Skip to main content

GitLab ML Platform Integration Guide

Overview​

GitLab's Machine Learning platform provides comprehensive MLOps capabilities for enterprise AI development. This guide covers integration patterns, model lifecycle management, and deployment strategies for government and defense LLM applications.

GitLab ML Architecture​

Core Components​

Model Registry​

  • Centralized repository for managing ML models across projects
  • Semantic versioning with comprehensive metadata tracking
  • CI/CD integration for automated model deployment
  • MLflow compatibility for existing ML workflows

Experiment Tracking​

  • Experiment management for comparing model runs
  • Parameter and metric logging with automated collection
  • Artifact storage integrated with GitLab package registry
  • Performance visualization with built-in charts and comparisons

Integration Architecture​

graph TB
    A[Data Scientists] --> B[GitLab ML Experiments]
    B --> C[Model Registry]
    C --> D[CI/CD Pipeline]
    D --> E[Model Deployment]
    
    F[LLM Platform] --> G[MLflow Client]
    G --> B
    G --> C
    
    H[Drupal AI Module] --> I[Model API]
    I --> E
    
    J[Vector Database] --> K[Embeddings Pipeline]
    K --> D

Model Registry Implementation​

Basic Model Registration​

# Model registration with MLflow client
import mlflow
from mlflow.tracking import MlflowClient

# Configure GitLab ML backend
mlflow.set_tracking_uri("https://gitlab.company.gov/api/v4/projects/123/ml/mlflow")
mlflow.set_registry_uri("https://gitlab.company.gov/api/v4/projects/123/ml/mlflow")

client = MlflowClient()

# Register new model
model_name = "government-llm-classifier"
model_version = client.create_model_version(
    name=model_name,
    source="runs:/abc123/model",
    description="Classification model for government documents",
    tags={
        "classification": "official",
        "compliance": "fisma-moderate",
        "department": "defense"
    }
)

Enterprise Model Lifecycle Management​

# Advanced model lifecycle with governance
class EnterpriseModelManager:
    def __init__(self, gitlab_url: str, project_id: int, access_token: str):
        self.client = MlflowClient(
            tracking_uri=f"{gitlab_url}/api/v4/projects/{project_id}/ml/mlflow",
            registry_uri=f"{gitlab_url}/api/v4/projects/{project_id}/ml/mlflow"
        )
        self.project_id = project_id
        
    def register_model_with_governance(self, model_data: dict) -> ModelVersion:
        # Validate compliance requirements
        self.validate_compliance(model_data)
        
        # Create model with metadata
        model_version = self.client.create_model_version(
            name=model_data['name'],
            source=model_data['source'],
            description=model_data['description'],
            tags={
                **model_data.get('tags', {}),
                'registered_at': datetime.now().isoformat(),
                'compliance_validated': 'true',
                'security_scan': 'passed'
            }
        )
        
        # Set up approval workflow
        self.setup_approval_workflow(model_version)
        
        return model_version
    
    def validate_compliance(self, model_data: dict) -> bool:
        """Validate model against government compliance requirements"""
        required_tags = ['classification', 'compliance', 'department']
        
        for tag in required_tags:
            if tag not in model_data.get('tags', {}):
                raise ComplianceError(f"Missing required tag: {tag}")
        
        # Validate classification level
        classification = model_data['tags']['classification']
        if classification not in ['public', 'official', 'secret']:
            raise ComplianceError(f"Invalid classification: {classification}")
        
        return True

Experiment Tracking Integration​

LLM Training Experiment Tracking​

# Comprehensive LLM experiment tracking
import mlflow
import mlflow.pytorch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer

def train_government_llm():
    # Start MLflow experiment
    mlflow.set_experiment("government-llm-fine-tuning")
    
    with mlflow.start_run() as run:
        # Log training parameters
        training_params = {
            "model_name": "microsoft/DialoGPT-medium",
            "learning_rate": 1e-5,
            "batch_size": 16,
            "num_epochs": 3,
            "max_length": 512,
            "classification_level": "official",
            "compliance_framework": "fisma-moderate"
        }
        
        mlflow.log_params(training_params)
        
