flowchart LR
Core AutoML Tasks
subgraph Core_AutoML_Tasks["Core AutoML Tasks"]
D[Tabular Engine]
E[Vision Engine]
F[AutoML+ Engine]
C -->|For tabular data| D
C -->|For vision tasks| E
C -->|For general or multi-modal tasks| F
end
Tabular Path
subgraph Tabular_Path["Tabular Path"]
D2[Hyperparameter Search]
D3[Model Ensembles]
D -->|Uses| D2
D -->|Uses| D3
end
Data & Model Storage (AutoDW)
subgraph AutoDW["AutoDW"]
G[Model Store]
H[Dataset Store]
I[Session Store]
C -->|Saves model| G
C -->|Saves data| H
C -->|Saves session info| I
G1[Model Metadata]
H1[Dataset Metadata]
G <-->|Stores handler & ethics info| G1
H <-->|Stores splits & metadata| H1
end
Semantic & Fairness
C -->|Queries task context| M[Semantic Knowledge Graph]
M -->|Returns insights| C
M -->|Feeds| J
M -->|Informs| N[Fairness Evaluation]
N -->|Sends feedback| C
%% Styles
classDef core fill:#fef3c7,stroke:#f59e0b,stroke-width:2px,color:black;
classDef data fill:#dbeafe,stroke:#3b82f6,stroke-width:2px,color:black;
classDef vision fill:#ede9fe,stroke:#8b5cf6,stroke-width:2px,color:black;
classDef xai fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:black;
classDef external fill:#f0f9ff,stroke:#0ea5e9,stroke-width:2px,color:black;
classDef task fill:#fef9c3,stroke:#eab308,stroke-width:2px,color:black;
class D,D2,D3,F core;
class E,E2,E3 vision;
class G,H,I,G1,H1 data;
class J,J1,K,L,N xai;
class M external;
class T1,T2,T3 task;
sequenceDiagram
participant User as Intent Recognition
participant Controller
participant AutoML as AutoML Engine
participant Tabular as Tabular Engine
participant Vision as Vision Engine
participant AutoMLPlus as AutoML+ Engine
participant ModelStore as Model Store
participant DatasetStore as Dataset Store
participant SessionStore as Session Store
participant XAI as XAI Layer
participant UI as User Interface
participant SKG as Semantic Knowledge Graph
participant FairEval as Fairness Evaluation
User->>Controller: Extract task type & user input
Controller->>AutoML: Request requirements
AutoML-->>Controller: Return requirements JSON
Controller->>AutoML: Send config, data, task info
AutoML->>Tabular: For tabular data
AutoML->>Vision: For vision tasks
AutoML->>AutoMLPlus: For general/multi-modal tasks
Tabular->>Tabular: Uses hyperparameter search
Tabular->>Tabular: Uses model ensembles
Vision->>Vision: Uses transfer learning
Vision->>Vision: Avoids neural architecture search (cost)
AutoMLPlus->>Ensuring Unbiased AI in Autonomous Vehicles: Used by Ensuring Unbiased AI in Autonomous Vehicles
Vision->>Ensuring Unbiased AI in Autonomous Vehicles: Used by Ensuring Unbiased AI in Autonomous Vehicles
Vision->>Compliance Screener: Used by Compliance Screener
AutoMLPlus->>Website Accessibility Checker: Used by Website Accessibility Checker
AutoML->>ModelStore: Save model
AutoML->>DatasetStore: Save data
AutoML->>SessionStore: Save session info
ModelStore->>ModelStore: Store handler & ethics metadata
DatasetStore->>DatasetStore: Store splits & metadata
AutoML->>XAI: Trigger XAI Layer
XAI->>AutoML: Receive model, task, test data
XAI->>UI: Generate SHAP/GradCAM/Fairness metrics
UI->>User: Provide evaluation results
UI->>EndUser: Report transparency & trust
AutoML->>SKG: Query task context
SKG-->>AutoML: Return insights
SKG->>XAI: Feed insights
SKG->>FairEval: Inform fairness evaluation
FairEval->>AutoML: Send feedback