Model Serving Ontology

FLOW ALGEBRA
Model Serving Ontology
Kireth Semantic Research Archive

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

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "ModelServingOntology" v4.0

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

# ---------- PRIMARY ENTITY ----------
ENTITY ModelServing {
    definition:
        "The operational process and infrastructure used to load, expose,
         execute, scale, route, monitor and maintain trained artificial
         intelligence models so applications can obtain predictions,
         generated outputs or other inference results.";

    objectives: [
        ReliableInference,
        LowLatency,
        HighThroughput,
        Scalability,
        ModelVersionControl,
        Availability,
        Observability
    ];
}

# ---------- CORE ENTITIES ----------
ServingEntity := {
    Model,
    ModelVersion,
    ModelArtifact,
    ModelServer,
    InferenceRuntime,
    Endpoint,
    Request,
    Response
};

InfrastructureEntity := {
    ServingReplica,
    LoadBalancer,
    RequestQueue,
    Scheduler,
    Autoscaler,
    Cache,
    Device,
    HealthCheck
};

DeploymentEntity := {
    Deployment,
    Release,
    TrafficPolicy,
    Rollout,
    Rollback,
    ModelRegistry
};

ServingState := {
    Loading,
    Ready,
    Degraded,
    Draining,
    Unavailable,
    Retired
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
ModelRegistry STORES ModelVersion;
Deployment SELECTS ModelVersion;
ModelServer LOADS ModelArtifact;
InferenceRuntime EXECUTES Model;
Endpoint RECEIVES Request;
LoadBalancer ROUTES Request TO ServingReplica;
ServingReplica HOSTS ModelServer;
ModelServer RETURNS Response;
MonitoringSystem OBSERVES ServingBehaviour;

# ---------- MODEL SERVING FLOW ----------
ApprovedModelVersion
    -> CreateDeployment
    -> ProvisionServingResources
    -> LoadModelArtifact
    -> InitialiseRuntime
    -> PerformHealthCheck
    -> ExposeEndpoint
    -> RouteInferenceRequests
    -> ExecuteModel
    -> ReturnResponse
    -> MonitorPerformanceAndQuality
    -> ScaleUpdateOrRollback;

# ---------- MODEL VERSION ----------
ModelVersion :=
    IdentifiedStateOfTrainedModel;

ModelVersion HAS {
    VersionIdentifier,
    ModelArtifact,
    InputContract,
    OutputContract,
    EvaluationMetadata,
    Provenance
};

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

ModelRegistry MAY_TRACK {
    ApprovalState,
    DeploymentState,
    EvaluationMetrics,
    Lineage
};

OnlyApprovedModelVersion MAY_BE EligibleForProductionAccordingToPolicy;

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

ModelArtifact MAY_INCLUDE {
    Parameters,
    Architecture,
    RuntimeFormat,
    Tokenizer,
    Preprocessor,
    Configuration
};

# ---------- DEPLOYMENT ----------
ModelDeployment :=
    ModelVersion
    + ServingConfiguration
    + InfrastructureAllocation;

Deployment MAY_HAVE {
    Environment,
    ReplicaCount,
    DeviceType,
    ResourceLimits,
    TrafficPolicy
};

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

ModelServer MAY_SUPPORT {
    MultipleModels,
    MultipleVersions,
    DynamicBatching,
    Metrics,
    HealthChecks
};

# ---------- INFERENCE RUNTIME ----------
InferenceRuntime :=
    ExecutionEnvironmentForModelPrediction;

Runtime MAY_USE {
    CPU,
    GPU,
    AIAccelerator
};

RuntimeCompatibility MUST_MATCH ModelArtifactAndDevice;

# ---------- MODEL LOADING ----------
ModelLoad :=
    RetrieveArtifact
    -> ValidateArtifact
    -> AllocateMemory
    -> InitialiseRuntime
    -> LoadParameters
    -> WarmModel
    -> Ready;

