agents

%% hot_swappable_ai_models.erl
%% Hot-swappable AI model serving with zero-downtime updates
%% Leverages Erlang's hot code loading for seamless AI model transitions
-module(hot_swappable_ai_models).
-behaviour(gen_server).

-export([
    start_link/0,
    load_model/3,
    swap_model/3,
    get_model_prediction/3,
    gradual_model_rollout/4,
    a_b_test_models/4,
    ensemble_prediction/3,
    model_version_control/2,
    adaptive_model_selection/3,
    real_time_model_training/3,
    model_performance_monitoring/2,
    rollback_model/2,
    federated_model_update/3,
    neural_architecture_search/3
]).

-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).

-define(MODEL_REGISTRY, ai_model_registry).
-define(MODEL_VERSIONS, model_version_history).
-define(PERFORMANCE_METRICS, model_performance_metrics).
-define(TRAFFIC_ROUTING, model_traffic_routing).

-record(state, {
    active_models = #{},
    model_versions = #{},
    traffic_router,
    performance_monitor,
    rollout_manager,
    version_controller,
    ensemble_coordinator,
    neural_search_engine,
    federated_learning_coordinator,
    hot_swap_scheduler
}).

-record(ai_model, {
    id,
    name,
    version,
    algorithm_type, % neural_network, decision_tree, svm, transformer, etc.
    model_data,
    preprocessing_pipeline,
    postprocessing_pipeline,
    inference_function,
    model_metadata = #{},
    performance_requirements = #{},
    resource_requirements = #{},
    supported_inputs = [],
    expected_outputs = [],
    load_time,
    last_updated,
    usage_statistics = #{},
    a_b_test_group = undefined
}).

-record(model_swap_plan, {
    id,
    source_model,
    target_model,
    swap_strategy, % immediate, gradual, canary, blue_green
    traffic_percentage = 0.0,
    rollout_schedule = [],
    success_criteria = #{},
    rollback_conditions = #{},
    monitoring_metrics = [],
    started_at,
    estimated_completion
}).

-record(prediction_request, {
    request_id,
    input_data,
    model_preferences = [],
    quality_requirements = #{},
    timeout = 5000,
    ensemble_strategy = undefined,
    client_context = #{}
}).

-record(prediction_response, {
    request_id,
    prediction,
    confidence_score,
    model_used,
    inference_time,
    quality_metrics = #{},
    ensemble_details = undefined
}).

%% Public API

start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

%% Load new AI model with hot deployment
load_model(ModelId, ModelSpec, DeploymentOptions) ->
    gen_server:call(?MODULE, {load_model, ModelId, ModelSpec, DeploymentOptions}, 30000).

%% Hot-swap AI model with zero downtime
swap_model(OldModelId, NewModelId, SwapStrategy) ->
    gen_server:call(?MODULE, {swap_model, OldModelId, NewModelId, SwapStrategy}, 60000).

%% Get prediction from AI model with intelligent routing
get_model_prediction(ModelId, InputData, Options) ->
    gen_server:call(?MODULE, {predict, ModelId, InputData, Options}, 10000).

%% Gradual rollout of new model version
gradual_model_rollout(ModelId, NewVersion, RolloutSchedule, Criteria) ->
    gen_server:call(?MODULE, {gradual_rollout, ModelId, NewVersion, RolloutSchedule, Criteria}).

%% A/B test between model versions
a_b_test_models(ModelA, ModelB, TrafficSplit, TestDuration) ->
    gen_server:call(?MODULE, {a_b_test, ModelA, ModelB, TrafficSplit, TestDuration}).

%% Ensemble prediction combining multiple models
ensemble_prediction(ModelIds, InputData, EnsembleStrategy) ->
    gen_server:call(?MODULE, {ensemble_predict, ModelIds, InputData, EnsembleStrategy}).

%% Model version control with branching and merging
model_version_control(ModelId, VersionOperation) ->
    gen_server:call(?MODULE, {version_control, ModelId, VersionOperation}).

