%% 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.