Agentic Frontiers

from modelforge import MLPipeline, ModelRegistry
import mlflow
import docker

class ModelForgePipeline:
    def __init__(self, project_name):
        self.project_name = project_name
        self.registry = ModelRegistry()
        self.client = docker.from_env()
        
    def deploy_model(self, model_path, version, environment='staging'):
        """Deploy ML model with automated testing"""
        try:
            # Load and validate model
            model = mlflow.pytorch.load_model(model_path)
            test_results = self.run_model_tests(model)
            
            if test_results['accuracy'] > 0.85:
                # Build Docker container
                container = self.build_container(model, version)
                
                # Deploy to environment
                deployment = self.deploy_container(container, environment)
                
                # Register in model registry
                self.registry.register_model(
                    name=self.project_name,
                    version=version,
                    metrics=test_results,
                    deployment_id=deployment.id
                )
                
                return {
                    'status': 'success',
                    'deployment_id': deployment.id,
                    'metrics': test_results
                }
            else:
                raise ValueError("Model accuracy below threshold")
                
        except Exception as e:
            self.rollback_deployment(version)
            return {'status': 'failed', 'error': str(e)}

# Pipeline Configuration
pipeline = ModelForgePipeline('intelliops-model')
result = pipeline.deploy_model('./models/latest/', 'v2.1.0')

import hashlib
import asyncio
from typing import Dict, List
import numpy as np

class SecureShield:
    def __init__(self):
        self.threat_patterns = self.load_threat_signatures()
        self.risk_threshold = 0.7
        
    async def analyze_security_event(self, event_data: Dict) -> Dict:
        """Analyze security event and determine threat level"""
        threat_score = await self.calculate_threat_score(event_data)
        
        if threat_score > self.risk_threshold:
            await self.trigger_security_response(event_data, threat_score)
            
        return {
            'event_id': event_data.get('id'),
            'threat_level': self.get_threat_level(threat_score),
            'risk_score': threat_score,
            'recommendations': self.generate_security_recommendations(event_data)
        }
    
    async def calculate_threat_score(self, event: Dict) -> float:
        """Calculate threat score using ML algorithms"""
        features = self.extract_features(event)
        
        # Behavioral analysis
        behavioral_score = self.analyze_behavior_patterns(features)
        
        # Signature matching
        signature_score = self.match_threat_signatures(features)
        
        # Combine scores
        combined_score = (behavioral_score * 0.6) + (signature_score * 0.4)
        
        return min(combined_score, 1.0)
    
    def hash_password(self, password: str) -> str:
        """Secure password hashing"""
        salt = "intellecta_secure_salt"
        return hashlib.sha256((password + salt).encode()).hexdigest()

# Security Event Analysis
shield = SecureShield()
event = {
    'id': 'evt_001',
    'source_ip': '192.168.1.100',
    'event_type': 'login_attempt',
    'timestamp': '2025-08-24T21:30:00Z'
}

result = await shield.analyze_security_event(event)

import boto3
import asyncio
from botocore.exceptions import ClientError

class CloudShiftManager:
    def __init__(self, region_name='us-east-1'):
        self.s3_client = boto3.client('s3', region_name=region_name)
        self.ec2_client = boto3.client('ec2', region_name=region_name)
        self.ecs_client = boto3.client('ecs', region_name=region_name)
        
    async def migrate_ml_workload(self, workload_config):
        """Migrate ML workload to cloud with optimization"""
        try:
            # Create optimized S3 buckets
            buckets = await self.setup_data_storage(workload_config)
            
            # Deploy compute resources
            compute_resources = await self.deploy_compute_cluster(workload_config)
            
            # Setup monitoring
            monitoring = await self.setup_monitoring(workload_config)
            
            return {
                'status': 'success',
                'resources': {
                    'storage': buckets,
                    'compute': compute_resources,
                    'monitoring': monitoring
                },
                'estimated_cost_savings': '45%'
            }
            
        except Exception as e:
            await self.rollback_migration(workload_config['id'])
            return {'status': 'failed', 'error': str(e)}
    
    async def setup_data_storage(self, config):
        """Setup optimized S3 storage"""
        bucket_name = f"intellecta-ml-{config['project_id']}"
        
        try:
            self.s3_client.create_bucket(
                Bucket=bucket_name,
                CreateBucketConfiguration={
                    'LocationConstraint': 'us-west-2'
                }
            )
            
            # Enable intelligent tiering
            self.s3_client.put_bucket_intelligent_tiering_configuration(
                Bucket=bucket_name,
                Id='EntireBucket',
                IntelligentTieringConfiguration={
                    'Id': 'EntireBucket',
                    'Status': 'Enabled',
                    'Prefix': '',
                }
            )
            
            return {'bucket_name': bucket_name, 'region': 'us-west-2'}
            
        except ClientError as e:
            raise Exception(f"Failed to create storage: {e}")

# Initialize CloudShift
cloud_shift = CloudShiftManager()
config = {
    'project_id': 'intelliops-migration',
    'workload_type': 'ml_training',
    'data_size_gb': 500
}

migration_result = await cloud_shift.migrate_ml_workload(config)

from kubernetes import client, config
from kubernetes.client.rest import ApiException
import yaml

class KubeGeniusOrchestrator:
    def __init__(self):
        config.load_incluster_config()  # For in-cluster usage
        self.v1 = client.CoreV1Api()
        self.apps_v1 = client.AppsV1Api()
        
    async def smart_deploy(self, app_config):
        """Deploy application with AI-optimized configuration"""
        try:
            # Analyze resource requirements
            optimized_resources = self.optimize_resources(app_config)
            
            # Create namespace if not exists
            await self.ensure_namespace(app_config['namespace'])
            
            # Deploy with intelligent scaling
            deployment = await self.create_smart_deployment(
                app_config, 
                optimized_resources
            )
            
            # Setup auto-scaling
            hpa = await self.setup_auto_scaling(deployment)
            
            # Create service
            service = await self.create_service(app_config)
            
            return {
                'status': 'deployed',
                'deployment': deployment.metadata.name,
                'service': service.metadata.name,
                'replicas': optimized_resources['replicas'],
                'resource_optimization': '30% improvement'
            }
            
        except ApiException as e:
            return {'status': 'failed', 'error': str(e)}
    
    def optimize_resources(self, config):
        """AI-based resource optimization"""
        base_cpu = config.get('cpu', '100m')
        base_memory = config.get('memory', '128Mi')
        
        # Intelligent scaling based on app type
        if config['type'] == 'ml_inference':
            return {
                'cpu': '500m',
                'memory': '1Gi',
                'replicas': 3
            }
        elif config['type'] == 'web_api':
            return {
                'cpu': '200m',
                'memory': '256Mi',
                'replicas': 2
            }
        else:
            return {
                'cpu': base_cpu,
                'memory': base_memory,
                'replicas': 1
            }

# Deploy with KubeGenius
kube_genius = KubeGeniusOrchestrator()
app_config = {
    'name': 'intelliops-api',
    'namespace': 'intellecta',
    'type': 'ml_inference',
    'image': 'intellecta/intelliops:v2.1.0'
}

deployment_result = await kube_genius.smart_deploy(app_config)