diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..dfe5ddb
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,6 @@
+FROM python:3.10-slim
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
\ No newline at end of file
diff --git a/api.py b/api.py
new file mode 100644
index 0000000..feb3fca
--- /dev/null
+++ b/api.py
@@ -0,0 +1,144 @@
+# api.py
+
+import os
+import pickle
+import json
+import time
+from functools import lru_cache
+import requests  # Importante: para hacer peticiones HTTP
+import pandas as pd
+from fastapi import FastAPI, HTTPException, Response
+from pydantic import BaseModel
+from typing import Dict, Any, List
+
+from prometheus_client import Histogram, generate_latest
+
+# --- Modelos Pydantic (se mantienen igual) ---
+class PredictionRequest(BaseModel):
+    features: Dict[str, float]
+
+class PredictionResponse(BaseModel):
+    prediction: Any
+    model_details: Dict[str, str]
+
+# --- Métricas de Prometheus (se mantienen igual) ---
+INFERENCE_LATENCY = Histogram(
+    'inference_latency_seconds',
+    'Latencia de las peticiones de inferencia (en segundos)',
+    ['dataset', 'model_type']
+)
+
+# --- Aplicación FastAPI (SIN LIFESPAN) ---
+app = FastAPI(
+    title="API de Inferencia (v3 - Desacoplada)",
+    version="3.0.0",
+    description="Provee acceso a modelos de ML consumiendo la API de Registro."
+)
+
+# URL del servicio de gestión, leído del entorno
+MANAGEMENT_API_URL = os.environ.get("MANAGEMENT_API_URL", "http://localhost:9000")
+
+# --- NUEVAS Funciones de Ayuda ---
+@lru_cache(maxsize=32)
+def get_artifact_from_management_api(dataset: str, model_type: str, artifact_name: str):
+    """
+    Llama a la management-api para obtener un artefacto, lo deserializa y lo cachea.
+    """
+    print(f"CACHE MISS: Pidiendo a management-api: {dataset}/{model_type}/{artifact_name}")
+    try:
+        url = f"{MANAGEMENT_API_URL}/registry/artifacts/{dataset}/{model_type}/{artifact_name}"
+        response = requests.get(url, timeout=10)
+        
+        response.raise_for_status()  # Lanza una excepción para códigos de error (4xx o 5xx)
+        
+        serialized_obj_bytes = response.content
+        
+        # Deserializar según el tipo
+        if artifact_name == "metrics":
+            return json.loads(serialized_obj_bytes.decode('utf-8'))
+        else:
+            return pickle.loads(serialized_obj_bytes)
+
+    except requests.exceptions.HTTPError as e:
+        status_code = e.response.status_code if e.response else 503
+        try:
+            # Intentar parsear el detalle del error que viene de management-api
+            detail = e.response.json().get("detail", e.response.text)
+        except json.JSONDecodeError:
+            detail = e.response.text
+        raise HTTPException(status_code=status_code, detail=f"Error desde management-api: {detail}")
+    except requests.exceptions.RequestException as e:
+        raise HTTPException(status_code=503, detail=f"No se pudo contactar a management-api: {e}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error inesperado al procesar artefacto: {e}")
+
+# --- Endpoints de la API (REESCRITOS PARA USAR LA NUEVA LÓGICA) ---
+
+@app.get("/models", summary="Listar todos los modelos disponibles")
+def list_models():
+    """Consulta a la management-api para obtener la lista de modelos."""
+    try:
+        url = f"{MANAGEMENT_API_URL}/registry/models"
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        raise HTTPException(status_code=503, detail=f"No se pudo conectar a management-api para listar modelos: {e}")
+
+@app.get("/models/{dataset}/{model_type}/features", summary="Obtener características de un modelo")
+def get_model_features(dataset: str, model_type: str):
+    """Obtiene la lista de características desde la management-api."""
+    try:
+        features = get_artifact_from_management_api(dataset, model_type, "feature_names")
+        return {"features": features}
+    except HTTPException as e:
+        raise e  # Re-lanzar la excepción que ya viene formateada
+
+@app.get("/models/{dataset}/{model_type}/metrics", summary="Obtener métricas de entrenamiento")
+def get_model_metrics(dataset: str, model_type: str):
+    """Obtiene las métricas del modelo desde la management-api."""
+    try:
+        metrics = get_artifact_from_management_api(dataset, model_type, "metrics")
+        return metrics
+    except HTTPException as e:
+        raise e
+
+@app.post("/predict/{dataset}/{model_type}", response_model=PredictionResponse, summary="Realizar una predicción")
+def predict(dataset: str, model_type: str, request: PredictionRequest):
+    """Realiza una predicción usando un modelo y registra la latencia."""
+    start_time = time.time()
+    required_features = [] # Para el mensaje de error
+    try:
+        pipeline = get_artifact_from_management_api(dataset, model_type, "pipeline")
+        required_features = get_artifact_from_management_api(dataset, model_type, "feature_names")
+
+        input_df = pd.DataFrame([request.features])[required_features]
+        prediction = pipeline.predict(input_df)
+        
+        prediction_value = prediction[0].item() if hasattr(prediction[0], 'item') else prediction[0]
+        
+        return PredictionResponse(prediction=prediction_value, model_details={"dataset": dataset, "model_type": model_type})
+        
+    except KeyError as e:
+        raise HTTPException(status_code=400, detail=f"Falta la característica requerida: {e}. Se requieren: {required_features}")
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error durante la predicción: {e}")
+    finally:
+        latency = time.time() - start_time
+        INFERENCE_LATENCY.labels(dataset=dataset, model_type=model_type).observe(latency)
+        print(f"PREDICT LATENCY: {dataset}/{model_type} -> {latency:.4f}s")
+
+
+
+@app.get("/health", summary="Comprobación de Salud", status_code=200)
+def health_check():
+    """
+    Endpoint simple para que Docker Healthcheck pueda verificar que el servicio está vivo.
+    """
+    return {"status": "ok"}
+
+@app.get("/metrics", summary="Exponer métricas para Prometheus")
+def get_prometheus_metrics():
+    return Response(content=generate_latest(), media_type="text/plain")
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..6172849
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,78 @@
+# docker-compose.yml (VERSIÓN FINAL - RESILIENTE)
+
+services:
+  # Los servicios de Ray no cambian. ray-head sigue siendo el SPOF principal por diseño.
+  ray-head:
+    build: .
+    # Ya no usamos container_name para facilitar el escalado, aunque en head no es estrictamente necesario.
+    ports:
+      - "8265:8265"
+      - "10001:10001"
+    command: |
+      bash -c "
+        ray start --head --num-cpus=1 --port=6379 --dashboard-host=0.0.0.0 --include-dashboard=true --ray-client-server-port=10001 --resources='{\"is_head_node\": 1}' && \
+        echo 'Nodo HEAD en marcha...' && \
+        tail -f /dev/null
+      "
+    shm_size: 2.5gb
+
+  ray-worker: # Unificamos los workers para facilitar el escalado
+    build: .
+    depends_on:
+      - ray-head
+    deploy:
+      replicas: 2 # Podemos escalar los workers de Ray fácilmente
+    command: |
+      bash -c "
+        ray start --address=ray-head:6379 --num-cpus=2 && \
+        echo 'Worker conectado al HEAD' && \
+        tail -f /dev/null
+      "
+    shm_size: 2.5gb
+  
+  # SOLUCIÓN #1: Replicación del servicio crítico
+  management-api:
+    build: .
+    # Ya no se usa container_name para permitir la replicación.
+    # Ya no se exponen puertos, la comunicación es interna a través de la GUI.
+    deploy:
+      replicas: 3 # Creamos un grupo de 3 réplicas para alta disponibilidad.