        # Load and configure model
        tokenizer = AutoTokenizer.from_pretrained(training_params["model_name"])
        model = AutoModelForCausalLM.from_pretrained(training_params["model_name"])
        
        # Training configuration
        training_args = TrainingArguments(
            output_dir="./results",
            learning_rate=training_params["learning_rate"],
            per_device_train_batch_size=training_params["batch_size"],
            num_train_epochs=training_params["num_epochs"],
            logging_steps=100,
            save_steps=500,
            evaluation_strategy="steps",
            eval_steps=500
        )
        
        # Custom trainer with MLflow logging
        trainer = MLflowTrainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            mlflow_run=run
        )
        
        # Train model
        trainer.train()
        
        # Log final metrics
        eval_results = trainer.evaluate()
        mlflow.log_metrics({
            "final_loss": eval_results["eval_loss"],
            "perplexity": eval_results["eval_perplexity"],
            "training_time": trainer.training_time
        })
        
        # Log model artifacts
        mlflow.pytorch.log_model(
            model,
            "model",
            registered_model_name="government-llm-v1",
            metadata={
                "compliance_validated": True,
                "security_scan_passed": True,
                "classification": "official"
            }
        )
        
        # Log additional artifacts
        mlflow.log_artifact("./training_logs.txt")
        mlflow.log_artifact("./compliance_report.pdf")
        
        return run.info.run_id

Custom MLflow Trainer for Government Compliance​

class MLflowTrainer(Trainer):
    def __init__(self, mlflow_run, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.mlflow_run = mlflow_run
        self.training_time = 0
        
    def log_metrics(self, logs, step):
        """Enhanced metrics logging with compliance tracking"""
        super().log_metrics(logs, step)
        
        # Log to MLflow with additional compliance metadata
        compliance_metrics = {
            f"compliance/{k}": v for k, v in logs.items()
        }
        
        mlflow.log_metrics(compliance_metrics, step=step)
        
        # Log security-relevant metrics
        if "eval_loss" in logs:
            security_score = self.calculate_security_score(logs["eval_loss"])
            mlflow.log_metric("security_score", security_score, step=step)
    
    def calculate_security_score(self, loss: float) -> float:
        """Calculate security score based on model performance"""
        # Lower loss generally indicates better model security
        # (less likely to generate inappropriate content)
        if loss < 0.5:
            return 0.95
        elif loss < 1.0:
            return 0.85
        elif loss < 2.0:
            return 0.75
        else:
            return 0.60

CI/CD Integration for Model Deployment​

GitLab CI Pipeline for Model Deployment​

# .gitlab-ci.yml for ML model deployment
stages:
  - validate
  - test
  - security-scan
  - deploy-staging
  - deploy-production

variables:
  MLFLOW_TRACKING_URI: "${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/ml/mlflow"
  MODEL_NAME: "government-llm-classifier"

validate_model:
  stage: validate
  script:
    - pip install mlflow boto3
    - python scripts/validate_model.py --model-name $MODEL_NAME
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"

test_model_performance:
  stage: test
  script:
    - python scripts/test_model_performance.py
    - python scripts/compliance_tests.py
  artifacts:
    reports:
      junit: test-results.xml
    paths:
      - compliance-report.pdf

security_scan:
  stage: security-scan
  script:
    - pip install bandit safety
    - bandit -r src/
    - safety check
    - python scripts/model_security_scan.py
  artifacts:
    reports:
      security: security-report.json

deploy_to_staging:
  stage: deploy-staging
  script:
    - python scripts/deploy_model.py --environment staging
  environment:
    name: staging
    url: https://ml-staging.agency.gov
  rules:
    - if: $CI_COMMIT_BRANCH == "main"

deploy_to_production:
  stage: deploy-production
  script:
    - python scripts/deploy_model.py --environment production
    - python scripts/notify_compliance_team.py
  environment:
    name: production
    url: https://ml.agency.gov
  rules:
    - if: $CI_COMMIT_TAG
  when: manual