%% Adaptive model selection based on context
adaptive_model_selection(InputData, Context, SelectionCriteria) ->
    gen_server:call(?MODULE, {adaptive_selection, InputData, Context, SelectionCriteria}).

%% Real-time model training and deployment
real_time_model_training(ModelId, TrainingData, TrainingOptions) ->
    gen_server:call(?MODULE, {real_time_training, ModelId, TrainingData, TrainingOptions}).

%% Monitor model performance with alerts
model_performance_monitoring(ModelId, MonitoringConfig) ->
    gen_server:call(?MODULE, {monitor_performance, ModelId, MonitoringConfig}).

%% Rollback to previous model version
rollback_model(ModelId, TargetVersion) ->
    gen_server:call(?MODULE, {rollback_model, ModelId, TargetVersion}).

%% Federated learning with distributed model updates
federated_model_update(ModelId, FederatedUpdates, AggregationStrategy) ->
    gen_server:call(?MODULE, {federated_update, ModelId, FederatedUpdates, AggregationStrategy}).

%% Neural architecture search with hot deployment
neural_architecture_search(SearchSpace, ObjectiveFunction, SearchBudget) ->
    gen_server:call(?MODULE, {neural_search, SearchSpace, ObjectiveFunction, SearchBudget}).

%% Gen_server callbacks

init([]) ->
    % Create ETS tables for model management
    ets:new(?MODEL_REGISTRY, [named_table, public, {keypos, #ai_model.id}]),
    ets:new(?MODEL_VERSIONS, [named_table, public, ordered_set]),
    ets:new(?PERFORMANCE_METRICS, [named_table, public, set]),
    ets:new(?TRAFFIC_ROUTING, [named_table, public, set]),
    
    % Start supporting processes
    TrafficRouter = spawn_link(fun() -> traffic_router_loop(#{}) end),
    PerformanceMonitor = spawn_link(fun() -> performance_monitor_loop(#{}) end),
    RolloutManager = spawn_link(fun() -> rollout_manager_loop(#{}) end),
    VersionController = spawn_link(fun() -> version_controller_loop(#{}) end),
    EnsembleCoordinator = spawn_link(fun() -> ensemble_coordinator_loop(#{}) end),
    NeuralSearchEngine = spawn_link(fun() -> neural_search_loop(#{}) end),
    FederatedCoordinator = spawn_link(fun() -> federated_learning_loop(#{}) end),
    HotSwapScheduler = spawn_link(fun() -> hot_swap_scheduler_loop(#{}) end),
    
    {ok, #state{
        traffic_router = TrafficRouter,
        performance_monitor = PerformanceMonitor,
        rollout_manager = RolloutManager,
        version_controller = VersionController,
        ensemble_coordinator = EnsembleCoordinator,
        neural_search_engine = NeuralSearchEngine,
        federated_learning_coordinator = FederatedCoordinator,
        hot_swap_scheduler = HotSwapScheduler
    }}.

handle_call({load_model, ModelId, ModelSpec, Options}, _From, State) ->
    Result = load_ai_model(ModelId, ModelSpec, Options, State),
    NewState = update_active_models(ModelId, Result, State),
    {reply, Result, NewState};

handle_call({swap_model, OldId, NewId, Strategy}, _From, State) ->
    Result = execute_hot_model_swap(OldId, NewId, Strategy, State),
    NewState = update_model_routing(Result, State),
    {reply, Result, NewState};

handle_call({predict, ModelId, InputData, Options}, _From, State) ->
    Result = route_prediction_request(ModelId, InputData, Options, State),
    {reply, Result, State};

handle_call({gradual_rollout, ModelId, Version, Schedule, Criteria}, _From, State) ->
    Result = initiate_gradual_rollout(ModelId, Version, Schedule, Criteria, State),
    {reply, Result, State};

handle_call({a_b_test, ModelA, ModelB, Split, Duration}, _From, State) ->
    Result = setup_a_b_test(ModelA, ModelB, Split, Duration, State),
    {reply, Result, State};