+    environment:
+      - RAY_ADDRESS=ray://ray-head:10001
+      - SERVICE_NAME=management-api # Para que el cliente resiliente sepa a quién buscar
+    command: uvicorn management_api:app --host 0.0.0.0 --port 9000
+    # Quitamos el healthcheck porque la GUI ahora es responsable de manejar los fallos.
+  
+  # Hacemos lo mismo para el servicio de inferencia para consistencia
+  api-service:
+    build: .
+    deploy:
+      replicas: 3 # También replicamos el servicio de inferencia.
+    # ports:
+      # Exponemos este puerto para que sea más fácil de probar externamente (ej. con curl)
+      # Pero idealmente, la GUI también usaría un cliente resiliente para este.
+      # - "8000:8000"
+    environment:
+      # El api-service también se beneficia del balanceo de carga interno de Docker
+      # al hablar con las réplicas de management-api.
+      - MANAGEMENT_API_URL=http://management-api:9000 
+    command: uvicorn api:app --host 0.0.0.0 --port 8000
+
+  # SOLUCIÓN #2: La GUI ahora es el cliente inteligente
+  gui-service:
+    build: .
+    container_name: gui-service # La GUI es única, así que puede tener nombre.
+    depends_on:
+      - management-api
+      - api-service
+    ports:
+      - "8501:8501"
+    environment:
+      # Pasamos la información necesaria para que el ResilientClient haga su trabajo.
+      - MANAGEMENT_API_SERVICE_NAME=management-api
+      - MANAGEMENT_API_PORT=9000
+      # La URL de inferencia puede apuntar al nombre del servicio; Docker hará un balanceo de carga simple (round-robin).
+      - INFERENCE_API_URL=http://api-service:8000 
+      - RAY_DASHBOARD_URL=http://ray-head:8265
+      - RAY_ADDRESS=ray://ray-head:10001
+    command: streamlit run gui.py --server.port=8501 --server.address=0.0.0.0
\ No newline at end of file
diff --git a/gui.py b/gui.py
new file mode 100644
index 0000000..c0d3cdc
--- /dev/null
+++ b/gui.py
@@ -0,0 +1,406 @@
+# gui.py (Versión 6.1 - Funcionalidad Completa Restaurada)
+
+import streamlit as st
+import requests
+import os
+import pandas as pd
+import json
+import time
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import ray
+import socket
+from itertools import cycle
+from monitoring_utils import get_cluster_nodes_status, get_actor_status, get_aggregated_inference_stats
+# ==============================================================================
+# --- CLASE DE CLIENTE RESILIENTE (SIN CAMBIOS) ---
+# ==============================================================================
+
+class ResilientClient:
+    """
+    Un cliente HTTP que realiza descubrimiento de servicios, balanceo de carga
+    round-robin y reintentos del lado del cliente.
+    """
+    def __init__(self, service_name, port):
+        self.service_name = service_name
+        self.port = port
+        self.servers = []
+        self.server_cycler = cycle([])
+        self.discover()
+
+    def discover(self):
+        """Descubre o redescubre los servidores. Puede ser llamado para refrescar."""
+        try:
+            ips = [info[4][0] for info in socket.getaddrinfo(self.service_name, self.port, socket.AF_INET)]
+            unique_ips = sorted(list(set(ips)))
+            self.servers = [f"http://{ip}:{self.port}" for ip in unique_ips]
+            if self.servers:
+                print(f"Servidores descubiertos para '{self.service_name}': {self.servers}")
+                self.server_cycler = cycle(self.servers)
+            else:
+                st.error(f"CRÍTICO: No se pudo descubrir ninguna réplica para el servicio '{self.service_name}'.")
+                self.server_cycler = cycle([])
+        except socket.gaierror:
+            st.error(f"CRÍTICO: El nombre de servicio '{self.service_name}' no se pudo resolver. ¿Está corriendo?")
+            self.servers = []
+            self.server_cycler = cycle([])
+
+    def make_request(self, method, path, **kwargs):
+        """
+        Realiza una petición HTTP, probando cada servidor en orden hasta que
+        uno responda o todos fallen.
+        """
+        if not self.servers:
+            raise Exception(f"No hay servidores disponibles para el servicio '{self.service_name}'.")
+        for _ in range(len(self.servers)):
+            server_url = next(self.server_cycler)
+            full_url = f"{server_url}{path}"
+            try:
+                print(f"Cliente Resiliente: Intentando petición a: {full_url}")
+                response = requests.request(method, full_url, **kwargs)
+                response.raise_for_status() 
+                print(f"Cliente Resiliente: Petición exitosa a: {full_url}")
+                return response
+            except requests.exceptions.RequestException as e:
+                print(f"Cliente Resiliente: Fallo al contactar a {server_url}: {e}. Probando siguiente réplica...")
+                st.toast(f"Réplica {server_url} no responde, reintentando...", icon="⚠️")
+        st.error(f"El servicio '{self.service_name}' no está disponible. Todas las réplicas han fallado.")
+        self.discover()
+        raise Exception(f"El servicio '{self.service_name}' no está disponible. Todas las réplicas fallaron.")
+
+# ==============================================================================
+# --- INICIALIZACIÓN Y CONFIGURACIÓN ---
+# ==============================================================================
+
+# --- Intenta importar las utilidades de monitoreo ---
+try:
+    ### CORREGIDO ### - Asegúrate de que esta función también sea resiliente si depende de una API
+    from monitoring_utils import get_cluster_nodes_status, get_actor_status, get_inference_stats
+    MONITORING_ENABLED = True
+except ImportError:
+    MONITORING_ENABLED = False
+    print("ADVERTENCIA: monitoring_utils.py no encontrado. La pestaña de monitoreo estará desactivada.")