Model Deployment Script​

# scripts/deploy_model.py
import argparse
import mlflow
from mlflow.tracking import MlflowClient
import requests
import os

class ModelDeploymentManager:
    def __init__(self, environment: str):
        self.environment = environment
        self.client = MlflowClient()
        self.deployment_config = self.load_deployment_config(environment)
        
    def deploy_model(self, model_name: str, model_version: str = "latest"):
        """Deploy model to specified environment with governance checks"""
        
        # Get model version details
        if model_version == "latest":
            model_version = self.get_latest_approved_version(model_name)
        
        model_version_details = self.client.get_model_version(
            name=model_name,
            version=model_version
        )
        
        # Validate deployment prerequisites
        self.validate_deployment_prerequisites(model_version_details)
        
        # Deploy model
        deployment_result = self.perform_deployment(model_version_details)
        
        # Log deployment event
        self.log_deployment_event(model_version_details, deployment_result)
        
        return deployment_result
    
    def validate_deployment_prerequisites(self, model_version):
        """Validate all prerequisites for model deployment"""
        
        # Check compliance tags
        tags = model_version.tags
        required_tags = ['compliance_validated', 'security_scan', 'classification']
        
        for tag in required_tags:
            if tag not in tags:
                raise DeploymentError(f"Missing required tag: {tag}")
        
        # Validate security scan results
        if tags.get('security_scan') != 'passed':
            raise DeploymentError("Security scan must pass before deployment")
        
        # Check classification compatibility with environment
        classification = tags.get('classification')
        if not self.is_classification_compatible(classification):
            raise DeploymentError(f"Classification {classification} not compatible with {self.environment}")
    
    def perform_deployment(self, model_version):
        """Perform actual model deployment"""
        
        # Download model artifacts
        model_path = mlflow.artifacts.download_artifacts(
            artifact_uri=model_version.source,
            dst_path="./deployment"
        )
        
        # Deploy to target environment
        if self.environment == "production":
            return self.deploy_to_production(model_path)
        elif self.environment == "staging":
            return self.deploy_to_staging(model_path)
        else:
            raise ValueError(f"Unknown environment: {self.environment}")
    
    def deploy_to_production(self, model_path: str):
        """Deploy model to production environment"""
        
        # Create deployment configuration
        deployment_config = {
            "model_path": model_path,
            "replicas": 3,
            "resources": {
                "cpu": "2",
                "memory": "4Gi"
            },
            "security": {
                "tls": True,
                "rbac": True,
                "network_policies": True
            }
        }
        
        # Deploy via Kubernetes API or Helm
        deployment_result = self.kubernetes_deploy(deployment_config)
        
        # Verify deployment
        self.verify_deployment_health(deployment_result)
        
        return deployment_result

Government Compliance Integration​

Compliance Tracking and Reporting​

# Compliance tracking for government ML projects
class GovernmentComplianceTracker:
    def __init__(self, project_id: int, compliance_framework: str = "fisma"):
        self.project_id = project_id
        self.compliance_framework = compliance_framework
        
    def track_experiment_compliance(self, experiment_id: str):
        """Track compliance for ML experiments"""
        
        experiment = mlflow.get_experiment(experiment_id)
        runs = mlflow.search_runs(experiment_ids=[experiment_id])
        
        compliance_report = {
            "experiment_id": experiment_id,
            "experiment_name": experiment.name,
            "compliance_framework": self.compliance_framework,
            "runs": []
        }
        
        for _, run in runs.iterrows():
            run_compliance = self.assess_run_compliance(run)
            compliance_report["runs"].append(run_compliance)
        