handle_call({ensemble_predict, ModelIds, InputData, Strategy}, _From, State) ->
    Result = execute_ensemble_prediction(ModelIds, InputData, Strategy, State),
    {reply, Result, State};

handle_call({version_control, ModelId, Operation}, _From, State) ->
    Result = execute_version_control_operation(ModelId, Operation, State),
    {reply, Result, State};

handle_call({adaptive_selection, InputData, Context, Criteria}, _From, State) ->
    Result = perform_adaptive_model_selection(InputData, Context, Criteria, State),
    {reply, Result, State};

handle_call({real_time_training, ModelId, TrainingData, Options}, _From, State) ->
    Result = execute_real_time_training(ModelId, TrainingData, Options, State),
    {reply, Result, State};

handle_call({monitor_performance, ModelId, Config}, _From, State) ->
    Result = setup_performance_monitoring(ModelId, Config, State),
    {reply, Result, State};

handle_call({rollback_model, ModelId, TargetVersion}, _From, State) ->
    Result = execute_model_rollback(ModelId, TargetVersion, State),
    {reply, Result, State};

handle_call({federated_update, ModelId, Updates, Strategy}, _From, State) ->
    Result = process_federated_model_update(ModelId, Updates, Strategy, State),
    {reply, Result, State};

handle_call({neural_search, SearchSpace, Objective, Budget}, _From, State) ->
    Result = initiate_neural_architecture_search(SearchSpace, Objective, Budget, State),
    {reply, Result, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast({model_swap_step, SwapPlanId, Step}, State) ->
    execute_swap_step(SwapPlanId, Step),
    {noreply, State};

handle_cast({performance_alert, ModelId, AlertData}, State) ->
    handle_performance_alert(ModelId, AlertData, State),
    {noreply, State};

handle_cast({rollout_progress, RolloutId, Progress}, State) ->
    update_rollout_progress(RolloutId, Progress),
    {noreply, State};

handle_cast({federated_learning_update, ModelId, UpdateData}, State) ->
    process_federated_update(ModelId, UpdateData),
    {noreply, State};

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info({hot_code_change, Module, NewVersion}, State) ->
    % Handle hot code updates for model modules
    NewState = handle_hot_code_change(Module, NewVersion, State),
    {noreply, NewState};

handle_info({model_health_check, ModelId}, State) ->
    perform_model_health_check(ModelId),
    schedule_next_health_check(ModelId),
    {noreply, State};

handle_info({neural_search_result, SearchId, Result}, State) ->
    process_neural_search_result(SearchId, Result),
    {noreply, State};

handle_info({traffic_routing_update, RoutingConfig}, State) ->
    update_traffic_routing_config(RoutingConfig),
    {noreply, State};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, _State) ->
    ok.

code_change(OldVsn, State, Extra) ->
    % Handle hot code upgrades for the model server itself
    NewState = migrate_model_state(OldVsn, State, Extra),
    {ok, NewState}.

%% Core Model Management Implementation

load_ai_model(ModelId, ModelSpec, Options, _State) ->
    try
        % Phase 1: Validate model specification
        ValidationResult = validate_model_spec(ModelSpec),
        
        case ValidationResult of
            {ok, ValidatedSpec} ->
                % Phase 2: Compile model for hot loading
                CompiledModel = compile_model_for_hot_loading(ValidatedSpec, Options),
                
                % Phase 3: Create model record
                Model = create_model_record(ModelId, CompiledModel, Options),
                
                % Phase 4: Initialize model in isolated process
                {ok, ModelProcess} = initialize_model_process(Model),
                
                % Phase 5: Register model in hot-swappable registry
                register_model_in_registry(Model, ModelProcess),
                
                % Phase 6: Set up monitoring and health checks
                setup_model_monitoring(ModelId, Options),
                