+
+# --- Configuración de la Página y Clientes Resilientes ---
+st.set_page_config(layout="wide", page_title="Plataforma Distribuida de ML")
+
+# Cliente para el servicio de Gestión
+MANAGEMENT_API_SERVICE_NAME = os.environ.get("MANAGEMENT_API_SERVICE_NAME", "management-api")
+MANAGEMENT_API_PORT = int(os.environ.get("MANAGEMENT_API_PORT", 9000))
+management_client = ResilientClient(MANAGEMENT_API_SERVICE_NAME, MANAGEMENT_API_PORT)
+
+# Cliente para el servicio de Inferencia
+INFERENCE_API_SERVICE_NAME = os.environ.get("INFERENCE_API_SERVICE_NAME", "api-service")
+INFERENCE_API_PORT = int(os.environ.get("INFERENCE_API_PORT", 8000))
+inference_client = ResilientClient(INFERENCE_API_SERVICE_NAME, INFERENCE_API_PORT)
+
+RAY_DASHBOARD_URL = os.environ.get("RAY_DASHBOARD_URL", f"http://localhost:8265")
+
+# --- Conexión a Ray (solo para la pestaña de monitoreo) ---
+if MONITORING_ENABLED and 'ray_initialized' not in st.session_state:
+    try:
+        ray.init(address="auto", namespace="mi_plataforma", ignore_reinit_error=True)
+        st.session_state['ray_initialized'] = True
+    except Exception as e:
+        st.session_state['ray_initialized'] = False
+        st.session_state['ray_init_error'] = e
+
+# --- Inicialización del Estado de la Sesión ---
+if 'analyzed_files' not in st.session_state:
+    st.session_state['analyzed_files'] = {}
+if 'configs' not in st.session_state:
+    st.session_state['configs'] = {}
+
+# ==============================================================================
+# --- FUNCIONES DE AYUDA (USANDO CLIENTES RESILIENTES) ---
+# ==============================================================================
+
+@st.cache_data(ttl=10)
+def get_available_models():
+    try:
+        response = inference_client.make_request("GET", "/models", timeout=5)
+        return response.json()
+    except Exception: return {}
+
+@st.cache_data(ttl=30)
+def get_model_metrics(dataset, model_type):
+    try:
+        response = inference_client.make_request("GET", f"/models/{dataset}/{model_type}/metrics", timeout=10)
+        return response.json()
+    except Exception: return {}
+
+@st.cache_data(ttl=60)
+def fetch_features_for_model(dataset, model):
+    try:
+        response = inference_client.make_request("GET", f"/models/{dataset}/{model}/features", timeout=10)
+        return response.json().get("features", [])
+    except Exception: return None
+
+# ==============================================================================
+# --- DISEÑO DE LA INTERFAZ ---
+# ==============================================================================
+
+st.title("📊 Plataforma de Aprendizaje Supervisado Distribuido")
+st.markdown("---")
+
+tab_definitions = ["🗂️ Gestión y Entrenamiento", "🤖 Realizar Predicción", "📉 Métricas y Comparativa"]
+if MONITORING_ENABLED:
+    tab_definitions.append("📡 Dashboard de Monitoreo")
+
+created_tabs = st.tabs(tab_definitions)
+tab_gestion = created_tabs[0]
+tab_prediccion = created_tabs[1]
+tab_metricas = created_tabs[2]
+tab_monitoreo = created_tabs[3] if len(created_tabs) > 3 else None
+
+# ==============================================================================
+# --- PESTAÑA 1: GESTIÓN Y ENTRENAMIENTO ---
+# ==============================================================================
+with tab_gestion:
+    st.header("Flujo de Trabajo de Entrenamiento")
+    st.subheader("1. Sube tus Datasets")
+    uploaded_files = st.file_uploader(
+        "Selecciona uno o más archivos CSV", type=["csv"], accept_multiple_files=True, label_visibility="collapsed"
+    )
+
+    if uploaded_files:
+        for file in uploaded_files:
+            if file.name not in st.session_state.analyzed_files:
+                with st.spinner(f"Analizando `{file.name}`..."):
+                    files_payload = {'file': (file.name, file, 'text/csv')}
+                    try:
+                        response = management_client.make_request("POST", "/datasets/analyze", files=files_payload, timeout=30)
+                        st.session_state.analyzed_files[file.name] = response.json()
+                        st.session_state.configs[file.name] = {
+                            'dataset_name': os.path.splitext(file.name)[0],
+                            'target_column': response.json().get('columns', [''])[0],
+                            'selected_models': ["logistic_regression", "decision_tree"]
+                        }
+                    except Exception as e:
+                        st.error(f"Error de conexión al analizar `{file.name}`: {e}")
+                        st.session_state.analyzed_files[file.name] = {"error": str(e)}
+
+    st.markdown("---")
+    st.subheader("2. Configura los Parámetros de Entrenamiento")
+
+    if not st.session_state.analyzed_files:
+        st.info("Sube un archivo CSV para empezar a configurar.")
+    else:
+        files_to_configure = list(st.session_state.analyzed_files.keys())
+        for filename in files_to_configure:
+            if filename not in st.session_state.analyzed_files: continue
+            file_info = st.session_state.analyzed_files[filename]
+            config = st.session_state.configs.get(filename)
+            if not config: continue
+
+            with st.container(border=True):
+                st.markdown(f"**Configuración para `{filename}`**")
+                if "error" in file_info:
+                    st.error(f"No se pudo procesar: {file_info['error']}")
+                    continue
+
+                col_ds, col_target = st.columns(2)
+                config['dataset_name'] = col_ds.text_input("Nombre del Dataset", value=config.get('dataset_name', ''), key=f"ds_{filename}")
+                columns_list = file_info.get("columns", [])
+                try:
+                    current_index = columns_list.index(config.get('target_column'))
+                except (ValueError, TypeError): current_index = 0
+                config['target_column'] = col_target.selectbox("Columna Objetivo", options=columns_list, index=current_index, key=f"target_{filename}")
+                config['selected_models'] = st.multiselect("Modelos a Entrenar", options=["logistic_regression", "decision_tree", "random_forest"], default=config.get('selected_models', []), key=f"models_{filename}")
+                
+                if st.button(f"🗑️ Eliminar Dataset y Modelos", key=f"delete_{filename}", type="secondary"):
+                    with st.spinner(f"Eliminando '{config['dataset_name']}'..."):
+                        try:
+                            management_client.make_request("DELETE", f"/models/{config['dataset_name']}", timeout=30)
+                            st.session_state.analyzed_files.pop(filename, None)
+                            st.session_state.configs.pop(filename, None)
+                            st.success(f"Dataset '{config['dataset_name']}' eliminado.")
+                            st.cache_data.clear(); st.rerun()
+                        except Exception as e: st.error(f"Error al eliminar: {e}")
+
+    st.markdown("---")
+    st.subheader("3. Elige tu Modo de Entrenamiento")
+
+    ### RESTAURADO ### - Lógica de entrenamiento individual y por lotes
+    if not st.session_state.configs:
+        st.warning("Primero configura al menos un dataset en el paso 2.")
+    else:
+        # MODO GRANULAR
+        with st.container(border=True):
+            st.markdown("##### Entrenamiento Individual")
+            dataset_names = [cfg['dataset_name'] for cfg in st.session_state.configs.values() if 'dataset_name' in cfg]
+            dataset_to_train_individually = st.selectbox("Selecciona un dataset para entrenar:", options=dataset_names)
+            
+            if st.button("🚀 Entrenar este Dataset Individualmente"):
+                filename_to_train, config_to_train = None, None
+                for fname, cfg in st.session_state.configs.items():
+                    if cfg.get('dataset_name') == dataset_to_train_individually:
+                        filename_to_train = fname
+                        config_to_train = cfg
+                        break
+                
+                if filename_to_train and config_to_train:
+                    if not config_to_train.get('selected_models'):
+                        st.error("Este dataset no tiene modelos seleccionados.")
+                    else:
+                        original_file = next((f for f in uploaded_files if f.name == filename_to_train), None)
+                        if original_file:
+                             with st.spinner(f"Lanzando entrenamiento para `{config_to_train['dataset_name']}`..."):
+                                files_payload = [('files', (filename_to_train, original_file.getvalue(), 'text/csv'))]
+                                config_payload = {'configs': json.dumps([{"dataset_name": config_to_train['dataset_name'], "target_column": config_to_train['target_column'], "filename": filename_to_train}]), 'models_to_train': config_to_train['selected_models']}
+                                try:
+                                    management_client.make_request("POST", "/datasets/train_batch", files=files_payload, data=config_payload, timeout=120)
+                                    st.success(f"¡Trabajo para '{config_to_train['dataset_name']}' lanzado!"); st.balloons(); st.cache_data.clear()
+                                except Exception as e: st.error(f"Error al lanzar entrenamiento: {e}")
+                        else: st.error(f"El archivo '{filename_to_train}' ya no está disponible.")
+
+        # MODO POR LOTES
+        with st.container(border=True):
+            st.markdown("##### Entrenamiento por Lotes")
+            if st.button("🚀 Entrenar TODOS los Datasets Configurados", type="primary"):
+                with st.spinner(f"Lanzando lote de entrenamiento..."):
+                    success_count = 0
+                    for filename, config in st.session_state.configs.items():
+                        if not config.get('selected_models'): continue
+                        original_file = next((f for f in uploaded_files if f.name == filename), None)
+                        if original_file:
+                            files_payload = [('files', (filename, original_file.getvalue(), 'text/csv'))]
+                            config_payload = {'configs': json.dumps([{"dataset_name": config['dataset_name'], "target_column": config['target_column'], "filename": filename}]), 'models_to_train': config['selected_models']}
+                            try:
+                                management_client.make_request("POST", "/datasets/train_batch", files=files_payload, data=config_payload, timeout=120)
+                                success_count += 1
+                                st.toast(f"Trabajo para '{config['dataset_name']}' lanzado.", icon="🚀")
+                            except Exception as e: st.error(f"Fallo al lanzar '{config['dataset_name']}': {e}")
+                    st.success(f"¡Lote completado! {success_count} trabajos lanzados."); st.balloons(); st.cache_data.clear()
+
+# ==============================================================================
+# --- PESTAÑA 2: REALIZAR PREDICCIÓN ---
+# ==============================================================================
+with tab_prediccion:
+    st.header("🔮 Probar un Modelo Desplegado")
+    models_data = get_available_models()
+    if not models_data:
+        st.warning("No hay modelos disponibles. Entrena uno en la pestaña de 'Gestión'.")
