AI-Driven Modeling

AI-Driven Modeling

Streamline tensor data workflows so you can focus on your core competency and keep your competitive edge.

Overview

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.


solutions

AI-driven modeling

Modernize data operations so you can focus on what you do best.

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benefits

Accelerate all phases of AI/ML model development

Data preparation
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
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
Model training

Evolve features rapidly while carefully tracking changes with Earthmover’s advanced data version control features, including snapshots, branches, and tags.

Model evaluation
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
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.

Jon Weisbaum

Jon Weisbaum

Director of Meteorological Engineering at Pelmorex
testimonial icon

Flux allows us to get immediate feedback from customers, as we design our data offerings, without having to deploy new infrastructure. This allows us to experiment with product market fit without a significant engineering investment.

case studies

Build smarter and faster

Jumpstart your development cycle with guides and cookbooks developed by our expert team of climate scientists and data engineers.

Case Study

Cloud native data loaders for machine learning using Zarr and Xarray

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

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.

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