                {ok, #{
                    model_id => ModelId,
                    model_process => ModelProcess,
                    load_time => erlang:system_time(microsecond),
                    status => loaded,
                    capabilities => extract_model_capabilities(Model)
                }};
            {error, ValidationError} ->
                {error, {validation_failed, ValidationError}}
        end
    catch
        E:R:S ->
            {error, {model_load_failed, E, R, S}}
    end.

execute_hot_model_swap(OldModelId, NewModelId, SwapStrategy, _State) ->
    % Zero-downtime model swapping using Erlang's hot code loading
    
    % Phase 1: Validate both models are available
    case {get_model_from_registry(OldModelId), get_model_from_registry(NewModelId)} of
        {{ok, OldModel}, {ok, NewModel}} ->
            % Phase 2: Create swap plan
            SwapPlan = create_model_swap_plan(OldModel, NewModel, SwapStrategy),
            
            % Phase 3: Execute swap based on strategy
            case SwapStrategy of
                immediate ->
                    execute_immediate_swap(OldModel, NewModel, SwapPlan);
                gradual ->
                    execute_gradual_swap(OldModel, NewModel, SwapPlan);
                canary ->
                    execute_canary_swap(OldModel, NewModel, SwapPlan);
                blue_green ->
                    execute_blue_green_swap(OldModel, NewModel, SwapPlan);
                shadow ->
                    execute_shadow_swap(OldModel, NewModel, SwapPlan)
            end;
        {ErrorOld, ErrorNew} ->
            {error, {models_not_available, [{old, ErrorOld}, {new, ErrorNew}]}}
    end.

execute_immediate_swap(OldModel, NewModel, SwapPlan) ->
    % Immediate hot swap using Erlang's code replacement
    
    % Phase 1: Prepare new model for immediate activation
    PreparedModel = prepare_model_for_immediate_swap(NewModel),
    
    % Phase 2: Atomic routing switch
    RoutingSwitchResult = atomic_routing_switch(OldModel, PreparedModel),
    
    case RoutingSwitchResult of
        {ok, switched} ->
            % Phase 3: Hot code replacement
            HotSwapResult = perform_hot_code_replacement(OldModel, PreparedModel),
            
            % Phase 4: Update traffic routing
            update_traffic_routing_immediate(OldModel#ai_model.id, PreparedModel#ai_model.id),
            
            % Phase 5: Monitor swap success
            MonitoringResult = monitor_immediate_swap_success(PreparedModel, SwapPlan),
            
            {ok, #{
                swap_type => immediate,
                old_model => OldModel#ai_model.id,
                new_model => PreparedModel#ai_model.id,
                swap_time => erlang:system_time(microsecond),
                hot_swap_result => HotSwapResult,
                monitoring_result => MonitoringResult
            }};
        {error, SwitchError} ->
            {error, {routing_switch_failed, SwitchError}}
    end.

execute_gradual_swap(OldModel, NewModel, SwapPlan) ->
    % Gradual traffic shifting over time
    
    % Phase 1: Initialize gradual rollout
    RolloutState = initialize_gradual_rollout(OldModel, NewModel, SwapPlan),
    
    % Phase 2: Start traffic percentage routing
    TrafficPercentages = calculate_rollout_percentages(SwapPlan),
    
    % Phase 3: Execute rollout steps
    RolloutResults = execute_rollout_steps(RolloutState, TrafficPercentages),
    
    % Phase 4: Monitor each step
    MonitoringResults = monitor_gradual_rollout(RolloutResults),
    
    {ok, #{
        swap_type => gradual,
        rollout_plan => SwapPlan,
        rollout_results => RolloutResults,
        monitoring_results => MonitoringResults,
        estimated_completion => calculate_completion_time(SwapPlan)
    }}.

execute_canary_swap(OldModel, NewModel, SwapPlan) ->
    % Canary deployment with small traffic percentage
    {ok, #{
        swap_type => canary,
        canary_percentage => 5,
        swap_plan => SwapPlan,
        monitoring_active => true
    }}.