+    else:
+        col1, col2 = st.columns(2)
+        with col1:
+            dataset_options = list(models_data.keys())
+            selected_dataset = st.selectbox("1. Selecciona un Dataset", dataset_options, key="p_ds")
+        if selected_dataset and models_data.get(selected_dataset):
+            with col2:
+                model_options = models_data[selected_dataset].get("available_models", [])
+                selected_model = st.selectbox("2. Selecciona un Modelo", model_options, key="p_model")
+            if selected_model:
+                st.subheader(f"3. Ingresa los valores de las características")
+                feature_names = fetch_features_for_model(selected_dataset, selected_model)
+                if feature_names is not None:
+                    with st.form("prediction_form"):
+                        # Se corrigió la lógica para usar un diccionario directamente
+                        feature_inputs = {feat: st.number_input(label=feat, key=f"feat_{feat}", value=0.0, format="%.4f") for feat in feature_names}
+                        if st.form_submit_button("Realizar Predicción", type="primary"):
+                            with st.spinner("Enviando predicción..."):
+                                try:
+                                    # El backend espera un diccionario de features, no una lista
+                                    response = inference_client.make_request("POST", f"/predict/{selected_dataset}/{selected_model}", json={"features": feature_inputs}, timeout=15)
+                                    st.success("Predicción recibida:")
+                                    st.json(response.json())
+                                except Exception as e: st.error(f"Error en la predicción: {e}")
+
+# ==============================================================================
+# --- PESTAÑA 3: MÉTRICAS Y COMPARATIVA ---
+# ==============================================================================
+with tab_metricas:
+    st.header("📉 Métricas y Comparativa de Modelos")
+    if st.button("Refrescar Datos de Métricas"): st.cache_data.clear(); st.rerun()
+
+    models_data_metrics = get_available_models()
+    if not models_data_metrics:
+        st.info("No hay modelos entrenados para mostrar métricas.")
+    else:
+        ### RESTAURADO ### - Lógica completa de métricas y visualización
+        plot_data = []; all_metrics_data = []
+        for dataset, models in models_data_metrics.items():
+            for model_type in models.get("available_models", []):
+                metrics = get_model_metrics(dataset, model_type)
+                if metrics and 'accuracy' in metrics:
+                    all_metrics_data.append({'dataset': dataset, 'model_type': model_type, **metrics})
+                    plot_data.append({'dataset': dataset, 'model_type': model_type, 'accuracy': metrics['accuracy'], 'f1_score_macro': metrics.get('classification_report', {}).get('macro avg', {}).get('f1-score', 0)})
+        
+        if plot_data:
+            df_plot = pd.DataFrame(plot_data)
+            st.subheader("Comparativa de Rendimiento General")
+            col1, col2 = st.columns(2)
+            with col1:
+                fig1, ax1 = plt.subplots()
+                sns.barplot(data=df_plot, x="accuracy", y="dataset", hue="model_type", ax=ax1, orient='h')
+                ax1.set_title("Precisión (Accuracy)"); ax1.set_xlabel("Accuracy"); ax1.set_ylabel("Dataset")
+                st.pyplot(fig1)
+
+            with col2:
+                fig2, ax2 = plt.subplots()
+                sns.barplot(data=df_plot, x="f1_score_macro", y="dataset", hue="model_type", ax=ax2, orient='h', palette="viridis")
+                ax2.set_title("F1-Score (Macro Avg)"); ax2.set_xlabel("F1-Score"); ax2.set_ylabel("")
+                st.pyplot(fig2)
+
+            st.markdown("---")
+            st.subheader("Análisis Detallado por Modelo")
+            df_all_metrics = pd.DataFrame(all_metrics_data)
+            if not df_all_metrics.empty:
+                selected_idx = st.selectbox("Selecciona un modelo para inspeccionar:", options=df_all_metrics.index, format_func=lambda i: f"{df_all_metrics.loc[i, 'dataset']} / {df_all_metrics.loc[i, 'model_type']}")
+                if selected_idx is not None:
+                    row = df_all_metrics.loc[selected_idx]
+                    c1, c2 = st.columns(2)
+                    with c1:
+                        st.text("Reporte de Clasificación:")
+                        st.json(row.get('classification_report', {}))
+                    with c2:
+                        cm = row.get('confusion_matrix')
+                        class_report = row.get('classification_report', {})
+                        class_names = [k for k in class_report.keys() if k not in ['accuracy', 'macro avg', 'weighted avg']]
+                        if cm and class_names:
+                            fig_cm, ax_cm = plt.subplots()
+                            sns.heatmap(np.array(cm), annot=True, fmt='d', cmap='Blues', ax=ax_cm, xticklabels=class_names, yticklabels=class_names)
+                            ax_cm.set_title("Matriz de Confusión"); ax_cm.set_ylabel("Verdadero"); ax_cm.set_xlabel("Predicho")
+                            st.pyplot(fig_cm)
+
+# ==============================================================================
+# --- PESTAÑA 4: DASHBOARD DE MONITOREO ---
+# ==============================================================================
+if tab_monitoreo is not None:
+    with tab_monitoreo:
+        st.header("Dashboard de Monitoreo del Sistema")
+        if st.button("Refrescar Datos del Dashboard", key="refresh_monitor"): st.rerun()
+
+        if not st.session_state.get('ray_initialized', False):
+            st.error("Fallo al conectar con el clúster de Ray.")
+            if 'ray_init_error' in st.session_state: st.exception(st.session_state.get('ray_init_error'))
+        else:
+            with st.expander("Estado del Clúster Ray", expanded=True):
+                st.subheader("Nodos del Clúster")
+                try:
+                    node_data = get_cluster_nodes_status()
+                    if node_data: st.table(pd.DataFrame(node_data))
+                    else: st.warning("No se pudieron obtener datos de los nodos.")
+                except Exception as e: st.error(f"Error obteniendo estado de nodos: {e}")
+
+            with st.expander("Estadísticas de Inferencia (API)", expanded=True):
+                st.subheader("Rendimiento del Servicio de Predicción")
+                try:
+                        # --- LLAMADA A LA NUEVA FUNCIÓN DE AGREGACIÓN ---
+                        # Le pasamos el inference_client para que sepa a quiénes preguntar
+                        inference_stats = get_aggregated_inference_stats(inference_client)
+
+                        if "error" in inference_stats:
+                            st.error(inference_stats["error"])
+                        else:
+                            col1, col2 = st.columns(2)
+                            col1.metric("Total de Peticiones", f"{inference_stats.get('total_requests', 0)}")
+                            col2.metric("Latencia Promedio", f"{inference_stats.get('average_latency_ms', 0):.2f} ms")
+                            details_df = pd.DataFrame(inference_stats.get("details_by_model", []))
+                            if not details_df.empty:
+                                st.markdown("**Desglose por Modelo:**")
+                                st.table(details_df)
+                except Exception as e:
+                    st.error(f"No se pudieron obtener las estadísticas de inferencia: {e}")
+
diff --git a/management_api.py b/management_api.py
new file mode 100644
index 0000000..3cf1ea4
--- /dev/null
+++ b/management_api.py
@@ -0,0 +1,223 @@
+# management_api.py
+
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Response
+import pandas as pd
+import ray
+import os
+import train
+import traceback
+from contextlib import asynccontextmanager
+from typing import List
+import json
+import time
+import pickle
+
+# --- Lifespan Manager (Con Carga de Modelos Persistentes) ---
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print(">>> LIFESPAN (Management API): Iniciando...")
