Real-World AI Projects: From Concept to Production

Key Components:

Implementation:

# Temporal Fusion Transformer
from pytorch_forecasting import TemporalFusionTransformer

model = TemporalFusionTransformer.from_dataset(
    training_dataset,
    hidden_size=32,
    lstm_layers=2,
    attention_head_size=4,
    dropout=0.1
)

# AWS SageMaker deployment
from sagemaker.pytorch import PyTorchModel

pytorch_model = PyTorchModel(
    model_data='s3://bucket/model.tar.gz',
    role=sagemaker_role,
    framework_version='1.8.0',
    entry_script='inference.py'
)

predictor = pytorch_model.deploy(
    instance_type='ml.m5.large',
    initial_instance_count=1
)
        

Monitoring:

Project Architecture:

• Data Pipeline: DICOM → PNG conversion (PyDicom)
• Annotation: CVAT with radiologists
• Model: MONAI 3D UNet
• Deployment: NVIDIA Clara

Implementation:

# MONAI 3D segmentation
from monai.networks.nets import UNet
from monai.losses import DiceLoss

model = UNet(
    spatial_dims=3,
    in_channels=1,
    out_channels=2,
    channels=(16, 32, 64, 128, 256),
    strides=(2, 2, 2, 2),
    num_res_units=2
)

loss_function = DiceLoss(to_onehot_y=True, softmax=True)
optimizer = torch.optim.Adam(model.parameters(), 1e-4)

# Federated Learning with NVIDIA FLARE
from nvflare.apis.dxo import DXO
from nvflare.app_opt.pt.client_api import PTFedClient

class MedicalClient(PTFedClient):
    def train(self, model, data):
        # Local training logic
        return DXO(data_kind=DataKind.WEIGHTS, data=model.state_dict())
        

Compliance Considerations:

# Data anonymization
import dicognito

anonymizer = dicognito.Anonymizer()
anonymizer.anonymize("input.dcm", "output.dcm")

# Grad-CAM visualization
from monai.visualize import GradCAM

cam = GradCAM(nn_module=model, target_layers="conv_final")
result = cam(x=test_image, class_idx=1)

Implementation Framework:

1. Edge Collection: IoT sensors → Kafka stream
2. Feature Engineering: Rolling averages (Spark)
3. Anomaly Detection: LSTM Autoencoder
4. Alerting: PagerDuty integration

Code Implementation:

# LSTM Autoencoder
class AnomalyDetector(nn.Module):
    def __init__(self, input_dim=10, hidden_dim=64):
        super().__init__()
        self.encoder = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.decoder = nn.LSTM(hidden_dim, input_dim, batch_first=True)
        
    def forward(self, x):
        encoded, _ = self.encoder(x)
        decoded, _ = self.decoder(encoded)
        return decoded

# Azure IoT Edge Deployment
from azure.iot.device import Message
from azure.iot.device.aio import IoTHubModuleClient

async def send_alert(anomaly_score):
    module_client = IoTHubModuleClient.create_from_edge_environment()
    await module_client.connect()
    message = Message(json.dumps({
        "machine_id": "CNC-27",
        "anomaly_score": anomaly_score,
        "timestamp": datetime.utcnow().isoformat()
    }))
    await module_client.send_message_to_output(message, "alertOutput")
        

ROI Calculation:

MLOps Workflow:

Project Templates: