AI-driven modeling
Streamline tensor data workflows so you can focus on your core competency and keep your competitive edge.
Focus engineering effort on AI model quality, not DevOps
Over and over, we see companies hiring top AI and scientific talent, and then tasking these teams with repetitive and inefficient data wrangling tasks. These teams end up with over-engineered data architectures that rapidly become a source of tech debt while failing to deliver performance and flexibility.
Building on top of the Earthmover platform frees data scientists and AI/ML practitioners to focus entirely on iterating on model quality instead of data infrastructure or DevOps bottlenecks, even as data sets scale.
AI-driven modeling
Modernize data operations so you can focus on what you do best.
Accelerate all phases of AI/ML model development
Data preparation
Massively simplify the data ingestion process thanks to Earthmover’s native compatibility with common scientific file formats like HDF5, NetCDF4, GRIB, and TIFF.
Data loading
Optimize GPU utilization for model training with high-performance cloud-native data loaders that allow you to flux data directly from object storage to the GPU, bypassing local file storage.
Model training
Evolve features rapidly while carefully tracking changes with Earthmover’s advanced data version control features, including snapshots, branches, and tags.
Model evaluation
Store evaluation targets in a flexible, performant way while leveraging data version control to carefully track changes. Easily store training data and the models themselves, all using the same core data structure.
Inference and production
Immediately share and publish results of inference stored in Arraylake via high-performance endpoints that can deliver data in a range of industry standard API formats, accelerating the time to value.
At CTrees, we create machine learning models that integrate multiple data sources to produce high-resolution, time-series datasets on forest carbon and activity. We faced the all-too-familiar chaos of manual and ad-hoc dataset versioning, inconsistent folder structures, and directories packed with thousands of tiny GeoTIFFs. Transitioning to Arraylake enabled structured, versioned cloud-native datacube access. As a bonus, Flux makes it easy to view the data via a WMS service, streamlining the visualization process for both internal users and external stakeholders."
Build smarter and faster
Jumpstart your development cycle with guides and cookbooks developed by our expert team of climate scientists and data engineers.
Demo
Cloud native data loaders for machine learning using Zarr and Xarray
We set up a high-performance PyTorch dataloader using data stored as Zarr in the cloud.
Case Study
Solving NASA’s Cloud Data Dilemma: How Icechunk Revolutionizes Earth Data Access
Earthmover helps NASA achieve 100x performance boost for cloud data analytics with the Icechunk tensor storage engine.