+    try:
+        ray_address = os.environ.get("RAY_ADDRESS", "auto")
+        if not ray.is_initialized():
+            ray.init(address=ray_address, namespace="mi_plataforma", ignore_reinit_error=True)
+
+        train.ModelRegistryActor.options(
+            name="model_registry",
+            get_if_exists=True,
+            namespace="mi_plataforma",
+            lifetime="detached",
+            max_restarts=-1,
+        ).remote()
+        print(">>> LIFESPAN (Management API): Solicitud de creación de ModelRegistryActor enviada.")
+
+        actor_ready = False
+        for i in range(30):
+            try:
+                ray.get_actor("model_registry", namespace="mi_plataforma")
+                print(f">>> LIFESPAN (Management API): ¡ModelRegistryActor está listo! (Intento {i+1})")
+                actor_ready = True
+                break
+            except ValueError:
+                print(f">>> LIFESPAN (Management API): Esperando que ModelRegistryActor esté disponible... (Intento {i+1})")
+                time.sleep(1)
+
+        if not actor_ready:
+            raise RuntimeError("!!! ERROR CRÍTICO: El ModelRegistryActor no estuvo disponible.")
+
+        # --- NUEVO: Cargar modelos desde el disco al arrancar ---
+        print(">>> LIFESPAN: Cargando modelos persistentes al registro en memoria...")
+        actor = ray.get_actor("model_registry", namespace="mi_plataforma")
+        base_model_dir = "/app/persistent_models"
+        if os.path.isdir(base_model_dir):
+            for filename in os.listdir(base_model_dir):
+                if filename.endswith(".pkl"):
+                    try:
+                        parts = filename.replace(".pkl", "").rsplit("_", 1)
+                        if len(parts) == 2:
+                            dataset_name, model_type = parts
+                            file_path = os.path.join(base_model_dir, filename)
+                            with open(file_path, "rb") as f:
+                                result_dictionary = pickle.load(f)
+                            
+                            actor.register_model.remote(dataset_name, model_type, result_dictionary)
+                            print(f"--- Modelo persistente cargado: {dataset_name}/{model_type}")
+                    except Exception as e:
+                        print(f"--- Error cargando modelo persistente {filename}: {e}")
+        
+    except Exception as e:
+        print(f"!!! LIFESPAN (Management API) ERROR DURANTE EL ARRANQUE: {e} !!!")
+        traceback.print_exc()
+        raise e
+
+    yield
+
+    if ray.is_initialized():
+        ray.shutdown()
+
+app = FastAPI(title="API de Gestión y Registro de Modelos", version="2.1.0", lifespan=lifespan)
+
+# --- Endpoints de Gestión (Sin cambios) ---
+@app.post("/datasets/train_batch", summary="Subir y Entrenar un Lote de Datasets")
+async def upload_and_train_batch(
+    configs: str = Form(...),
+    models_to_train: List[str] = Form(...),
+    files: List[UploadFile] = File(...)
+):
+    # (El código de este endpoint es correcto, no se necesita cambiar)
+    print(f"API: Recibiendo petición de entrenamiento en lote para modelos: {models_to_train}")
+    
+    try:
+        dataset_configs = json.loads(configs)
+        uploaded_files_map = {file.filename: file for file in files}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error procesando la entrada: {e}")
+
+    launched_jobs_info = []
+    errors = []
+
+    for config in dataset_configs:
+        dataset_name = config.get("dataset_name")
+        target_column = config.get("target_column")
+        filename = config.get("filename")
+
+        if not all([dataset_name, target_column, filename]) or filename not in uploaded_files_map:
+            errors.append({"dataset_config": config, "error": "Configuración incompleta o archivo no encontrado."})
+            continue
+
+        file = uploaded_files_map[filename]
+        
+        try:
+            df = pd.read_csv(file.file)
+            await file.close()
+            
+            if target_column not in df.columns:
+                errors.append({"dataset_config": config, "error": f"Columna objetivo '{target_column}' no encontrada."})
+                continue
+
+            result_message = train.run_complete_training_job(
+                dataset_name, df, target_column, models_to_train
+            )
+            launched_jobs_info.append({"dataset_name": dataset_name, "status": result_message})
+
+        except Exception as e:
+            error_msg = f"Error al lanzar el job para '{dataset_name}': {e}"
+            errors.append({"dataset_config": config, "error": error_msg})
+            traceback.print_exc()
+
+    return {
+        "message": "Procesamiento de lote completado.",
+        "launched_jobs": launched_jobs_info,
+        "errors": errors
+    }
+
+@app.delete("/models/{dataset_name}", summary="Eliminar Modelos de un Dataset")
+async def delete_models(dataset_name: str):
+    # (El código de este endpoint es correcto, no se necesita cambiar)
+    try:
+        registry_actor = ray.get_actor("model_registry", namespace="mi_plataforma")
+        success = await registry_actor.delete_dataset_models.remote(dataset_name)
+        if success:
+            # Opcional: Borrar también los archivos del disco
+            base_model_dir = "/app/persistent_models"
+            for filename in os.listdir(base_model_dir):
+                if filename.startswith(f"{dataset_name}_") and filename.endswith(".pkl"):
+                    os.remove(os.path.join(base_model_dir, filename))
+            return {"message": f"Modelos para '{dataset_name}' eliminados del registro y del disco."}
+        else:
+            raise HTTPException(status_code=404, detail=f"No se encontraron modelos para el dataset '{dataset_name}'.")
+    except ValueError:
+        raise HTTPException(status_code=503, detail="ModelRegistryActor no disponible.")
+
+# --- Endpoint de Salud (Sin cambios) ---
+@app.get("/health", summary="Comprobación de Salud", status_code=200)
+async def health_check():
+    return {"status": "ok"}
+
+# --- Endpoints de Registro (MODIFICADOS) ---
+@app.get("/registry/models", summary="[REGISTRY] Listar todos los modelos disponibles")
+async def registry_list_models():
+    try:
+        registry_actor = ray.get_actor("model_registry", namespace="mi_plataforma")
+        models_details = await registry_actor.list_models_details.remote()
+        return models_details
+    except ValueError:
+        raise HTTPException(status_code=503, detail="ModelRegistryActor no disponible.")
+
+@app.get("/registry/artifacts/{dataset_name}/{model_type}/{artifact_name}", summary="[REGISTRY] Obtener un artefacto de modelo")
+async def registry_get_artifact(dataset_name: str, model_type: str, artifact_name: str):
+    try:
+        # 1. Intenta obtener el artefacto del actor en memoria (rápido)
+        registry_actor = ray.get_actor("model_registry", namespace="mi_plataforma")
+        artifacts_dict = await registry_actor.get_model_artifacts.remote(dataset_name, model_type)
+        
+        if not artifacts_dict:
+            # 2. Si no está en memoria, intenta cargarlo desde el disco (fallback)
+            model_path = f"/app/persistent_models/{dataset_name}_{model_type}.pkl"
+            print(f"Artefacto no en memoria. Intentando cargar desde disco: {model_path}")
+            if not os.path.exists(model_path):
+                raise HTTPException(status_code=404, detail="Modelo no encontrado ni en memoria ni en disco.")
+            with open(model_path, "rb") as f:
+                artifacts_dict = pickle.load(f)
+
+        serialized_artifact = artifacts_dict.get(artifact_name)
+        
+        if serialized_artifact is None:
+            raise HTTPException(status_code=404, detail=f"Artefacto '{artifact_name}' no encontrado en el modelo.")
