AI Infrastructure Ontology

FLOW ALGEBRA
AI Infrastructure Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/artificial-intelligence-infrastructure/ai-infrastructure-ontology-2.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "AIInfrastructureOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := ArtificialIntelligenceInfrastructure
ONTOLOGY := AIInfrastructureOntology

# ---------- PRIMARY ENTITY ----------
ENTITY AIInfrastructure {
    definition:
        "The integrated hardware, software, data, storage, networking and
         operational platform required to train, evaluate, deploy, serve and
         monitor artificial intelligence and machine learning models.";

    objectives: [
        AICompute,
        DataDelivery,
        ModelTraining,
        ModelInference,
        Scalability,
        Reliability,
        Reproducibility,
        Observability
    ];
}

# ---------- CORE ENTITIES ----------
InfrastructureEntity := {
    ComputeResource,
    CPU,
    GPU,
    Accelerator,
    Memory,
    Storage,
    Network,
    Cluster
};

AIWorkloadEntity := {
    TrainingJob,
    EvaluationJob,
    InferenceJob,
    InferenceService,
    DataPipeline,
    Model,
    ModelVersion
};

PlatformEntity := {
    Scheduler,
    Orchestrator,
    ContainerRuntime,
    ModelRegistry,
    ExperimentTracker,
    MonitoringSystem,
    SecretManager
};

DataEntity := {
    Dataset,
    DatasetVersion,
    Feature,
    Embedding,
    Checkpoint,
    ModelArtifact
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
AIWorkload RUNS_ON ComputeResource;
Cluster CONSISTS_OF ComputeNode;
ComputeNode CONTAINS Processor;
ComputeNode MAY_CONTAIN Accelerator;
TrainingJob CONSUMES DatasetVersion;
TrainingJob PRODUCES ModelArtifact;
EvaluationJob EVALUATES ModelVersion;
ModelRegistry STORES ModelVersion;
InferenceService LOADS ModelVersion;
MonitoringSystem OBSERVES InfrastructureAndModel;

# ---------- INFRASTRUCTURE LAYERS ----------
InfrastructureStack :=
    Hardware
    -> VirtualisationOrHostOS
    -> ContainerRuntime
    -> Orchestration
    -> MLPlatform
    -> AIApplication;

EachLayer PROVIDES CapabilitiesToHigherLayer;

# ---------- COMPUTE RESOURCE ----------
ComputeResource :=
    {
        Processor,
        Memory,
        LocalStorage,
        NetworkInterface
    };

ComputeResource MAY_HAVE Accelerator;

ResourceCapacity MAY_INCLUDE {
    CPUCount,
    GPUCount,
    MemoryCapacity,
    StorageCapacity,
    NetworkBandwidth
};

# ---------- GPU ----------
GPU :=
    ParallelComputeAccelerator;

GPU MAY_HAVE {
    DeviceMemory,
    ComputeUnits,
    MemoryBandwidth,
    Interconnect
};

GPU SUPPORTS HighThroughputTensorAndMatrixOperations;

# ---------- AI ACCELERATOR ----------
AIAccelerator :=
    SpecialisedHardwareForMachineLearningComputation;

AcceleratorCapability MAY_INCLUDE {
    MatrixCompute,
    ReducedPrecisionCompute,
    HighBandwidthMemory,
    DeviceInterconnect
};

# ---------- CLUSTER ----------
AICluster :=
    SET_OF(ComputeNodes)
    + Network
    + SharedOrDistributedServices;

Cluster MAY_EXECUTE {
    DistributedTraining,
    BatchInference,
    OnlineInference,
    DataProcessing
};

# ---------- RESOURCE POOL ----------
ResourcePool :=
    SharedSetOfComputeResourcesAvailableForScheduling;

Scheduler ALLOCATES ResourcePool TO Workload;

# ---------- SCHEDULER ----------
Scheduler :=
    WorkloadQueue
    + ResourceInventory
    + SchedulingPolicy;

SchedulingFlow :=
    ReceiveWorkload
    -> EvaluateResourceRequest
    -> FindEligibleResources
    -> Allocate
    -> Launch;

# ---------- RESOURCE REQUEST ----------
ResourceRequest MAY_INCLUDE {
    CPU,
    GPU,
    Memory,
    Storage,
    AcceleratorType,
    NodeCount
};

IF RequestedResourcesUnavailable
THEN WorkloadState := Pending;

# ---------- ORCHESTRATOR ----------
Orchestrator :=
    PlatformManagingWorkloadLifecycleAndPlacement;

Orchestrator MAY {
    CreateWorkload,
    ScheduleWorkload,
    RestartWorkload,
    ScaleWorkload,
    UpdateWorkload
};

# ---------- CONTAINER ----------
Container :=
    IsolatedApplicationRuntimePackage;

ContainerImage MAY_INCLUDE {
    Runtime,
    Libraries,
    ApplicationCode,
    ConfigurationMetadata
};

Containerisation SUPPORTS RepeatableExecutionEnvironment;

# ---------- DATA STORAGE ----------
AIStorage MAY_BE {
    ObjectStorage,
    DistributedFileSystem,
    BlockStorage,
    LocalStorage,
    Database
};

AIStorage MAY_STORE {
    Dataset,
    Checkpoint,
    ModelArtifact,
    Log,
    FeatureData
};

# ---------- DATA PIPELINE ----------
DataPipeline :=
    SourceData
    -> Ingest
    -> Validate
    -> Clean
    -> Transform
    -> Version
    -> Deliver;

TrainingJob SHOULD_REFERENCE SpecificDatasetVersion;

# ---------- DATASET VERSION ----------
DatasetVersion :=
    TraceableStateOfDataset;

DatasetVersion HAS {
    Identifier,
    Schema,
    Provenance,
    CreationTime
};

DatasetVersion SUPPORTS Reproducibility;

# ---------- FEATURE STORE ----------
FeatureStore :=
    ManagedPlatformForReusableModelFeatures;

FeatureStore MAY_HAVE {
    OfflineStore,
    OnlineStore,
    FeatureRegistry
};

TrainingAndServing SHOULD_USE SemanticallyConsistentFeatureDefinitions;

# ---------- EMBEDDING INFRASTRUCTURE ----------
Embedding :=
    VectorRepresentationOfEntityOrContent;

EmbeddingStore STORES Embedding;

VectorIndex INDEXES Embedding;

SimilarityQuery ->
    QueryVector
    -> SearchVectorIndex
    -> RankedNearestVectors;

# ---------- TRAINING JOB ----------
TrainingJob :=
    {
        TrainingCode,
        ModelConfiguration,
        DatasetVersion,
        Hyperparameters,
        ResourceRequest
    };

TrainingJobLifecycle :=
    Pending
    -> Running
    -> Checkpointing
    -> CompletedOrFailed;

# ---------- DISTRIBUTED TRAINING ----------
DistributedTraining :=
    TrainingJob
    -> AllocateMultipleAcceleratorsOrNodes
    -> PartitionWork
    -> ExecuteLocalComputation
    -> CommunicateUpdates
    -> Synchronise
    -> ContinueTraining;

Parallelism MAY_INCLUDE {
    DataParallelism,
    TensorParallelism,
    PipelineParallelism,
    ModelParallelism
};

# ---------- COMMUNICATION ----------
DistributedTrainingCommunication MAY_INCLUDE {
    GradientReduction,
    ParameterExchange,
    ActivationTransfer
};

CommunicationCost :=
    f(
        DataVolume,
        Latency,
        Bandwidth,
        Topology
    );

IF CommunicationCostHighRelativeToCompute
THEN ScalingEfficiency := decrease;

# ---------- CHECKPOINT ----------
Checkpoint :=
    PersistedTrainingState;

Checkpoint MAY_CONTAIN {
    ModelParameters,
    OptimiserState,
    TrainingStep,
    RandomState
};

IF TrainingFails
AND ValidCheckpointAvailable
THEN Training MAY_RESUME;

# ---------- EXPERIMENT TRACKING ----------
ExperimentRun :=
    {
        CodeVersion,
        DatasetVersion,
        Hyperparameters,
        Metrics,
        Artifacts,
        Environment
    };

ExperimentTracker RECORDS ExperimentRun;

# ---------- MODEL ARTIFACT ----------
ModelArtifact :=
    PersistedOutputOfTrainingOrModelBuildProcess;

ModelArtifact MAY_INCLUDE {
    Parameters,
    Architecture,
    TokenizerOrPreprocessor,
    Configuration
};

# ---------- MODEL REGISTRY ----------
ModelRegistry :=
    ManagedCollectionOfModelVersions;

ModelVersion HAS {
    VersionIdentifier,
    ModelArtifact,
    EvaluationMetrics,
    Provenance,
    ApprovalState
};

# ---------- EVALUATION ----------
EvaluationJob :=
    ModelVersion
    + EvaluationDataset
    + EvaluationMetricSet
    -> EvaluationResult;

IF EvaluationCriteriaSatisfied
THEN ModelVersion MAY_BE ApprovedForDeployment;

