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Advanced ML Pipeline Features

The Hokusai ML pipeline includes advanced features for production deployments and enterprise use cases.

Core Features​

Model Registry​

The pipeline integrates with MLFlow for comprehensive model management:

# Automatic model logging during pipeline execution
@step
def train_model(self):
    with mlflow.start_run(run_name=f"hokusai_{self.run_id}"):
        # Train model
        model = train_with_contributed_data(
            baseline=self.baseline_model,
            new_data=self.contributed_data
        )
        
        # Log to registry
        mlflow.log_model(model, "model")
        mlflow.log_metrics({
            "accuracy": accuracy,
            "deltaone": self.deltaone_score
        })

Current Capabilities:

  • Automatic model versioning
  • Performance metric tracking
  • Experiment comparison
  • Model artifact storage

Evaluation Framework​

The pipeline includes sophisticated evaluation capabilities:

# Built-in evaluation metrics
from src.pipeline.evaluation import EvaluationFramework

evaluator = EvaluationFramework()
results = evaluator.compare_models(
    baseline_model=baseline,
    improved_model=improved,
    test_data=test_set,
    metrics=["accuracy", "f1", "latency", "perplexity"]
)

# Automatic DeltaOne calculation
deltaone_score = evaluator.compute_deltaone(results)

Features:

  • Multiple metric support
  • Statistical significance testing
  • Performance benchmarking
  • Automated scoring

Data Processing Pipeline​

Efficient handling of large datasets:

# Streaming data processing
from src.pipeline.data_processor import StreamingProcessor

processor = StreamingProcessor()
for batch in processor.process_large_dataset("path/to/huge_dataset.csv"):
    # Process in chunks to handle any size
    validated_batch = processor.validate(batch)
    processed_batch = processor.transform(batch)
    yield processed_batch

Optimizations:

  • Streaming processing for large files
  • Automatic batching
  • Memory-efficient operations
  • Parallel processing support

Pipeline API​

Programmatic access to pipeline functionality:

# Direct pipeline invocation
from src.pipeline.hokusai_pipeline import HokusaiPipeline

pipeline = HokusaiPipeline()
result = pipeline.run(
    contributed_data="path/to/data.csv",
    eth_address="0x...",
    model_type="gpt-3.5-turbo",
    dry_run=False
)

print(f"DeltaOne Score: {result.deltaone_score}")
print(f"Attestation: {result.attestation_hash}")

Deployment Options​

Cloud Deployment​

The pipeline runs on Hokusai's managed infrastructure:

  • Kubernetes-based orchestration
  • Auto-scaling for large workloads
  • Built-in monitoring and logging
  • No infrastructure management required

Self-Hosted​

Run the pipeline on your own infrastructure:

  • Full control over data and compute
  • Customizable resource allocation
  • Private model training
  • Enterprise support available

Hybrid Mode​

Combine cloud and on-premise:

  • Sensitive data stays local
  • Leverage Hokusai's evaluation network
  • Attestation verification on-chain

Integration Examples​

REST API Integration​

# Submit data for evaluation via API
import requests

response = requests.post(
    "https://api.hokusai.io/evaluate",
    json={
        "model_type": "gpt-3.5-turbo",
        "data_url": "s3://bucket/data.csv",
        "eth_address": "0x..."
    },
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

result = response.json()
print(f"Job ID: {result['job_id']}")
print(f"Status: {result['status']}")

Python SDK Usage​

# Direct pipeline integration
from src.pipeline import utils

# Validate data before submission
validation_result = utils.validate_dataset("data.csv")
if validation_result.is_valid:
    # Submit to pipeline
    job = submit_to_pipeline(validation_result.cleaned_data)
    attestation = wait_for_completion(job.id)

Performance & Scalability​

Benchmarks​

  • Throughput: Process 1M+ records/hour
  • Latency: < 5 minutes for typical evaluation
  • Scalability: Horizontal scaling via Kubernetes
  • Reliability: 99.9% uptime SLA

Resource Requirements​

# Minimum requirements
resources:
  cpu: 4 cores
  memory: 16GB
  storage: 100GB
  
# Recommended for production
resources:
  cpu: 16 cores
  memory: 64GB
  storage: 1TB
  gpu: Optional (speeds up training)

Security & Privacy​

Data Protection​

  • PII Detection: Automatic scanning and removal
  • Encryption: Data encrypted at rest and in transit
  • Access Control: Role-based permissions
  • Audit Logging: Complete activity tracking

Compliance​

  • GDPR compliant data handling
  • SOC 2 Type II certification (in progress)
  • Regular security audits
  • Data retention policies

Support & Resources​

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