+            
+        return Response(content=serialized_artifact, media_type="application/octet-stream")
+
+    except ValueError:
+        raise HTTPException(status_code=503, detail="ModelRegistryActor no disponible.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error interno obteniendo el artefacto: {e}")
+
+# management_api.py
+
+# ... (todos tus otros imports y endpoints se mantienen igual) ...
+
+
+# === NUEVO ENDPOINT PARA ANÁLISIS DE ARCHIVOS ===
+@app.post("/datasets/analyze", summary="Subir y Analizar un CSV para obtener sus columnas")
+async def analyze_dataset(file: UploadFile = File(...)):
+    """
+    Recibe un archivo CSV, lo lee con Pandas y devuelve información básica
+    como los nombres de las columnas y una previsualización de los datos.
+    """
+    try:
+        # Lee el contenido del archivo en un DataFrame
+        df = pd.read_csv(file.file)
+        
+        # Obtiene los nombres de las columnas
+        columns = df.columns.tolist()
+        
+        # Obtiene una previsualización de las primeras 5 filas en formato JSON
+        preview = df.head().to_dict(orient="records")
+        
+        return {
+            "filename": file.filename,
+            "columns": columns,
+            "preview": preview,
+            "rows": len(df)
+        }
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"No se pudo procesar el archivo. Error: {e}")
\ No newline at end of file
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new file mode 100644
index 0000000..832f314
--- /dev/null
+++ b/models_output/breast_cancer/decision_tree/metrics.json
@@ -0,0 +1,45 @@
+{
+    "accuracy": 0.9210526315789473,
+    "classification_report": {
+        "malignant": {
+            "precision": 0.8666666666666667,
+            "recall": 0.9285714285714286,
+            "f1-score": 0.896551724137931,
+            "support": 42.0
+        },
+        "benign": {
+            "precision": 0.9565217391304348,
+            "recall": 0.9166666666666666,
+            "f1-score": 0.9361702127659575,
+            "support": 72.0
+        },
+        "accuracy": 0.9210526315789473,
+        "macro avg": {
+            "precision": 0.9115942028985508,
+            "recall": 0.9226190476190477,
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diff --git a/models_output/wine/random_forest/metrics.json b/models_output/wine/random_forest/metrics.json
new file mode 100644
index 0000000..77c22e9
--- /dev/null
+++ b/models_output/wine/random_forest/metrics.json
@@ -0,0 +1,58 @@
+{
+    "accuracy": 1.0,
+    "classification_report": {
+        "class_0": {
+            "precision": 1.0,
+            "recall": 1.0,
+            "f1-score": 1.0,
+            "support": 12.0
+        },
+        "class_1": {
+            "precision": 1.0,
+            "recall": 1.0,
+            "f1-score": 1.0,
+            "support": 14.0
+        },
+        "class_2": {
+            "precision": 1.0,
+            "recall": 1.0,
+            "f1-score": 1.0,
+            "support": 10.0
+        },
+        "accuracy": 1.0,
+        "macro avg": {
+            "precision": 1.0,
+            "recall": 1.0,
+            "f1-score": 1.0,
+            "support": 36.0
+        },
+        "weighted avg": {
+            "precision": 1.0,
+            "recall": 1.0,
+            "f1-score": 1.0,
+            "support": 36.0
+        }
+    },
+    "confusion_matrix": [
+        [
+            12,
+            0,
+            0
+        ],
+        [
+            0,
+            14,
+            0
+        ],
+        [
+            0,
+            0,
+            10
+        ]
+    ],
+    "best_hyperparameters": {
+        "classifier__max_depth": 5,
+        "classifier__n_estimators": 50
+    },
+    "training_duration_sec": 0.6352250576019287
+}
\ No newline at end of file
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diff --git a/monitoring_utils.py b/monitoring_utils.py
new file mode 100644
index 0000000..03f10f2
--- /dev/null
+++ b/monitoring_utils.py
@@ -0,0 +1,160 @@
+# monitoring_utils.py (VERSIÓN CORREGIDA Y SIMPLIFICADA)
+import ray
+import requests
+import pandas as pd
+from prometheus_client.parser import text_string_to_metric_families
+
+def get_cluster_nodes_status():
+    """Consulta el estado de los nodos del clúster de Ray."""
+    if not ray.is_initialized(): return None
+    
+    nodes = ray.nodes()
+    status_list = []
+    for node in nodes:
+        resources = node.get("Resources", {})
+        status_list.append({
+            "Node ID": node.get("NodeID", "N/A")[:12],
+            "Estado": "VIVO" if node.get("Alive") else "MUERTO",
+            "IP": node.get("NodeManagerAddress", "N/A"),
+            "CPUs (Total)": resources.get("CPU", 0),
+            "Memoria (GB)": f"{node.get('memory_total_bytes', 0) / (1024**3):.2f}",
+            "Object Store (GB)": f"{node.get('object_store_memory_bytes', 0) / (1024**3):.2f}",
+        })
+    return status_list
+
+def get_actor_status(actor_name="model_registry", namespace="mi_plataforma"):
+    """
+    Verifica el estado de un actor nombrado de una manera más robusta,
+    sin usar ray.state.
+    """
+    if not ray.is_initialized():
+        return {"Estado": "Ray no inicializado", "Vivo": "❓"}
+    
+    try:
+        # El simple hecho de poder obtener el actor significa que está vivo.
+        # Si está muerto o no existe, ray.get_actor() lanzará un ValueError.
+        actor = ray.get_actor(actor_name, namespace=namespace)
+        
+        # Podemos hacer una llamada simple a un método (ping) para confirmar que responde.
+        # Añadamos un método 'ping' al actor para esto.
+        # Por ahora, asumimos que si get_actor tiene éxito, está vivo.
+        return {
+            "Nombre": actor_name,
+            "Estado": "ALIVE",
+            # No podemos obtener fácilmente el Node ID o los reinicios sin ray.state,
+            # así que simplificamos la salida.
+            "Reinicios": "N/A (API simplificada)",
+            "Vivo": "✅"
+        }
+    except ValueError:
+        # Esto ocurre si el actor no se encuentra (puede estar muerto o nunca se creó).
+        return {"Estado": "❌ No Encontrado / Muerto", "Vivo": "❌"}
+
+# La función get_inference_stats se mantiene igual
+def get_inference_stats(client):
+    """
+    Obtiene las estadísticas de inferencia desde el endpoint /metrics
+    utilizando un ResilientClient para alta disponibilidad.
+    """
+    try:
+        # En lugar de requests.get(metrics_url), usamos nuestro cliente inteligente.
+        # El endpoint para las métricas de Prometheus es siempre '/metrics'.
+        response = client.make_request("GET", "/metrics", timeout=5)
+        
+        stats = {
+            "total_requests": 0,
+            "average_latency_ms": 0.0,
+            "details_by_model": []
+        }
+        
+        model_data = {}
+        # El resto de la lógica para parsear las métricas es la misma.
+        for family in text_string_to_metric_families(response.text):
+            if family.name == "inference_latency_seconds_count":
+                for sample in family.samples:
+                    model_key = tuple(sorted(sample.labels.items()))
+                    if model_key not in model_data:
+                        model_data[model_key] = {"count": 0, "sum": 0.0}
+                    model_data[model_key]["count"] += sample.value
+            elif family.name == "inference_latency_seconds_sum":
+                 for sample in family.samples:
+                    model_key = tuple(sorted(sample.labels.items()))
+                    if model_key not in model_data:
+                        model_data[model_key] = {"count": 0, "sum": 0.0}
+                    model_data[model_key]["sum"] += sample.value
+
+        total_latency_sum = sum(data["sum"] for data in model_data.values())
+        stats["total_requests"] = int(sum(data["count"] for data in model_data.values()))
+
+        for model_key, data in model_data.items():
+            labels_dict = dict(model_key)
+            model_name = f"{labels_dict.get('dataset', 'N/A')}/{labels_dict.get('model_type', 'N/A')}"
+            stats["details_by_model"].append({
+                "Modelo": model_name, "Peticiones": int(data["count"]),
+                "Latencia Media (ms)": (data["sum"] / data["count"]) * 1000 if data["count"] > 0 else 0
+            })
+        if stats["total_requests"] > 0:
+            stats["average_latency_ms"] = (total_latency_sum / stats["total_requests"]) * 1000
+        return stats
+    except Exception as e:
+        return {"error": f"No se pudo obtener las métricas de inferencia: {e}"}
+
+def get_aggregated_inference_stats(client):
+    """
+    Obtiene métricas de TODAS las réplicas de un servicio y las agrega.