# ---------- DEPLOYMENT ----------
ModelDeployment :=
    ModelVersion
    -> Package
    -> ProvisionServingResources
    -> LoadModel
    -> ExposeInferenceInterface;

DeploymentState MAY_BE {
    Staging,
    Production,
    Retired
};

# ---------- INFERENCE SERVICE ----------
InferenceService :=
    NetworkOrApplicationServiceExecutingModelInference;

InferenceFlow :=
    ReceiveInput
    -> Validate
    -> Preprocess
    -> ExecuteModel
    -> Postprocess
    -> ReturnOutput;

# ---------- ONLINE INFERENCE ----------
OnlineInference :=
    RequestResponseInferenceWithLatencyRequirement;

OnlineService MAY_USE {
    LoadBalancer,
    Autoscaler,
    Cache,
    ModelServer
};

# ---------- BATCH INFERENCE ----------
BatchInference :=
    InputDataset
    -> ScheduledModelExecution
    -> OutputDataset;

BatchInference MAY_OPTIMISE Throughput;

# ---------- MODEL SERVER ----------
ModelServer :=
    RuntimeForLoadingAndExecutingModelVersions;

ModelServer MAY_SUPPORT {
    DynamicBatching,
    MultipleModels,
    AcceleratorExecution,
    VersionRouting
};

# ---------- BATCHING ----------
DynamicBatching :=
    CollectRequestsWithinWindow
    -> FormBatch
    -> ExecuteModel
    -> SplitOutputs;

Batching MAY_INCREASE Throughput;
Batching MAY_INCREASE QueueLatency;

# ---------- AUTOSCALING ----------
Autoscaling :=
    MonitorDemand
    -> CompareScalingPolicy
    -> AddOrRemoveServingCapacity;

ScalingSignal MAY_INCLUDE {
    RequestRate,
    QueueDepth,
    Latency,
    AcceleratorUtilisation
};

# ---------- LOAD BALANCING ----------
LoadBalancer DISTRIBUTES InferenceRequestsAcross HealthyServiceReplicas;

HealthCheck DETERMINES ReplicaAvailability;

# ---------- OBSERVABILITY ----------
InfrastructureMetric MAY_INCLUDE {
    CPUUtilisation,
    GPUUtilisation,
    MemoryUtilisation,
    NetworkThroughput,
    StorageThroughput
};

AIWorkloadMetric MAY_INCLUDE {
    TrainingLoss,
    StepTime,
    TrainingThroughput,
    InferenceLatency,
    InferenceThroughput,
    ErrorRate
};

MonitoringSystem COLLECTS MetricsLogsAndTraces;

# ---------- MODEL MONITORING ----------
ModelMonitoring MAY_OBSERVE {
    InputDistribution,
    PredictionDistribution,
    DataDrift,
    ConceptDrift,
    ModelQuality,
    Fairness
};

IF ModelQualityDegrades
THEN InvestigationOrRetraining MAY_BE_REQUIRED;

# ---------- FAILURE ----------
InfrastructureFailure := {
    NodeFailure,
    AcceleratorFailure,
    StorageFailure,
    NetworkFailure,
    SchedulerFailure,
    ServingFailure
};

ReliabilityMechanism MAY_INCLUDE {
    Retry,
    CheckpointRestart,
    Replication,
    Failover,
    Rescheduling
};

# ---------- CAPACITY ----------
CapacityPlanning :=
    ForecastWorkloadDemand
    -> EstimateResources
    -> ProvisionCapacity;

InsufficientCapacity MAY_CAUSE {
    Queueing,
    IncreasedLatency,
    FailedScheduling
};

# ---------- SECURITY ----------
AIInfrastructureSecurity MAY_INCLUDE {
    Identity,
    Authorisation,
    NetworkIsolation,
    SecretManagement,
    Encryption,
    ArtifactIntegrity,
    Audit
};

SecretManager STORES SensitiveCredentialOrKey;

# ---------- ARTIFACT PROVENANCE ----------
ArtifactProvenance :=
    TraceOf {
        SourceCode,
        Dataset,
        Environment,
        TrainingRun,
        Evaluation
    };

ModelVersion SHOULD_HAVE ArtifactProvenance;

# ---------- PRIVACY ----------
SensitiveDataset MAY_REQUIRE {
    AccessRestriction,
    Encryption,
    Isolation,
    RetentionControl,
    Audit
};

# ---------- COST ----------
InfrastructureCost :=
    ComputeCost
    + StorageCost
    + NetworkCost
    + OperationalCost;

