Plan before spend
See the shape of available compute before committing a workload, so infrastructure choices can follow fit instead of guesswork.
ML Infrastructure With Context
Aero helps teams plan, select, and train ML systems with control.
Aero is a distributed ML engine for teams that need to understand available compute, choose the right model and dataset, and keep training decisions connected instead of scattered across disconnected tools.
Project
Aero
Problem
ML decisions are fragmented
What
A distributed ML engine
Help
Control before training
What becomes possible
PlanTrain
The problem
ML work breaks down when compute, data, and model decisions live in separate tools.
Teams can train models, rent compute, prepare datasets, and push deployments. The hard part is keeping those decisions connected enough to understand what changed, why it matters, and what should happen next.
Compute is chosen too late.
GPU and CPU availability, cost, latency, and region should be visible before a workload is shaped, not discovered after the team is already blocked.
Model and data selection lacks context.
Algorithms and datasets are often selected as isolated assets. Aero is designed to make that selection part of a controlled customization workflow.
Training knowledge does not compound.
Each run should leave behind useful evidence about data, relationships, and decisions instead of becoming another disconnected experiment.
What Aero is
Aero is a distributed ML engine for planning, training, customization, and production handoff.
It is not a single model and it is not a black-box cloud. Aero is intended to coordinate customer-controlled compute, selectable ML assets, dataset-aware training context, and future backend services into one workflow.
Compute planning surface
Customers should be able to compare available GPU and CPU capacity by region, cost, latency, and fit once backend data is connected.
Model and dataset selection
The catalog gives customers a place to choose algorithms and datasets before configuring how those assets should be adapted.
Dataset-aware training memory
Aero is designed so training runs can create reusable evidence about datapoints, model context, and future rerun decisions.
How it helps
Aero removes the bottleneck between choosing ML resources and turning them into a controlled workflow.
The website should make one thing clear: Aero helps teams decide what to run, where to run it, what data or model to use, and how that decision can become repeatable infrastructure.
Customize with context
Start from the model and dataset a customer actually needs, then attach the training and configuration path later.
Keep control
Aero is designed around customer-owned compute and data boundaries, with backend connections added only when the product workflow is ready.