IF ModelLoadFails
THEN ServingState := Unavailable;

# ---------- WARMUP ----------
Warmup :=
    ExecuteRepresentativeOrSyntheticInputs
    TO InitialiseRuntimeCachesCompilationAndDeviceState;

Warmup MAY_REDUCE FirstRequestLatency;

# ---------- ENDPOINT ----------
ServingEndpoint :=
    InterfaceThroughWhichClientRequestsInference;

Endpoint MAY_USE {
    HTTP,
    RPC,
    MessageQueue,
    LocalInterface
};

Endpoint HAS {
    Address,
    InputContract,
    AuthenticationPolicy,
    TimeoutPolicy
};

# ---------- REQUEST ----------
InferenceRequest :=
    {
        RequestIdentifier,
        Input,
        ModelTarget,
        Metadata
    };

Request MAY_TARGET {
    DefaultModel,
    SpecificModel,
    SpecificVersion
};

# ---------- REQUEST VALIDATION ----------
RequestValidation :=
    CheckAuthentication
    -> CheckAuthorisation
    -> ValidateInputSchema
    -> ValidateSizeAndLimits;

IF RequestInvalid
THEN ModelExecution SHOULD_NOT_PROCEED;

# ---------- PREPROCESSING ----------
Preprocessing :=
    RawInput
    -> Validate
    -> Transform
    -> Encode
    -> ModelInput;

Preprocessing MUST_BE CompatibleWithTrainingAndModelContract;

# ---------- REQUEST QUEUE ----------
RequestQueue STORES PendingRequests;

QueueDepth :=
    Count(PendingRequests);

QueueTime :=
    ExecutionStart - RequestArrival;

IF ArrivalRate > SustainableServingRate
THEN QueueDepth MAY_INCREASE;

# ---------- BATCHING ----------
ServingBatch :=
    CompatibleInferenceRequestsCombinedForExecution;

Batching MAY_INCREASE Throughput;

Batching MAY_AFFECT {
    QueueLatency,
    DeviceUtilisation,
    MemoryUse
};

# ---------- DYNAMIC BATCHING ----------
DynamicBatching :=
    QueueRequests
    -> WaitWithinConfiguredWindow
    -> SelectCompatibleRequests
    -> FormBatch
    -> Execute
    -> SplitResults;

# ---------- INFERENCE ----------
ServingInference :=
    ModelInput
    -> RuntimeExecution
    -> ModelOutput;

ModelExecution MAY_BE {
    Synchronous,
    Asynchronous,
    Streaming
};

# ---------- POSTPROCESSING ----------
Postprocessing :=
    ModelOutput
    -> Decode
    -> Transform
    -> ApplyBusinessFormatting
    -> ResponsePayload;

# ---------- RESPONSE ----------
InferenceResponse :=
    {
        RequestIdentifier,
        Result,
        ModelVersion,
        OptionalMetadata
    };

Response MAY_INCLUDE {
    Prediction,
    Score,
    Probability,
    Embedding,
    GeneratedContent
};

# ---------- STREAMING ----------
StreamingServing :=
    ProduceAndTransmitPartialOutputsDuringInference;

Streaming MAY_REDUCE TimeToFirstOutput;

Stream MAY_END_ON {
    Completion,
    StopCondition,
    ClientCancellation,
    Error
};

# ---------- SERVING REPLICA ----------
ServingReplica :=
    IndependentModelServingInstance;

Replica HAS {
    HealthState,
    LoadedModelVersion,
    ResourceAllocation
};

MultipleReplicas MAY_SUPPORT CapacityAndAvailability;

# ---------- LOAD BALANCER ----------
LoadBalancer :=
    RequestRouterAcrossServingReplicas;

RoutingPolicy MAY_INCLUDE {
    RoundRobin,
    LeastLoaded,
    Weighted,
    HashBased,
    LocalityAware
};

