Model
The model you want trained, or a clear starting point for it. It can be your own architecture or an existing one you are building from, we work with what you already have.
Train with IOTA
IOTA
Bring the workload. We deliver the result.
The managed route to disaggregated training. You bring a defined workload, we take it from qualification through to a finished, reported outcome.
The managed training principle
You own the outcome. We run the training.
You do not need a distributed-systems team to get a model trained across disaggregated compute. Bring a clear objective, and we take on everything between the workload and the result.
Defined training objective
The engagement starts from what you are trying to achieve: the model you want, the result that counts as success, and the constraint you are working against. From there, the training is ours to run.
Where this route fits
When managed is the right way in.
The managed route fits when the outcome matters more than running the training yourself.
A defined workload, without the expertise to run it
You know what you want to build and can define success, but you do not want to run disaggregated training in-house. We run it for you.
Discuss a workload →A workload too important to leave to chance
The workload matters too much to leave to chance. We qualify it, run it, and stand behind the result.
Discuss a workload →Platform or partner workloads that are hard to run
You have workloads that are hard to serve profitably, or complex to run. We take on the execution, so you can offer the outcome.
Discuss a workload →What you bring
A clear workload. Not a systems team.
The managed route asks little of you up front. Define these five things, and we take it from there.
Dataset
The data the model will train on. You bring it in whatever shape it is in, and we handle preparing it to run across the compute underneath.
Training objective
What the training is meant to achieve. A clear goal for the run, whether that is a capability, a quality target, or a specific task the model needs to do well.
Success criteria
What a good result looks like, so we both know when it is met. Agreeing this up front means the run is measured against your definition of done, not a vague sense of better.
Current baseline
What you are doing today, so we can measure the outcome against it. Your current cost, setup, or result gives us the mark to beat, and gives you a clear before and after.
How the engagement works
The same clear stages, every time.
A managed engagement follows the same clear stages every time, so you always know where things stand.
WORKLOADRESULT
Stage 01 / 06
Bring the workload
You bring the model, the data, the objective, and what success looks like. That is the whole of what we need to begin, the rest is ours to run.
Stage 02 / 06
Qualify
We confirm the workload is well defined, feasible, and has a clear measure of success before anything runs.
Stage 03 / 06
Configure
We set up the model, data, and training to run across disaggregated compute.
Stage 04 / 06
Train
We run the training across disaggregated, variable compute, coordinated as one system.
Stage 05 / 06
Monitor and respond
We watch the run continuously, catch problems early, and reissue work when a machine drops out.
Stage 06 / 06
Outcome
We deliver the trained model and a clear account of what the training achieved and what it cost.
IOTA MANAGED TRAINING LAYER
Everything between the workload and the result.
The full managed layer, from qualifying the workload to reporting the result.
Workload qualification
We confirm the workload is well defined, feasible, and has a clear measure of success before anything runs.
Training configuration
We set up the model, data, and training to run across disaggregated compute.
Distributed execution
We run the training across disaggregated, variable compute, coordinated as one system.
Monitoring
We watch the run continuously and catch problems early.
Failure response
When a machine drops out, its work is reissued and the run continues, with progress preserved rather than lost.
Progression and reporting
We keep the run moving toward the objective, without waiting on the slowest hardware, and you get a clear account of what the training achieved and what it cost.
What you get back
A trained model, and the proof behind it.
The outcome is more than a model. It is a documented result you can judge, act on, and build the next run from.
The trained model, meeting the success criteria you agreed at the start.
A clear account of how the training ran and how it performed.
How the model converged over the run, not just where it ended up.
What the run cost, measured against the baseline you brought in.
What was learned, so the next run starts from a stronger position.
Next step
Bring us a workload.
Tell us what you are trying to train and what a good result looks like, and we will take it from there.