        # Generate compliance report
        self.generate_compliance_report(compliance_report)
        
        return compliance_report
    
    def assess_run_compliance(self, run) -> dict:
        """Assess compliance for individual experiment run"""
        
        compliance_checks = {
            "data_classification": self.check_data_classification(run),
            "audit_logging": self.check_audit_logging(run),
            "access_control": self.check_access_control(run),
            "encryption": self.check_encryption(run),
            "retention_policy": self.check_retention_policy(run)
        }
        
        overall_compliance = all(compliance_checks.values())
        
        return {
            "run_id": run["run_id"],
            "compliance_checks": compliance_checks,
            "overall_compliant": overall_compliance,
            "risk_level": self.calculate_risk_level(compliance_checks)
        }
    
    def generate_compliance_report(self, compliance_data: dict):
        """Generate formal compliance report"""
        
        report_template = f"""
        GOVERNMENT ML COMPLIANCE REPORT
        ==============================
        
        Experiment: {compliance_data['experiment_name']}
        Framework: {compliance_data['compliance_framework']}
        Generated: {datetime.now().isoformat()}
        
        COMPLIANCE SUMMARY:
        Total Runs: {len(compliance_data['runs'])}
        Compliant Runs: {sum(1 for r in compliance_data['runs'] if r['overall_compliant'])}
        Non-Compliant Runs: {sum(1 for r in compliance_data['runs'] if not r['overall_compliant'])}
        
        DETAILED FINDINGS:
        {self.format_detailed_findings(compliance_data['runs'])}
        """
        
        # Save report as artifact
        with open("compliance_report.txt", "w") as f:
            f.write(report_template)
        
        mlflow.log_artifact("compliance_report.txt")

Drupal Integration Patterns​

MLflow Integration with Drupal AI Module​

<?php
// Drupal service for GitLab ML integration
class GitLabMLService {
  
  protected $httpClient;
  protected $mlflowUrl;
  protected $accessToken;
  
  public function __construct(ClientInterface $http_client, ConfigFactoryInterface $config_factory) {
    $this->httpClient = $http_client;
    $config = $config_factory->get('gitlab_ml.settings');
    $this->mlflowUrl = $config->get('mlflow_url');
    $this->accessToken = $config->get('access_token');
  }
  
  public function getModelVersions(string $model_name): array {
    $url = $this->mlflowUrl . '/api/2.0/mlflow/model-versions/search';
    
    $response = $this->httpClient->request('GET', $url, [
      'headers' => [
        'Authorization' => 'Bearer ' . $this->accessToken,
        'Content-Type' => 'application/json'
      ],
      'query' => [
        'filter' => "name='{$model_name}'"
      ]
    ]);
    
    $data = json_decode($response->getBody(), true);
    return $data['model_versions'] ?? [];
  }
  
  public function deployModelVersion(string $model_name, string $version): bool {
    // Trigger GitLab CI pipeline for model deployment
    $pipeline_url = $this->getGitLabApiUrl() . '/trigger/pipeline';
    
    $response = $this->httpClient->request('POST', $pipeline_url, [
      'form_params' => [
        'token' => $this->getDeploymentToken(),
        'ref' => 'main',
        'variables[MODEL_NAME]' => $model_name,
        'variables[MODEL_VERSION]' => $version,
        'variables[DEPLOY_ENVIRONMENT]' => 'production'
      ]
    ]);
    
    return $response->getStatusCode() === 201;
  }
}

Best Practices for Government ML​

Security Best Practices​

  1. Model Encryption: Encrypt model artifacts at rest and in transit
  2. Access Control: Implement fine-grained RBAC for model access
  3. Audit Trails: Comprehensive logging of all model operations
  4. Compliance Validation: Automated compliance checking in CI/CD

Operational Best Practices​

  1. Version Control: Semantic versioning for all models
  2. Automated Testing: Comprehensive testing before deployment
  3. Rollback Procedures: Ability to quickly rollback problematic deployments
  4. Monitoring: Real-time monitoring of model performance and security

Governance Framework​

  1. Approval Workflows: Multi-stage approval for production deployments
  2. Risk Assessment: Automated risk scoring for model deployments
  3. Compliance Reporting: Regular compliance status reporting
  4. Incident Response: Defined procedures for security incidents

GitLab ML platform provides comprehensive MLOps capabilities ideal for government and defense AI applications, with strong emphasis on security, compliance, and auditability.