LoadBalancer SHOULD_ROUTE_TO HealthyReplica;

# ---------- HEALTH CHECK ----------
HealthCheck MAY_VERIFY {
    ProcessAlive,
    RuntimeReady,
    ModelLoaded,
    DeviceAvailable,
    DependencyAvailable
};

ReadinessCheck DETERMINES WhetherReplicaShouldReceiveTraffic;

LivenessCheck DETERMINES WhetherReplicaMayRequireRestart;

# ---------- AUTOSCALING ----------
Autoscaler :=
    ObserveDemandAndPerformance
    -> CompareScalingPolicy
    -> AdjustReplicaCountOrResources;

ScalingSignal MAY_INCLUDE {
    RequestRate,
    QueueDepth,
    Latency,
    CPUUtilisation,
    GPUUtilisation
};

# ---------- SCALE OUT ----------
IF DemandHigh
AND CapacityAvailable
THEN ReplicaCount MAY_INCREASE;

# ---------- SCALE IN ----------
IF DemandLow
AND MinimumCapacityPreserved
THEN ReplicaCount MAY_DECREASE;

ScaleIn SHOULD_DRAIN ActiveRequestsWhereRequired;

# ---------- DRAINING ----------
Draining :=
    StopAcceptingNewRequests
    + CompleteOrTerminateExistingRequestsAccordingToPolicy;

Draining MAY_PRECEDE {
    Shutdown,
    Update,
    ScaleIn
};

# ---------- TRAFFIC ROUTING ----------
TrafficPolicy :=
    RuleMappingRequestsToModelVersionsOrDeployments;

TrafficPolicy MAY_SUPPORT {
    StableRouting,
    CanaryRouting,
    ABRouting,
    ShadowRouting
};

# ---------- CANARY DEPLOYMENT ----------
CanaryDeployment :=
    RouteSmallTrafficFractionToNewModelVersion
    -> ObserveMetrics
    -> IncreaseOrRollback;

Canary MAY_REDUCE DeploymentRisk;

# ---------- BLUE GREEN ----------
BlueGreenDeployment :=
    MaintainOldAndNewServingEnvironments
    -> ValidateNew
    -> SwitchTraffic;

# ---------- SHADOW DEPLOYMENT ----------
ShadowDeployment :=
    DuplicateSelectedRequestsToCandidateModel
    WITHOUT UsingCandidateResponseForPrimaryUserResult;

ShadowEvaluation MAY_COMPARE ProductionBehaviourSafely;

# ---------- ROLLBACK ----------
Rollback :=
    StopOrReduceTrafficToProblematicVersion
    -> RestorePreviousKnownGoodVersion;

Rollback MAY_BE TriggeredBy {
    ErrorRate,
    LatencyRegression,
    QualityRegression,
    OperationalFailure
};

# ---------- VERSION ROUTING ----------
VersionRouter MAY_SELECT ModelVersionBasedOn {
    TrafficPercentage,
    Header,
    UserCohort,
    Experiment,
    Compatibility
};

# ---------- CACHE ----------
InferenceCache :=
    StoredInferenceResultOrIntermediateData;

CacheKey SHOULD_INCLUDE Relevant {
    ModelVersion,
    InputIdentity,
    Configuration
};

CacheUse MUST_RESPECT PrivacyAndFreshness;

# ---------- MODEL CACHE ----------
ServingNode MAY_CACHE ModelArtifactLocally;

LocalModelCache MAY_REDUCE StartupAndScaleOutTime;

# ---------- GPU SERVING ----------
GPUModelServing :=
    ModelServer
    -> LoadModelOnGPU
    -> BatchRequests
    -> ExecuteAcceleratedInference;

GPUServingPerformance MAY_DEPEND_ON {
    BatchSize,
    ModelSize,
    DeviceMemory,
    KernelEfficiency
};

