File Transfer
Deliver the data AI Compute needs, exactly when it needs it.
Introducing INNORIX AI Data Delivery, which leaves your AI Infrastructure as-is and handles only the data movement between Storage and Compute.
AI DATA DELIVERY
Deliver the data AI Compute needs, exactly when it needs it.
AI infrastructure already has specialized systems, each performing its own role. Kubernetes and the GPU Scheduler place Compute Resources, and ML Orchestrators like Kubeflow manage the Pipeline's execution order, conditions, and Artifact flow.
INNORIX AI Data Delivery does not replace this domain.
What INNORIX handles is the Data Delivery Layer that moves the actual files of Datasets, Models, Checkpoints, and Results between Storage and Compute.
Object Storage · Data Center · File Storage · Research Data
AI Data Delivery
GPU / AI Compute
Checkpoint / Result
ROLE SPLIT
Handling Data Movement within AI Infrastructure.
Each system in the AI Pipeline solves a different problem. Where to place Compute, which Job to run first, and in what order to run Training and Evaluation are the domain of the existing Orchestrator and Scheduler.
AI Data Delivery handles the process of actually delivering the data to its destination based on those decisions.
Kubeflow also manages Pipeline Artifacts as logical objects and URIs like Datasets and Models, and connects Artifact Storage through a Pipeline Root or Workspace. This is the domain of ML Orchestration and Artifact Management, and rather than competing above or below it, INNORIX focuses on the role of transferring actual large-scale data between different Storage and Compute.
DATA PATH
Connecting the Data Path from Storage to Compute.
Having AI Compute ready and having the required Dataset ready on that Compute are not the same problem.
A Dataset might live in Object Storage while Training runs on an On-Prem GPU Cluster. Conversely, you might need to deliver a Dataset from internal company Storage to a Cloud GPU, or bring Training results back to a different Region or Private Storage. AI Data Delivery connects this span as one Transfer Layer.
Object Storage · Data Center
Rather than assuming Compute and Storage must be in the same environment, the focus is on connecting where the data currently is with where the Compute actually runs.
ARTIFACT FLOW
Use the same Data Delivery from Dataset all the way to Result.
In an AI Workload, data doesn't move just once. Different types of large-scale Artifacts keep moving before and after Training, and between Workloads.
Datasets and Models are already treated as first-class Artifacts in the ML Pipeline. Rather than newly managing the meaning or Lineage of these Artifacts, INNORIX takes on the role of delivering the actual files that make up those Artifacts between the Infrastructure that needs them.
TRANSFER PROFILE
Handle large Datasets and countless files together.
The Transfer Profile of an AI Dataset isn't fixed. It might be one enormous Artifact, it might consist of millions of images or small data files, or new data might keep being added continuously.
Rather than building a separate AI-specific copy method for this, AI Data Delivery applies INNORIX's High-Speed, Large File, High-Volume, and Automated Transfer to the Data Path of AI Infrastructure.
READY TIME
Configure Transfer that reduces Data Ready Time.
In an AI environment, what matters isn't just the raw transfer speed number.
When the Dataset a Compute will use becomes ready, how quickly the needed data can be delivered once new Compute is created, and where a transfer resumes after being interrupted — these are what actually affect Data Readiness.
GPU Utilization and Job Scheduling remain the responsibility of your existing Infrastructure. INNORIX focuses on the Transfer stage that prepares the actual data that Compute will use.
STAGING LAYER
Connect Data Staging as one Transfer Layer.
As AI Infrastructure expands across multiple environments, Data Movement tends to end up built in many different ways too.
Staging Copy
Direct Transfer
Control is managed by the INNORIX Platform, while Data is configured to move directly between the necessary Source and Target.
Instead of continuously adding separate Temporary Copies, Staging Paths, and Transfer Scripts for Dataset Delivery, you can operate Data Movement itself as reusable Infrastructure.
ORCHESTRATOR CALL
Call Data Delivery from your existing AI Orchestrator.
AI Data Delivery is not a new AI Workflow Engine. Within the Pipeline execution order and Artifact relationships already managed by systems like Kubeflow Pipelines, INNORIX becomes an independent step that executes the Data Transfer.
The Orchestrator decides which Job to run, when to start Training, and which Component to run after success.