execute_blue_green_swap(OldModel, NewModel, SwapPlan) ->
    % Blue-green deployment with instant switch
    {ok, #{
        swap_type => blue_green,
        old_environment => blue,
        new_environment => green,
        swap_plan => SwapPlan,
        switch_completed => true
    }}.

execute_shadow_swap(OldModel, NewModel, SwapPlan) ->
    % Shadow mode - new model processes requests but doesn't serve results
    {ok, #{
        swap_type => shadow,
        shadow_mode_active => true,
        swap_plan => SwapPlan,
        comparison_metrics => #{}
    }}.

route_prediction_request(ModelId, InputData, Options, State) ->
    % Intelligent prediction routing with fallbacks
    
    % Phase 1: Create prediction request
    Request = #prediction_request{
        request_id = generate_request_id(),
        input_data = InputData,
        model_preferences = extract_model_preferences(ModelId, Options),
        quality_requirements = maps:get(quality_requirements, Options, #{}),
        timeout = maps:get(timeout, Options, 5000),
        ensemble_strategy = maps:get(ensemble_strategy, Options, undefined),
        client_context = maps:get(context, Options, #{})
    },
    
    % Phase 2: Route to appropriate model(s)
    RoutingResult = route_to_optimal_model(Request, State),
    
    case RoutingResult of
        {ok, {ModelProcess, RoutingInfo}} ->
            % Phase 3: Execute prediction
            PredictionResult = execute_model_prediction(ModelProcess, Request),
            
            % Phase 4: Post-process and validate
            FinalResult = post_process_prediction(PredictionResult, Request, RoutingInfo),
            
            % Phase 5: Update routing metrics
            update_routing_metrics(Request, FinalResult, RoutingInfo),
            
            {ok, FinalResult};
        {error, RoutingError} ->
            % Fallback to backup model or ensemble
            FallbackResult = execute_fallback_prediction(Request, RoutingError, State),
            {ok, FallbackResult}
    end.

execute_ensemble_prediction(ModelIds, InputData, EnsembleStrategy, _State) ->
    % Coordinate predictions from multiple models
    
    % Phase 1: Validate all models are available
    ModelValidation = validate_ensemble_models(ModelIds),
    
    case ModelValidation of
        {ok, ValidModels} ->
            % Phase 2: Execute parallel predictions
            PredictionTasks = create_parallel_prediction_tasks(ValidModels, InputData),
            
            % Phase 3: Collect predictions with timeout
            PredictionResults = collect_ensemble_predictions(PredictionTasks, 10000),
            
            % Phase 4: Apply ensemble strategy
            EnsembleResult = apply_ensemble_strategy(EnsembleStrategy, PredictionResults),
            
            % Phase 5: Calculate ensemble confidence
            EnsembleConfidence = calculate_ensemble_confidence(PredictionResults, EnsembleResult),
            
            {ok, #prediction_response{
                request_id = generate_request_id(),
                prediction = EnsembleResult,
                confidence_score = EnsembleConfidence,
                model_used = ensemble,
                inference_time = calculate_ensemble_time(PredictionResults),
                ensemble_details = #{
                    strategy => EnsembleStrategy,
                    participating_models => ModelIds,
                    individual_predictions => PredictionResults,
                    ensemble_weights => calculate_ensemble_weights(PredictionResults)
                }
            }};
        {error, ValidationError} ->
            {error, {ensemble_validation_failed, ValidationError}}
    end.

execute_real_time_training(ModelId, TrainingData, Options, _State) ->
    % Real-time model training with hot deployment
    
    % Phase 1: Get current model
    {ok, CurrentModel} = get_model_from_registry(ModelId),
    
    % Phase 2: Prepare incremental training
    TrainingPlan = prepare_incremental_training(CurrentModel, TrainingData, Options),
    
    % Phase 3: Execute training in isolated process
    TrainingResult = execute_isolated_training(TrainingPlan),
    
    case TrainingResult of
        {ok, UpdatedModelData} ->
            % Phase 4: Create new model version
            NewModelVersion = create_incremental_model_version(CurrentModel, UpdatedModelData),
            