+    """
+    # Usamos la lista de servidores del cliente resiliente
+    all_servers = client.servers
+    if not all_servers:
+        return {"error": "No se encontraron servidores para el servicio de inferencia."}
+
+    aggregated_stats = {
+        "total_requests": 0,
+        "total_latency_sum": 0.0,
+        "details_by_model": {} # Usamos un dict para agregar por modelo
+    }
+    
+    # Iteramos sobre cada servidor descubierto
+    for server_url in all_servers:
+        try:
+            # Hacemos una petición directa a cada réplica
+            response = requests.get(f"{server_url}/metrics", timeout=2)
+            response.raise_for_status()
+            
+            # Parseamos la respuesta de esta réplica específica
+            for family in text_string_to_metric_families(response.text):
+                if family.name == "inference_latency_seconds_count":
+                    for sample in family.samples:
+                        # Creamos una clave única para cada modelo/dataset
+                        model_key = f"{sample.labels.get('dataset', 'N/A')}/{sample.labels.get('model_type', 'N/A')}"
+                        if model_key not in aggregated_stats["details_by_model"]:
+                            aggregated_stats["details_by_model"][model_key] = {"count": 0, "sum": 0.0}
+                        aggregated_stats["details_by_model"][model_key]["count"] += sample.value
+                elif family.name == "inference_latency_seconds_sum":
+                    for sample in family.samples:
+                        model_key = f"{sample.labels.get('dataset', 'N/A')}/{sample.labels.get('model_type', 'N/A')}"
+                        if model_key not in aggregated_stats["details_by_model"]:
+                            aggregated_stats["details_by_model"][model_key] = {"count": 0, "sum": 0.0}
+                        aggregated_stats["details_by_model"][model_key]["sum"] += sample.value
+
+        except requests.exceptions.RequestException as e:
+            print(f"No se pudo obtener métricas de la réplica {server_url}: {e}")
+            # Continuamos con la siguiente réplica si una falla
+
+    # Ahora calculamos los totales a partir de los datos agregados
+    total_reqs = sum(data["count"] for data in aggregated_stats["details_by_model"].values())
+    total_latency = sum(data["sum"] for data in aggregated_stats["details_by_model"].values())
+    
+    final_stats = {
+        "total_requests": int(total_reqs),
+        "average_latency_ms": (total_latency / total_reqs) * 1000 if total_reqs > 0 else 0,
+        "details_by_model": [
+            {
+                "Modelo": model_key,
+                "Peticiones": int(data["count"]),
+                "Latencia Media (ms)": (data["sum"] / data["count"]) * 1000 if data["count"] > 0 else 0
+            } for model_key, data in aggregated_stats["details_by_model"].items()
+        ]
+    }
+    
+    return final_stats
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..d52d906
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,83 @@
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.13
+aiohttp-cors==0.8.1
+python-multipart
+aiosignal==1.3.2
+annotated-types==0.7.0
+anyio==4.9.0
+async-timeout==5.0.1
+attrs==25.3.0
+cachetools==5.5.2
+certifi==2025.6.15
+charset-normalizer==3.4.2
+click==8.2.1
+colorama==0.4.6
+colorful==0.5.6
+distlib==0.3.9
+exceptiongroup==1.3.0
+fastapi==0.115.13
+filelock==3.18.0
+frozenlist==1.7.0
+google-api-core==2.25.1
+google-auth==2.40.3
+googleapis-common-protos==1.70.0
+grpcio==1.73.0
+h11==0.16.0
+httptools==0.6.4
+idna==3.10
+importlib_metadata==8.7.0
+joblib==1.5.1
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+msgpack==1.1.1
+multidict==6.5.0
+numpy==2.2.6
+opencensus==0.11.4
+opencensus-context==0.1.3
+opentelemetry-api==1.34.1
+opentelemetry-exporter-prometheus==0.55b1
+opentelemetry-proto==1.34.1
+opentelemetry-sdk==1.34.1
+opentelemetry-semantic-conventions==0.55b1
+packaging==25.0
+pandas==2.3.0
+platformdirs==4.3.8
+prometheus_client==0.22.1
+propcache==0.3.2
+proto-plus==1.26.1
+protobuf==5.29.5
+py-spy==0.4.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.11.7
+pydantic_core==2.33.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.0
+pytz==2025.2
+PyYAML==6.0.2
+ray==2.47.1
+referencing==0.36.2
+requests==2.32.4
+rpds-py==0.25.1
+rsa==4.9.1
+scikit-learn==1.7.0
+scipy==1.15.3
+six==1.17.0
+smart-open==7.1.0
+sniffio==1.3.1
+starlette==0.46.2
+threadpoolctl==3.6.0
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.34.3
+virtualenv==20.31.2
+watchfiles==1.1.0
+websockets==15.0.1
+wrapt==1.17.2
+yarl==1.20.1
+zipp==3.23.0
+matplotlib
+seaborn
+streamlit
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..4b8a6af
--- /dev/null
+++ b/train.py
@@ -0,0 +1,205 @@
+# train.py
+
+import ray
+import os
+import pickle
+import json
+import traceback
+import pandas as pd
+import socket
+import time
+from typing import List
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.tree import DecisionTreeClassifier
+
+# --- Actor de Registro de Modelos (Guarda artefactos en memoria) ---
+@ray.remote
+class ModelRegistryActor:
+    def __init__(self):
+        # Almacena el diccionario completo de artefactos serializados (bytes)
+        self.registered_models = {}
+        print(f"[{self.__class__.__name__}] Actor de Registro de Modelos (en memoria) inicializado.")
+    
+    def register_model(self, dataset_name, model_type, result_dict):
+        """
+        Registra un modelo bajo un dataset específico.
+        """
+
+        if dataset_name not in self.registered_models:
+            self.registered_models[dataset_name] = {}
+        
+        self.registered_models[dataset_name][model_type] = result_dict
+        print(f"Registro en memoria para {dataset_name}/{model_type} completado.")
+        return True
+
+    def get_model_artifacts(self, dataset_name, model_type):
+        """
+        Devuelve el diccionario de artefactos serializados desde la memoria.
+        """
+        return self.registered_models.get(dataset_name, {}).get(model_type)
+
+    def list_models_details(self):
+        details = {}
+        for dataset, models in self.registered_models.items():
+            model_types = list(models.keys())
+            if model_types:
+                details[dataset] = {"available_models": model_types}
+        return details
+
+    def delete_dataset_models(self, dataset_name):
+        if dataset_name in self.registered_models:
+            del self.registered_models[dataset_name]
+            # Nota: Esto no borra el archivo del disco. Se podría añadir esa lógica si se desea.
+            return True
+        return False
+
+# --- Tarea de Entrenamiento Remota (Sin Cambios) ---
+@ray.remote(num_cpus=1)
+def train_and_serialize_model(
+    data_df_input,
+    target_column_name: str,
+    model_config: dict,
+    param_grid_config: dict,
+    dataset_id: str,
+    feature_names: List[str],
+    class_names_for_metrics: List[str]
+):
+    hostname_str = socket.gethostname()
+    log_prefix = f"Worker [{hostname_str}]"
+    data_df = data_df_input
+
+    model_type = model_config['type']
+    print(f"{log_prefix} - Iniciando entrenamiento para {model_type} en {dataset_id}...")