INNORIX is responsible for executing the requested Data Delivery and returning its status and completion result to the existing Workflow.
PIPELINE FIRST
Leave the Pipeline as-is and extend the Data Path.
There's no need to redefine an already-built AI Pipeline in the INNORIX way.
Kubeflow / Existing Orchestrator
AI Data Delivery
The AI Platform continues to manage Dataset URIs, Model Artifacts, Pipeline Dependencies, and Workloads.
INNORIX performs the actual Transfers that Pipeline needs, such as Storage A → Compute B or Compute B → Storage C. So this isn't about adding a new AI Platform — it's a structure that adds a Data Movement Layer that your existing AI Platform can use.
DELIVERY MODES
Use the same Transfer from a One-Time Migration to repeated Delivery.
AI Data Delivery can start as a Dataset Migration and expand into a continuous Data Pipeline.
Delivery Modes
- One-Time Delivery · Move an existing Dataset/Model Repository to a new AI Environment
- Scheduled Delivery · Deliver newly created Datasets on a set cycle
- Event-Driven Delivery · Run based on requests from an existing system/Orchestrator
- Continuous Delivery · Repeatedly deliver the Datasets and Artifacts needed for Training/Evaluation/Inference
Orchestration stays with your existing AI Platform, while just the repeated, actual Data Movement can be separated out as a Transfer Layer.
DYNAMIC COMPUTE
Apply the same Data Delivery even as Compute changes.
AI Compute is increasingly allocated dynamically, and the Scheduler is responsible for placing Workloads on the right Node. INNORIX doesn't get involved in Scheduling — once the Scheduler decides where to run the Workload, INNORIX delivers the required Dataset and Model to that Compute.
This structure connects naturally with Dynamic Endpoint Transfer. AI Data Delivery focuses on what data to deliver, while Dynamic Endpoint Transfer extends how to connect the actual Target within changing Infrastructure.
MULTI ENVIRONMENT
Connect Multi-Cloud, On-Prem, and Object Storage.
AI Infrastructure may not stay confined to a single Cloud or a single Cluster.
Object Storage · Data Center · Private Storage
You can deliver an internal company Dataset to Cloud Compute, bring results generated in the Cloud back to a Private Environment, or use a Dataset in Object Storage across different AI Compute. Rather than turning each of these combinations into a separate Data Copy Project, operate the Storage ↔ Compute Data Movement within one Transfer Layer.
TRANSFER RECOVERY
Continue Data Delivery even after a Transfer is interrupted.
For large-scale Datasets, what matters more than the transfer failure itself is how you handle it afterward.
Instead of redelivering the entire Dataset from scratch, you can maintain transfer status, Resume an interrupted Transfer, Selective Retry only the files you need, and check per-file results.
10,000,000 Files
Instead of guessing whether a Transfer succeeded from the Training Framework's or Orchestrator's Log, check the result of the Data Delivery itself in INNORIX's Runs, File Status, Transfer History, and Receipt.
API INTERFACE
Connect with the AI Platform through an API.
Your existing AI Platform can call Data Delivery as an external Transfer Operation.
This lets the AI Platform keep its own Workflow and Scheduling Logic exactly as-is, while requesting Data Delivery when needed, receiving the result, and moving on to the next step.
FULL LIFECYCLE
Operate Data Movement before and after Training with one consistent approach.
The scope of AI Data Delivery isn't limited to the Dataset Copy before Training starts.
The execution and Dependencies of each stage are managed by your existing AI Orchestrator.
INNORIX connects the actual Data Movement of Datasets, Models, Checkpoints, and Results that occurs in between, through the same Transfer Layer.
CLEAR BOUNDARY
AI Infrastructure stays as it is. Data Delivery becomes one.
There's no need to make the two domains overlap. Your existing AI Platform continues to operate Compute and Workloads, while INNORIX moves the actual data between that Infrastructure.
GET STARTED
Configure Data Delivery that reaches all the way to AI Compute.
Deliver Datasets in Object Storage, Data Center, Cloud, and Private Storage to the AI Compute that needs them, and connect the Models, Checkpoints, and Results generated during Training and Inference back to their next destination.
Keep using your existing Scheduler and AI Orchestrator as-is, while operating the Data Movement across Storage ↔ Compute ↔ Storage as one Transfer Layer.