            % Phase 5: Hot swap to updated model
            SwapResult = execute_hot_model_swap(
                CurrentModel#ai_model.id, 
                NewModelVersion#ai_model.id, 
                gradual
            ),
            
            % Phase 6: Validate training improvements
            ValidationResult = validate_training_improvements(CurrentModel, NewModelVersion),
            
            {ok, #{
                model_id => ModelId,
                new_version => NewModelVersion#ai_model.version,
                training_result => TrainingResult,
                swap_result => SwapResult,
                validation_result => ValidationResult,
                performance_improvement => calculate_performance_improvement(ValidationResult)
            }};
        {error, TrainingError} ->
            {error, {real_time_training_failed, TrainingError}}
    end.

%% Supporting Process Loops

traffic_router_loop(State) ->
    receive
        {route_request, Request, From} ->
            RoutingResult = route_prediction_intelligently(Request, State),
            From ! {routing_result, RoutingResult},
            traffic_router_loop(State);
        {update_routing_config, Config} ->
            NewState = apply_routing_config_update(Config, State),
            traffic_router_loop(NewState);
        stop ->
            ok
    end.

performance_monitor_loop(State) ->
    receive
        {monitor_model, ModelId, Metrics} ->
            MonitoringResult = analyze_model_performance(ModelId, Metrics),
            case detect_performance_issues(MonitoringResult) of
                {issues_detected, Issues} ->
                    hot_swappable_ai_models ! {performance_alert, ModelId, Issues};
                no_issues ->
                    ok
            end,
            performance_monitor_loop(State);
        {update_monitoring_config, Config} ->
            NewState = update_performance_monitoring_config(Config, State),
            performance_monitor_loop(NewState);
        stop ->
            ok
    after 1000 ->
        perform_periodic_performance_check(),
        performance_monitor_loop(State)
    end.

rollout_manager_loop(State) ->
    receive
        {start_rollout, RolloutPlan} ->
            execute_managed_rollout(RolloutPlan),
            rollout_manager_loop(State);
        {rollout_step_complete, RolloutId, StepResult} ->
            NewState = update_rollout_state(RolloutId, StepResult, State),
            maybe_continue_rollout(RolloutId, NewState),
            rollout_manager_loop(NewState);
        stop ->
            ok
    end.

%% Utility Functions

validate_model_spec(ModelSpec) ->
    RequiredFields = [algorithm_type, model_data, inference_function],
    case lists:all(fun(Field) -> maps:is_key(Field, ModelSpec) end, RequiredFields) of
        true -> {ok, ModelSpec};
        false -> {error, missing_required_fields}
    end.

generate_request_id() ->
    list_to_binary([
        atom_to_list(node()),
        "_",
        integer_to_list(erlang:system_time(microsecond)),
        "_",
        integer_to_list(rand:uniform(1000000))
    ]).

execute_hot_model_swap(OldId, NewId, Strategy) ->
    % 3-argument version for compatibility
    execute_hot_model_swap(OldId, NewId, Strategy, #{}).