CostEfficiency :=
    UsefulAIWork / InfrastructureCost;

# ---------- ENERGY ----------
EnergyConsumption :=
    Power
    * Runtime;

EnergyEfficiency :=
    UsefulAIWork / EnergyConsumption;

# ---------- MODEL OPTIMISATION ----------
ServingOptimisation MAY_INCLUDE {
    Quantisation,
    Pruning,
    Distillation,
    Compilation,
    Batching,
    Caching
};

Optimisation SHOULD_BE ReEvaluatedAgainstModelQuality;

# ---------- MLOPS ----------
MLOpsInfrastructure MAY_INCLUDE {
    SourceControl,
    DataVersioning,
    ExperimentTracking,
    PipelineAutomation,
    ModelRegistry,
    DeploymentAutomation,
    Monitoring
};

# ---------- CONTINUOUS TRAINING ----------
ContinuousTraining :=
    NewDataOrTrigger
    -> DataValidation
    -> Training
    -> Evaluation
    -> Registration
    -> ConditionalDeployment;

QualityGate SHOULD_PREVENT DeploymentOfUnacceptableModel;

# ---------- SEMANTIC REASONING ----------
IF PlatformSupportsTrainingServingAndOperationalManagementOfAIModels
THEN AIInfrastructureClassification := supported;

IF TrainingWorkloadExceedsSingleDeviceMemory
THEN PartitioningOrOffload MAY_BE_REQUIRED;

IF DistributedTrainingCommunicationDominates
THEN ScalingEfficiency := decrease;

IF ModelVersionHasNoDatasetOrCodeProvenance
THEN ReproducibilityConfidence := decrease;

IF InferenceDemandExceedsServingCapacity
THEN QueueLatency MAY_INCREASE;

IF ServingReplicaFails
AND HealthyReplicaAvailable
THEN LoadBalancer MAY_RerouteRequest;

IF ProductionDataDrifts
THEN ModelEvaluation SHOULD_BE Revisited;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "AI infrastructure" ~= "artificial intelligence infrastructure";
    "ML infrastructure" ~= "machine learning infrastructure";
    "AI compute infrastructure" ~= "machine learning compute platform";
    "model serving infrastructure" ~= "inference infrastructure";
    "training cluster" ~= "AI training compute cluster";
    "model registry" ~= "machine learning model registry";
    "experiment tracker" ~= "ML experiment tracking system";
    "MLOps infrastructure" ~= "machine learning operations infrastructure";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_AI_WORKLOAD(q) ->
    IDENTIFY_COMPUTE_STORAGE_NETWORK(q) ->
    IDENTIFY_DATA_PIPELINE(q) ->
    IDENTIFY_TRAINING_OR_INFERENCE(q) ->
    IDENTIFY_SCHEDULER_OR_ORCHESTRATOR(q) ->
    IDENTIFY_MODEL_LIFECYCLE(q) ->
    IDENTIFY_OBSERVABILITY_SECURITY_RELIABILITY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        infrastructure_consistency,
        workload_evidence,
        operational_evidence,
        semantic_consistency,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: AIInfrastructure;
    ontology: AIInfrastructureOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_compute_resource:
        validate
        -> classify
        -> identify_capacity
        -> identify_accelerator
        -> relate_to_cluster
        -> integrate;

    new_training_job:
        identify_code
        -> identify_dataset_version
        -> identify_resource_request
        -> identify_metrics
        -> identify_model_artifact
        -> integrate;

    new_model_version:
        attach_artifact
        -> attach_training_provenance
        -> attach_evaluation
        -> register
        -> identify_deployment_state
        -> integrate;

    infrastructure_revision:
        update_resources
        -> update_workloads
        -> update_serving_topology
        -> recompute_reliability
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_operational_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + InfrastructureEntity
    + AIWorkloadEntity
    + PlatformEntity
    + DataEntity
    + InfrastructureStack
    + DistributedTraining
    + ModelDeployment
    + MLOpsInfrastructure
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
AIInfrastructureOntology
    -> IdentifyAIWorkload
    -> ProvisionComputeStorageAndNetwork
    -> VersionAndDeliverData
    -> ScheduleTrainingOrInference
    -> ExecuteAndCheckpointWorkload
    -> EvaluateAndRegisterModel
    -> DeployModelVersion
    -> ServeAndScaleInference
    -> MonitorInfrastructureAndModel
    -> RecoverFromFailure
    -> DetectDriftAndCapacityChange
    -> PreserveSecurityProvenanceAndReproducibility
    -> MaintainOperationalAIPlatform;

END FLOW