# ---------- MULTI-MODEL SERVING ----------
ModelServer MAY_HOST MultipleModels;

MultiModelServing REQUIRES {
    MemoryManagement,
    ModelLoadingPolicy,
    RequestRouting
};

# ---------- MODEL EVICTION ----------
IF DeviceMemoryInsufficient
THEN ModelServer MAY_EVICT LessUsedModelAccordingToPolicy;

ModelReload MAY_INCREASE RequestLatency;

# ---------- LATENCY ----------
EndToEndServingLatency :=
    NetworkTime
    + QueueTime
    + PreprocessingTime
    + InferenceTime
    + PostprocessingTime;

LatencyPercentile MAY_INCLUDE {
    P50,
    P95,
    P99
};

# ---------- THROUGHPUT ----------
ServingThroughput :=
    CompletedInferenceRequests / Time;

# ---------- AVAILABILITY ----------
ServingAvailability :=
    SuccessfulServiceTime / RequiredServiceTime;

Availability MAY_BE_SUPPORTED_BY {
    MultipleReplicas,
    HealthChecks,
    Failover,
    MultiZoneDeployment
};

# ---------- SERVICE LEVEL ----------
ServingSLO MAY_DEFINE {
    AvailabilityTarget,
    LatencyTarget,
    ErrorRateTarget,
    ThroughputTarget
};

# ---------- FAILURE ----------
ServingFailure := {
    ModelLoadFailure,
    RuntimeFailure,
    DeviceFailure,
    OutOfMemory,
    DependencyFailure,
    Timeout,
    NetworkFailure
};

FailureHandling MAY_INCLUDE {
    Retry,
    Reroute,
    Restart,
    FallbackModel,
    ErrorResponse
};

# ---------- FALLBACK ----------
FallbackServing :=
    PreferredModelUnavailableOrOverloaded
    -> SelectAlternativeModelOrResponseStrategy;

Fallback MAY_TRADE ModelQualityForAvailabilityOrLatency;

# ---------- OBSERVABILITY ----------
ServingObservability MAY_INCLUDE {
    RequestCount,
    Latency,
    QueueDepth,
    Throughput,
    ErrorRate,
    ReplicaHealth,
    ResourceUtilisation,
    ModelVersion
};

# ---------- LOGGING ----------
ServingLog MAY_RECORD {
    RequestIdentifier,
    ModelVersion,
    Timing,
    Status,
    Error
};

SensitiveInputOrOutput SHOULD_NOT_BE LoggedWithoutAppropriatePolicy;

# ---------- TRACING ----------
DistributedTrace MAY_CONNECT {
    Gateway,
    LoadBalancer,
    ModelServer,
    Preprocessing,
    InferenceRuntime,
    DownstreamService
};

# ---------- MODEL MONITORING ----------
ModelBehaviourMonitoring MAY_TRACK {
    InputDistribution,
    OutputDistribution,
    Confidence,
    Drift,
    QualityWhereLabelsAvailable
};

OperationalHealth != ModelQuality;

Both SHOULD_BE MonitoredWhereRelevant;

# ---------- DATA DRIFT ----------
DataDrift :=
    ServingInputDistributionChangesRelativeToReference;

IF DriftSignificant
THEN ModelEvaluationOrRetraining MAY_BE_REQUIRED;

# ---------- SECURITY ----------
ModelServingSecurity MAY_INCLUDE {
    Authentication,
    Authorisation,
    Encryption,
    InputValidation,
    RateLimiting,
    ModelIntegrity,
    SecretManagement
};

# ---------- RATE LIMITING ----------
RateLimit MAY_CONTROL RequestsBy {
    Client,
    Identity,
    Token,
    Endpoint,
    TimeWindow
};

# ---------- PRIVACY ----------
ServingInput MAY_CONTAIN SensitiveData;

PrivacyControl MAY_INCLUDE {
    DataMinimisation,
    Encryption,
    LoggingRestriction,
    RetentionControl,
    CacheRestriction
};

