TIO AUTOPLAT · Intelligence layer · Training foundation

AIBaseWide-domain model training foundation

Supports many kinds of training, including language, speech, vision, multimodal and time series; LLMs support LoRA fine-tuning, and training nodes scale on demand. One training foundation covers datasets, labeling, training and fine-tuning, evaluation gates, deployment and release; datasets come straight from DataHub, and releases sync to AIHub for inference.

Many
kinds of training: language, speech, vision, multimodal, time series
LoRA
fine-tuning for LLMs
Gate
failed evaluation can block release
On demand
training nodes scale as needed

Enterprises have many kinds of models, trained in silos

LLM fine-tuning, visual inspection, speech and time-series forecasting are split across tools and teams.

One process for every model
LLM fine-tuning, vision detection and time-series forecasting work very differently, yet share one process.
Data prepared elsewhere
Training data is exported, cleaned and uploaded by hand, with unclear versions and sources.
Evaluation disconnected from release
After training, teams don't know how good the model is or how to hand it to business systems.

One pipeline, seven steps

Pick a model type and watch it go from data to production.

  • Dataset：Import from DataHub tables, training sets or local files, with versions, splits and quality profiles
  • Annotate：Template-based annotation with pre-labeling, review, dispute resolution, agreement checks and gold standards
  • Train：Training recipes and data contracts, with live logs, metrics and checkpoints
  • Evaluate：Standard evaluation sets and comparison reports, with overfitting alerts
  • Deploy：Deploy inference services with unified resource and node management
  • Publish：Publish to the AIHub capability catalog and into role tool whitelists
  • Feedback：Production feedback and hard cases flow back into datasets for the next iteration
Dataset
Annotate
Train
Evaluate
Deploy
Publish
Feedback
Vision · 1/7 Dataset
Import from DataHub tables, training sets or local files, with versions, splits and quality profiles

Wide domain: one foundation, six model types

Each model type keeps its own labeling and training workflow, while sharing datasets, evaluation, deployment and release.

LLMLanguage understandingVisionSpeechMultimodalSmall models
LabelingSFT dialogs, preference pairs, rubricsSentence pairs and rerankBoxes and classesASR segments, TTS prosodyImage-text alignment and captionsTabular and time series
TrainingLoRA fine-tuningEmbedding / matching modelsDetection and classificationRecognition and synthesisMultimodal understandingXGBoost and more
Typical useEnterprise Q&A and generationRetrieval and semantic matchingIndustrial inspection, defect detectionSpeech recognition and synthesisImage-text understanding and searchForecasting and scoring

Training foundation: nodes, resources and gates

Training is not a script on one machine but a schedulable, observable platform capability that can block bad releases.

Training nodes scale on demand
Training nodes ship with 8 built-in trainers and install dependencies on demand at deployment; add nodes as needed, and failed jobs can be retried and reconciled.
Training nodes and resource profiles
Training nodes are registered centrally; resource profiles and runtime environments are injected per job, and GPU nodes are checked against memory tags.
Evaluation gate
Evaluation jobs and experiments; a failed evaluation can block release, and model artifacts are registered and verified.
Release and rollback
Deployment, health checks and retirement; releases and rollbacks sync to the platform model configuration for AIHub inference.

Key capabilities

Datasets
Import local files and DataHub tables, training sets or Parquet; versions, splits, derived comparisons, quality profiles and lineage.
Annotation workbench
Templates, model pre-labeling, review and disputes, agreement checks, gold standards and export.
Training and fine-tuning
Training profiles, data contract schemas, job retries and reconciliation, with logs, metrics, checkpoints and overfitting alerts.
Evaluation
Evaluation sets and multi-version comparison decide what gets released.
Inference deployment
Deploy inference services and manage training nodes with unified compute scheduling.
Security governance
Separate operator, security and audit roles; platform metadata with read-only references to tenant data.

Training methods and data sources per model domain are enabled per project; scope follows the delivery agreement.

The supply chain in AUTOPLAT

DataHub supplies data, AIBase trains models, AIHub uses them: training foundation and inference runtime are clearly separated.

A training foundation for every kind of enterprise model

Book a demo and follow a model from DataHub data through labeling, training and the evaluation gate to release in AIHub.