%% Placeholder implementations for complex AI operations
compile_model_for_hot_loading(_Spec, _Options) -> compiled_model.
create_model_record(_Id, _Compiled, _Options) -> #ai_model{}.
initialize_model_process(_Model) -> {ok, model_process}.
register_model_in_registry(_Model, _Process) -> ok.
setup_model_monitoring(_Id, _Options) -> ok.
extract_model_capabilities(_Model) -> [prediction, classification].
get_model_from_registry(_Id) -> {ok, #ai_model{}}.
create_model_swap_plan(_Old, _New, _Strategy) -> #model_swap_plan{}.
prepare_model_for_immediate_swap(Model) -> Model.
atomic_routing_switch(_Old, _New) -> {ok, switched}.
perform_hot_code_replacement(_Old, _New) -> {ok, replaced}.
update_traffic_routing_immediate(_OldId, _NewId) -> ok.
monitor_immediate_swap_success(_Model, _Plan) -> {ok, success}.
initialize_gradual_rollout(_Old, _New, _Plan) -> rollout_state.
calculate_rollout_percentages(_Plan) -> [10, 25, 50, 75, 100].
execute_rollout_steps(_State, _Percentages) -> rollout_results.
monitor_gradual_rollout(_Results) -> monitoring_results.
calculate_completion_time(_Plan) -> erlang:system_time(microsecond) + 3600000.
extract_model_preferences(_Id, _Options) -> [].
route_to_optimal_model(_Request, _State) -> {ok, {model_process, routing_info}}.
execute_model_prediction(_Process, _Request) -> prediction_result.
post_process_prediction(_Result, _Request, _Info) -> #prediction_response{}.
update_routing_metrics(_Request, _Result, _Info) -> ok.
execute_fallback_prediction(_Request, _Error, _State) -> #prediction_response{}.
validate_ensemble_models(_Ids) -> {ok, []}.
create_parallel_prediction_tasks(_Models, _Data) -> [].
collect_ensemble_predictions(_Tasks, _Timeout) -> [].
apply_ensemble_strategy(_Strategy, _Results) -> ensemble_result.
calculate_ensemble_confidence(_Results, _Ensemble) -> 0.9.
calculate_ensemble_time(_Results) -> 100.
calculate_ensemble_weights(_Results) -> #{}.
prepare_incremental_training(_Model, _Data, _Options) -> training_plan.
execute_isolated_training(_Plan) -> {ok, updated_model}.
create_incremental_model_version(_Current, _Updated) -> #ai_model{}.
validate_training_improvements(_Old, _New) -> validation_result.
calculate_performance_improvement(_Validation) -> 0.15.
route_prediction_intelligently(_Request, _State) -> routing_result.
apply_routing_config_update(_Config, State) -> State.
analyze_model_performance(_Id, _Metrics) -> performance_analysis.
detect_performance_issues(_Analysis) -> no_issues.
update_performance_monitoring_config(_Config, State) -> State.
perform_periodic_performance_check() -> ok.
execute_managed_rollout(_Plan) -> ok.
update_rollout_state(_Id, _Result, State) -> State.
maybe_continue_rollout(_Id, _State) -> ok.
handle_hot_code_change(_Module, _Version, State) -> State.
perform_model_health_check(_Id) -> ok.
schedule_next_health_check(_Id) -> erlang:send_after(60000, self(), {model_health_check, _Id}).
process_neural_search_result(_Id, _Result) -> ok.
update_traffic_routing_config(_Config) -> ok.
migrate_model_state(_OldVsn, State, _Extra) -> State.
update_active_models(_Id, _Result, State) -> State.
update_model_routing(_Result, State) -> State.
initiate_gradual_rollout(_Id, _Version, _Schedule, _Criteria, _State) -> {ok, rollout_initiated}.
setup_a_b_test(_A, _B, _Split, _Duration, _State) -> {ok, test_setup}.
execute_version_control_operation(_Id, _Op, _State) -> {ok, operation_complete}.
perform_adaptive_model_selection(_Data, _Context, _Criteria, _State) -> {ok, selected_model}.
setup_performance_monitoring(_Id, _Config, _State) -> {ok, monitoring_setup}.
execute_model_rollback(_Id, _Version, _State) -> {ok, rollback_complete}.
process_federated_model_update(_Id, _Updates, _Strategy, _State) -> {ok, federated_update}.
initiate_neural_architecture_search(_Space, _Objective, _Budget, _State) -> {ok, search_initiated}.
execute_swap_step(_Id, _Step) -> ok.
handle_performance_alert(_Id, _Alert, _State) -> ok.
update_rollout_progress(_Id, _Progress) -> ok.
process_federated_update(_Id, _Data) -> ok.
version_controller_loop(State) -> State.
ensemble_coordinator_loop(State) -> State.
neural_search_loop(State) -> State.
federated_learning_loop(State) -> State.
hot_swap_scheduler_loop(State) -> State.