AI Solutions

Enterprise AI

Production-ready AI solutions built for Canadian enterprises. Deploy secure, scalable ML systems with guaranteed data sovereignty.

Azure ML Pipeline

Production deployment on Azure

MLOps Setup

Automated ML pipelines

Custom Development

Specialized AI solutions

Security & Compliance

Enterprise-grade security

deployment.py

from stella.ml import Pipeline, ModelRegistry
from stella.deploy import AzureDeployment
from stella.monitor import MetricsCollector

# Initialize production ML pipeline
pipeline = Pipeline(
    name="vision-qa",
    registry=ModelRegistry(
        azure_location="canadaeast",
        compliance=["PIPEDA", "SOC2"]
    )
)

# Configure model deployment
deployment = AzureDeployment(
    pipeline=pipeline,
    compute="gpu-cluster",
    scaling={
        "min_replicas": 2,
        "max_replicas": 10,
        "target_gpu_util": 0.7
    }
)

# Deploy to production
deployment.launch(
    monitoring=metrics,
    canary=True,
    rollback_threshold=0.98
)

Production ML Systems

Enterprise-grade machine learning infrastructure deployed on Azure and AWS.

Automated MLOps pipelines
Scalable training infrastructure
Real-time inference APIs
Performance monitoring

Canadian Compliance

Built-in compliance with Canadian data sovereignty requirements.

PIPEDA compliance
Data residency guarantee
Audit trail & logging
Access control

Custom Solutions

Specialized AI solutions tailored for your business needs.

Computer vision systems
NLP processing
Recommendation engines
Time series forecasting

Performance Benchmarks

Latency

ResNet5023ms

BERT Base45ms

GPT-2 Small120ms

Custom CV15ms

Throughput

ResNet50250 req/s

BERT Base120 req/s

GPT-2 Small45 req/s

Custom CV400 req/s

GPU Utilization

ResNet5065%

BERT Base78%

GPT-2 Small85%

Custom CV55%

Deployment Architectures

High-Performance Vision API

Scalable computer vision API with real-time inference

Production

Configuration

config.yaml

# Kubernetes configuration
resources:
  limits:
    nvidia.com/gpu: 1
  requests:
    nvidia.com/gpu: 1
    memory: "16Gi"
    cpu: "4"

replicas:
  min: 2
  max: 8
  targetCPUUtilization: 70
  targetGPUUtilization: 80

cache:
  redis:
    size: "cache.r6g.xlarge"
    replication: true

Components

Azure Kubernetes Service / EKS

NVIDIA A100 GPU nodes

Redis Cache cluster

Azure Front Door / CloudFront CDN

Azure Monitor / CloudWatch

Best For

High-throughput computer vision processing

Real-time video analysis

Multi-tenant ML API services

Global-scale ML applications

Serverless NLP Pipeline

Scalable NLP processing system with automatic scaling

Production

Configuration

config.yaml

# AWS SAM Template
Resources:
  ProcessingFunction:
    Type: AWS::Serverless::Function
    Properties:
      MemorySize: 4096
      Timeout: 900
      Environment:
        Variables:
          MODEL_ENDPOINT: !Ref SageMakerEndpoint
          BATCH_SIZE: 32
      Policies:
        - SageMakerInvokeEndpointPolicy
        - DynamoDBCrudPolicy

  ModelEndpoint:
    Type: AWS::SageMaker::EndpointConfig
    Properties:
      ProductionVariants:
        - ModelName: !Ref ModelName
          InstanceType: ml.g4dn.xlarge
          InitialInstanceCount: 2
          VariantName: AllTraffic

Components

AWS Lambda functions

SageMaker endpoints

DynamoDB streams

S3 event triggers

CloudWatch metrics

Best For

Document processing workloads

Async NLP tasks

Cost-optimized processing

Variable workload patterns

Multi-Region ML Platform

Globally distributed ML platform with data sovereignty

Enterprise

Configuration

config.yaml

# Terraform Configuration
module "ml_platform" {
  source = "./modules/ml-platform"

  regions = {
    canada-east = {
      compute_tier   = "gpu-premium"
      instance_count = 4
      data_replication = false
    }
    canada-central = {
      compute_tier   = "gpu-standard"
      instance_count = 2
      data_replication = true
    }
  }

  compliance = {
    data_sovereignty = true
    encryption = "customer-managed"
    audit_logging = true
  }
}

