the data is readily queryable