# ---------- COST ----------
ServingCost :=
    Compute
    + Memory
    + Storage
    + Network
    + OperationalOverhead;

CostPerInference :=
    ServingCost / CompletedInferences;

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

Optimisation MUST_BE EvaluatedAgainst RequiredModelQuality;

# ---------- CAPACITY PLANNING ----------
ServingCapacityPlanning :=
    ForecastRequestDemand
    -> EstimateRequiredThroughput
    -> EstimateReplicaAndDeviceCapacity
    -> ProvisionHeadroom;

# ---------- SEMANTIC REASONING ----------
IF TrainedModelExposedThroughOperationalInferenceService
THEN ModelServingClassification := supported;

IF QueueDepthRisesPersistently
THEN ServingCapacity MAY_BE Insufficient;

IF ReplicaFailsHealthCheck
THEN NewTraffic SHOULD_NOT_BE RoutedToReplica;

IF CandidateModelShowsLatencyOrQualityRegression
THEN Rollback MAY_BE Appropriate;

IF CacheHitForEquivalentInputAndModelVersion
THEN ModelExecution MAY_BE Avoided;

IF AutoscalingAddsHealthyReplicas
THEN AvailableServingCapacity MAY_INCREASE;

IF OperationalMetricsHealthy
THEN ModelQuality IS_NOT_AUTOMATICALLY Guaranteed;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "model serving" ~= "AI model serving";
    "inference serving" ~= "model serving";
    "model server" ~= "inference server" WHEN context == AI_model;
    "serving replica" ~= "model serving instance";
    "model endpoint" ~= "inference endpoint";
    "canary deployment" ~= "canary model rollout" WHEN context == serving;
    "model rollback" ~= "serving version rollback";
    "dynamic batching" ~= "inference request batching";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_MODEL_VERSION(q) ->
    IDENTIFY_DEPLOYMENT(q) ->
    IDENTIFY_ENDPOINT_AND_REQUEST(q) ->
    IDENTIFY_REPLICAS_AND_ROUTING(q) ->
    IDENTIFY_INFERENCE_EXECUTION(q) ->
    IDENTIFY_SCALING_AND_HEALTH(q) ->
    IDENTIFY_OBSERVABILITY_SECURITY_MODEL_QUALITY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        serving_architecture_consistency,
        runtime_evidence,
        operational_evidence,
        model_quality_evidence,
        contextual_relevance
    );

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

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: ModelServing;
    ontology: ModelServingOntology;
    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_model_version:
        validate
        -> identify_artifact
        -> identify_contract
        -> attach_evaluation
        -> attach_provenance
        -> integrate;

    new_deployment:
        identify_model_version
        -> identify_serving_configuration
        -> provision_replicas
        -> define_traffic_policy
        -> integrate;

    new_serving_evidence:
        update_latency
        -> update_throughput
        -> update_error_rate
        -> update_health
        -> update_model_behaviour
        -> integrate;

    deployment_revision:
        update_model_version
        -> update_replica_count
        -> update_traffic_policy
        -> recompute_capacity
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + ServingEntity
    + InfrastructureEntity
    + DeploymentEntity
    + ServingState
    + ModelDeployment
    + TrafficPolicy
    + ServingObservability
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
ModelServingOntology
    -> SelectApprovedModelVersion
    -> CreateServingDeployment
    -> ProvisionAndWarmReplicas
    -> ExposeInferenceEndpoint
    -> ValidateIncomingRequests
    -> QueueBatchAndRouteTraffic
    -> ExecuteModelInference
    -> ReturnOrStreamResponse
    -> MonitorLatencyThroughputAndHealth
    -> ScaleServingCapacity
    -> RolloutOrRollbackModelVersions
    -> MonitorModelBehaviourAndSecurity
    -> MaintainReliableScalableModelServing;

END FLOW