Components

Multi-region Kubernetes clusters

Global load balancing

Regional data stores

Cross-region monitoring

Compliance automation

Best For

Enterprise ML platforms

Regulated industries

High-availability requirements

Canadian data sovereignty needs

Infrastructure & Security

Azure ML Enterprise

Production

Infrastructure as Code

terraform.tf

# Terraform for Azure ML
resource "azurerm_machine_learning_workspace" "mlw" {
  name                = "mlw-prod-canadaeast"
  location            = "canadaeast"
  resource_group_name = "rg-ml-prod"

  identity {
    type = "SystemAssigned"
  }

  encryption {
    key_vault_id = azurerm_key_vault.kv.id
    cmk_enabled  = true
  }

  high_business_impact = true
  
  private_endpoints {
    subnet_id = azurerm_subnet.private.id
  }
}

resource "azurerm_kubernetes_cluster" "aks" {
  name                = "aks-ml-prod"
  gpu_profile {
    node_count = 4
    sku        = "Standard_NC24ads_A100_v4"
  }
}

Core Services

Azure ML

AKS

Key Vault

Monitor

Features

Private Link enabled workspace

Customer-managed keys

RBAC integration

Private AKS cluster

AWS ML Platform

Production

Infrastructure as Code

terraform.tf

# CloudFormation for SageMaker
Resources:
  SageMakerDomain:
    Type: AWS::SageMaker::Domain
    Properties:
      DomainName: prod-ml-platform
      AuthMode: IAM
      VpcId: !Ref VpcId
      SubnetIds: 
        - !Ref PrivateSubnet1
        - !Ref PrivateSubnet2
      DefaultUserSettings:
        ExecutionRole: !GetAtt SageMakerExecutionRole.Arn
        SecurityGroups: 
          - !Ref MLSecurityGroup

  ModelEndpoint:
    Type: AWS::SageMaker::EndpointConfig
    Properties:
      ProductionVariants:
        - InitialInstanceCount: 2
          InstanceType: ml.g4dn.xlarge
          VariantName: AllTraffic

Core Services

SageMaker

EKS

KMS

CloudWatch

Features

VPC endpoint integration

KMS encryption

IAM roles per workload

Private EKS cluster

ML Infrastructure Patterns

Distributed Training Cluster

High-performance training infrastructure for large models

Infrastructure Config

# Kubernetes Config
apiVersion: v1
kind: Pod
metadata:
  name: distributed-training
spec:
  containers:
  - name: trainer
    image: stella/trainer:latest
    resources:
      limits:
        nvidia.com/gpu: 4
    env:
      - name: WORLD_SIZE
        value: "4"
      - name: MASTER_ADDR
        value: "trainer-0"
      - name: MASTER_PORT
        value: "29500"

Hardware

4x A100 80GB

100Gbps Network

2TB NVMe Cache

384GB RAM

Features

Automatic checkpointing

Gradient accumulation

Multi-node scaling

Memory optimization

Performance

93%

GPU Utilization

1.2PB/s

Network

98%

Training Efficiency

24/7

Uptime

Fine-tuning Pipeline

Efficient infrastructure for model adaptation

Infrastructure Config

# Training Config
train_config:
  base_model: "azure://models/bert-base"
  optimization:
    optimizer: "adamw"
    lr: 2e-5
    warmup_steps: 500
  distributed:
    strategy: "deepspeed"
    zero_stage: 3
    gradient_accumulation: 16

Hardware

2x V100 16GB

40Gbps Network

1TB SSD Cache

256GB RAM

Features

Parameter-efficient tuning

Layer freezing options

Mixed precision training

Dynamic batching

Performance

89%

GPU Utilization

800GB/s

Network

95%

Training Efficiency

12hr

Avg Duration

Implementation Process

Architecture Design

Comprehensive assessment and architecture planning tailored to your requirements.

Infrastructure Setup

Secure, scalable infrastructure deployment with monitoring and logging.

Model Development

Custom model development and optimization for your specific use case.

Production Deployment

Automated deployment pipeline with testing and validation.

Enterprise Benefits

Reduced Time-to-Market

Accelerate AI implementation with production-ready infrastructure.

Cost Optimization

Efficient resource utilization with automated scaling.

Enterprise Security

Bank-grade security with Canadian compliance built-in.

Scalable Architecture

Future-proof infrastructure that grows with your needs.