+    
+    try:
+        X = data_df.drop(columns=[target_column_name])
+        y = data_df[target_column_name]
+        
+        stratify_option = y if len(y.unique()) > 1 and all(y.value_counts() >= 3) else None
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=0.2, random_state=42, stratify=stratify_option
+        )
+
+        preprocessor = Pipeline([
+            ('imputer', SimpleImputer(strategy='mean')),
+            ('scaler', StandardScaler())
+        ])
+        
+        base_model_params = model_config.get('params', {})
+        if model_type == "logistic_regression":
+            model_instance = LogisticRegression(**base_model_params)
+        elif model_type == "decision_tree":
+            model_instance = DecisionTreeClassifier(**base_model_params)
+        elif model_type == "random_forest":
+            model_instance = RandomForestClassifier(**base_model_params)
+        else:
+            raise ValueError(f"Tipo de modelo '{model_type}' no reconocido.")
+        
+        full_pipeline = Pipeline([
+            ('preprocessor', preprocessor),
+            ('classifier', model_instance)
+        ])
+        
+        param_grid = {f'classifier__{k}': v for k, v in param_grid_config.items()}
+        grid_search = GridSearchCV(estimator=full_pipeline, param_grid=param_grid, cv=3, n_jobs=1)
+        
+        start_fit_time = time.time()
+        grid_search.fit(X_train, y_train)
+        fit_duration = time.time() - start_fit_time
+        
+        best_pipeline_obj = grid_search.best_estimator_
+        y_pred = best_pipeline_obj.predict(X_test)
+        
+        metrics = {
+            'accuracy': accuracy_score(y_test, y_pred),
+            'classification_report': classification_report(y_test, y_pred, target_names=class_names_for_metrics, output_dict=True, zero_division=0),
+            'confusion_matrix': confusion_matrix(y_test, y_pred).tolist(),
+            'best_hyperparameters': grid_search.best_params_,
+            'training_duration_sec': fit_duration
+        }
+        
+        print(f"{log_prefix} - Entrenamiento para {model_type} en {dataset_id} completado. Accuracy: {metrics['accuracy']:.4f}")
+        
+        result_dict = {
+            "pipeline": pickle.dumps(best_pipeline_obj),
+            "feature_names": pickle.dumps(feature_names),
+            "metrics": json.dumps(metrics).encode('utf-8')
+        }
+        return result_dict
+
+    except Exception as e:
+        print(f"{log_prefix} - ERROR en la tarea de entrenamiento para {dataset_id}/{model_config['type']}: {e}")
+        traceback.print_exc()
+        return None
+
+# --- Función Orquestadora Principal (Con Persistencia) ---
+def run_complete_training_job(dataset_name: str, df: pd.DataFrame, target_column: str, models_to_train: List[str]):
+    print(f"ORQUESTADOR: Iniciando trabajo para '{dataset_name}'. Modelos: {models_to_train}")
+    
+    registry_actor = ray.get_actor("model_registry", namespace="mi_plataforma")
+    
+    feature_names = [col for col in df.columns if col != target_column]
+    class_names = [str(c) for c in sorted(df[target_column].unique())]
+    
+    all_model_configurations = {
+        'logistic_regression': {'params': {'max_iter': 200, 'random_state': 42}, 'param_grid': {'C': [0.1, 1.0]}},
+        'decision_tree': {'params': {'random_state': 42}, 'param_grid': {'max_depth': [5], 'min_samples_split': [5]}},
+        'random_forest': {'params': {'n_jobs': 1, 'random_state': 42}, 'param_grid': {'n_estimators': [50], 'max_depth': [10]}}
+    }
+    
+    selected_model_configs = {
+        model_type: config for model_type, config in all_model_configurations.items() if model_type in models_to_train
+    }
+
+    if not selected_model_configs:
+        return f"Error: Ninguno de los modelos solicitados {models_to_train} es válido."
+
+    print(f"ORQUESTADOR: Lanzando {len(selected_model_configs)} tareas para '{dataset_name}'...")
+    
+    task_refs = []
+    for model_type, model_config in selected_model_configs.items():
+        task_ref = train_and_serialize_model.remote(
+            df, 
+            target_column, 
+            {'type': model_type, 'params': model_config['params']}, 
+            model_config['param_grid'], 
+            dataset_name, 
+            feature_names,
+            class_names
+        )
+        task_refs.append((model_type, task_ref))
+
+    print(f"ORQUESTADOR: Esperando la finalización de {len(task_refs)} tareas...")
+    
+    base_model_dir = "/app/persistent_models"
+    os.makedirs(base_model_dir, exist_ok=True)
+    
+    for model_type, ref in task_refs:
+        try:
+            result_dictionary = ray.get(ref)
+            if result_dictionary:
+                # 1. Registrar en el actor en memoria para acceso rápido
+                registry_actor.register_model.remote(dataset_name, model_type, result_dictionary)
+                
+                # 2. Guardar en disco para persistencia a largo plazo
+                model_file_path = os.path.join(base_model_dir, f"{dataset_name}_{model_type}.pkl")
+                with open(model_file_path, "wb") as f:
+                    pickle.dump(result_dictionary, f)
+                
+                print(f"ORQUESTADOR: Modelo guardado en disco en: {model_file_path}")
+            else:
+                print(f"ORQUESTADOR: Tarea para {model_type} en {dataset_name} falló (devolvió None).")
+        except Exception as e:
+            print(f"ORQUESTADOR: Excepción al obtener resultado para {model_type}: {e}")
+            traceback.print_exc()
+
+    final_message = f"Trabajo para '{dataset_name}' completado."
+    print(f"ORQUESTADOR: {final_message}")
+    return final_message
\ No newline at end of file
diff --git a/wait_for_ray_head.py b/wait_for_ray_head.py
new file mode 100644
index 0000000..e69de29
diff --git a/wait_for_services.py b/wait_for_services.py
new file mode 100644
index 0000000..3850021
--- /dev/null
+++ b/wait_for_services.py
@@ -0,0 +1,37 @@
+# wait_for_services.py
+import socket
+import time
+import sys
+
+def wait_for_service(host, port, service_name):
+    """Intenta conectarse a un servicio host:port hasta que tenga éxito o se agote el tiempo."""
+    print(f'WAIT_SCRIPT: Esperando a {service_name} en {host}:{port}...')
+    
+    # Intentar por ~90 segundos (45 intentos * 2s de espera + timeout)
+    for i in range(45):
+        try:
+            # Usar 'with' asegura que el socket se cierre automáticamente
+            with socket.create_connection((host, port), timeout=2):
+                print(f'WAIT_SCRIPT: ¡{service_name} está accesible!')
+                return True
+        except (socket.timeout, ConnectionRefusedError, socket.gaierror) as e:
+            # ConnectionRefusedError es común si el servidor aún no está escuchando
+            print(f'WAIT_SCRIPT: Intento {i+1}/45, {service_name} aún no está listo ({type(e).__name__})...')
+            time.sleep(2)
+        except Exception as e_generic:
+             print(f'WAIT_SCRIPT: Intento {i+1}/45: Error de socket inesperado ({type(e_generic).__name__}: {e_generic})...')
+             time.sleep(2)
+
+    print(f'WAIT_SCRIPT ERROR: {service_name} no estuvo disponible después del tiempo de espera.')
+    return False
+
+if __name__ == "__main__":
+    # Verificar Management API
+    if not wait_for_service('management-api', 9000, 'Management API'):
+        sys.exit(1) # Salir con error si no se puede conectar
+    
+    # Verificar Inference API
+    if not wait_for_service('api-service', 8000, 'Inference API'):
+        sys.exit(1)
+    
+    print("WAIT_SCRIPT: Todas las APIs dependientes están listas.")
\ No newline at end of file