Ml Pipeline Workflow

Ml Pipeline Workflow

Orchestrates intelligent skill selection and execution for ml pipeline workflow workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def select_ml_pipeline_step(
    task_spec: Dict,
    available_steps: List[Dict],
    hardware_constraints: Dict
) -> Optional[Dict]:
    """Select optimal ML pipeline step based on task type, data schema, and hardware.
    
    Evaluates steps using multi-factor scoring:
    - Task compatibility (classification vs regression vs clustering)
    - Data schema alignment (feature types, dimensionality)
    - Hardware fit (GPU memory, CPU cores, storage I/O)
    - Historical performance on similar datasets
    
    Args:
        task_spec: ML task configuration (type, data_path, target_column)
        available_steps: List of ML step metadata (framework, algorithm, resource_req)
        hardware_constraints: Current system resources (gpu_mem_gb, cpu_cores)
        
    Returns:
        Selected step metadata or None if no compatible step exists
    """
    # Guard clause - Early Exit (Law 1)
    if not task_spec.get("task_type") or not available_steps:
        raise ValueError("Task type and available steps are required")
        
    best_step = None
    best_score = 0.0
    
    for step in available_steps:
        # Check hardware compatibility first (Make Illegal States Unrepresentable - Law 2)
        if step["resource_req"]["gpu_mem_gb"] > hardware_constraints.get("gpu_mem_gb", 0):
            continue
            
        # Calculate compatibility score
        task_match = _match_task_type(task_spec["task_type"], step["supported_tasks"])
        schema_match = _validate_schema(task_spec.get("features", []), step["required_schema"])
        perf_history = step.get("historical_rmse", 1.0) if task_spec["task_type"] == "regression" else 1.0 - step.get("historical_accuracy", 0.0)
        
        score = (task_match * 0.4) + (schema_match * 0.3) + (perf_history * 0.3)
        
        if score > best_score:
            best_score = score
            best_step = step
            
    if best_step is None:
        return None
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    return {
        **best_step,
        "selection_score": best_score,
        "selected_at": datetime.utcnow().isoformat(),
        "hardware_profile": hardware_constraints
    }

Pattern 2: Execution with Fallback

def execute_ml_step_with_fallback(
    step_config: Dict,
    run_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute ML pipeline step with resilience patterns for training/evaluation.
    
    Implements ML-specific fallback chain:
    1. Retry with original hyperparameters
    2. Fallback to simplified model architecture (e.g., linear vs neural net)
    3. Use cached/precomputed features if training fails
    4. Defer to human review for critical model validation failures
    
    Args:
        step_config: ML step configuration (algorithm, hyperparams, data_path)
        run_context: Execution context (session_id, output_dir, metrics_tracker)
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with model artifacts, metrics, and fallback metadata
    """
    # Guard clause - validate config (Early Exit)
    if not step_config.get("algorithm"):
        raise ValueError("Algorithm specification is required for execution")
        
    validated_config = _normalize_ml_config(step_config)
    output_dir = run_context.get("output_dir", "/tmp/ml_run")
    
    for attempt in range(max_retries + 1):
        try:
            # Execute training/evaluation pipeline
            artifacts, metrics = _run_ml_pipeline(validated_config, output_dir)
            
            # Validate model quality (Fail Fast on poor performance - Law 4)
            if metrics.get("validation_loss", float('inf')) > run_context.get("max_acceptable_loss", 1.0):
                raise ModelQualityError(f"Validation loss {metrics['validation_loss']} exceeds threshold")
                
            return {
                "success": True,
                "step_executed": validated_config["algorithm"],
                "artifacts_path": artifacts,
                "metrics": metrics,
                "attempts": attempt + 1,
                "latency_sec": time.time() - run_context.get("start_time", time.time())
            }
            
        except OutOfMemoryError as e:
            # Fallback: Reduce batch size or switch to CPU
            if attempt < max_retries:
                validated_config["batch_size"] = max(1, validated_config.get("batch_size", 32) // 2)
                continue
            return _apply_ml_fallback(validated_config, run_context, "memory_overflow")
            
        except ConvergenceError as e:
            # Fallback: Adjust learning rate or switch algorithm
            if attempt < max_retries:
                validated_config["learning_rate"] *= 0.5
                continue
            return _apply_ml_fallback(validated_config, run_context, "convergence_failure")
            
    # All retries exhausted - Fail Loud (Law 4)
    raise PipelineExecutionError(f"ML step {validated_config['algorithm']} failed after {max_retries + 1} attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

| Skill | Purpose | |

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• MLflow Experiment Tracking and Pipeline Orchestration
• Apache Airflow ML Pipeline Guide
• Kubeflow ML Pipeline on Kubernetes
• DVC (Data Version Control) for ML Pipelines
• Scikit-learn Pipeline API