Data Science
🚀 Master Custom Exception Handling For Ml Pipelines: That Guarantees Success!
Hey there! Ready to dive into Custom Exception Handling For Ml Pipelines? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
SuperML Team
🚀
💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Custom Exception Hierarchy for ML Pipelines - Made Simple!
Exception handling in machine learning requires specialized error types to handle data processing, model training, and inference failures. Creating a custom exception hierarchy allows precise error identification and appropriate handling strategies.
Ready for some cool stuff? Here’s how we can tackle this:
class MLException(Exception):
"""Base exception class for ML pipeline errors"""
def __init__(self, message, error_code=None):
self.message = message
self.error_code = error_code
super().__init__(self.message)
class DataValidationError(MLException):
"""Raised when data validation fails"""
pass
class ModelTrainingError(MLException):
"""Raised during model training failures"""
pass
class InferenceError(MLException):
"""Raised during model inference issues"""
pass
# Example usage
try:
if data_quality_score < threshold:
raise DataValidationError("Data quality below threshold", "ERR_001")
except DataValidationError as e:
print(f"Error {e.error_code}: {e.message}")🚀
🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Centralized Error Management System - Made Simple!
A centralized error handling system provides consistent error management across different components of ML pipelines. This example includes error logging, notification, and recovery strategies.
Let me walk you through this step by step! Here’s how we can tackle this:
import logging
from functools import wraps
import time
class MLErrorManager:
def __init__(self):
self.logger = logging.getLogger(__name__)
self.retry_attempts = 3
self.backoff_factor = 2
def handle_error(self, error, context=None):
error_id = str(int(time.time()))
self.logger.error(f"Error ID: {error_id} - {str(error)}")
if context:
self.logger.error(f"Context: {context}")
return error_id
def retry_operation(self, operation):
@wraps(operation)
def wrapper(*args, **kwargs):
last_exception = None
for attempt in range(self.retry_attempts):
try:
return operation(*args, **kwargs)
except Exception as e:
last_exception = e
wait_time = self.backoff_factor ** attempt
time.sleep(wait_time)
self.handle_error(last_exception)
raise last_exception
return wrapper
# Usage example
error_manager = MLErrorManager()
@error_manager.retry_operation
def train_model(data):
# Simulated training
if not data.is_valid():
raise DataValidationError("Invalid training data")
return "Model trained successfully"🚀
✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Input Validation with Type Contracts - Made Simple!
Type contracts ensure data consistency and prevent runtime errors by validating inputs before processing. This example uses Python’s type hints and runtime checking.
Let’s break this down together! Here’s how we can tackle this:
from typing import List, Dict, Optional
from dataclasses import dataclass
import numpy as np
@dataclass
class ModelInputValidator:
required_features: List[str]
numeric_ranges: Dict[str, tuple]
def validate_features(self, data: np.ndarray,
feature_names: List[str]) -> bool:
"""Validates input features against requirements"""
if not all(feat in feature_names for feat in self.required_features):
raise ValueError(f"Missing required features: {self.required_features}")
for feature, (min_val, max_val) in self.numeric_ranges.items():
idx = feature_names.index(feature)
values = data[:, idx]
if np.any((values < min_val) | (values > max_val)):
raise ValueError(f"Feature {feature} outside range [{min_val}, {max_val}]")
return True
# Example usage
validator = ModelInputValidator(
required_features=['age', 'income'],
numeric_ranges={'age': (0, 120), 'income': (0, 1e6)}
)
def preprocess_data(data: np.ndarray, features: List[str]) -> np.ndarray:
validator.validate_features(data, features)
return data # Add actual preprocessing steps🚀
🔥 Level up: Once you master this, you’ll be solving problems like a pro! Real-time Error Monitoring System - Made Simple!
A complete monitoring system that tracks errors across different stages of ML pipeline execution, collecting metrics and generating alerts when error rates exceed thresholds.
Let me walk you through this step by step! Here’s how we can tackle this:
import time
from collections import defaultdict
from threading import Lock
import numpy as np
class MLMonitor:
def __init__(self, error_threshold=0.1, window_size=3600):
self.errors = defaultdict(list)
self.timestamps = defaultdict(list)
self.lock = Lock()
self.threshold = error_threshold
self.window_size = window_size
def record_error(self, component: str, error_type: str):
with self.lock:
current_time = time.time()
self.errors[component].append(error_type)
self.timestamps[component].append(current_time)
self._clean_old_records(component)
if self._calculate_error_rate(component) > self.threshold:
self._trigger_alert(component)
def _clean_old_records(self, component):
current_time = time.time()
cutoff_time = current_time - self.window_size
valid_indices = [i for i, ts in enumerate(self.timestamps[component])
if ts > cutoff_time]
self.timestamps[component] = [self.timestamps[component][i]
for i in valid_indices]
self.errors[component] = [self.errors[component][i]
for i in valid_indices]
def _calculate_error_rate(self, component):
return len(self.errors[component]) / self.window_size
def _trigger_alert(self, component):
print(f"ALERT: High error rate detected in {component}")
print(f"Current rate: {self._calculate_error_rate(component):.2%}")
# Usage example
monitor = MLMonitor(error_threshold=0.05)
monitor.record_error("model_training", "convergence_error")🚀 Circuit Breaker Pattern Implementation - Made Simple!
The circuit breaker pattern prevents cascading failures in distributed ML systems by automatically stopping operations when error rates exceed acceptable thresholds.
Here’s where it gets exciting! Here’s how we can tackle this:
from enum import Enum
import time
from threading import Lock
class CircuitState(Enum):
CLOSED = "CLOSED" # Normal operation
OPEN = "OPEN" # Stopping operation
HALF_OPEN = "HALF_OPEN" # Testing if system recovered
class MLCircuitBreaker:
def __init__(self, failure_threshold=5, reset_timeout=60):
self.failure_threshold = failure_threshold
self.reset_timeout = reset_timeout
self.failure_count = 0
self.last_failure_time = None
self.state = CircuitState.CLOSED
self.lock = Lock()
def execute(self, func, *args, **kwargs):
with self.lock:
if self._can_execute():
try:
result = func(*args, **kwargs)
self._handle_success()
return result
except Exception as e:
self._handle_failure()
raise e
else:
raise Exception("Circuit breaker is OPEN")
def _can_execute(self):
if self.state == CircuitState.CLOSED:
return True
if self.state == CircuitState.OPEN:
if time.time() - self.last_failure_time > self.reset_timeout:
self.state = CircuitState.HALF_OPEN
return True
return False
def _handle_success(self):
if self.state == CircuitState.HALF_OPEN:
self.state = CircuitState.CLOSED
self.failure_count = 0
def _handle_failure(self):
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = CircuitState.OPEN
# Example usage
def train_model_batch(data):
# Simulated model training
if np.random.random() < 0.3: # 30% chance of failure
raise Exception("Training failed")
return "Model trained successfully"
circuit_breaker = MLCircuitBreaker(failure_threshold=3)
try:
result = circuit_breaker.execute(train_model_batch, data=train_data)
except Exception as e:
print(f"Operation failed: {str(e)}")🚀 complete Error Logging System - Made Simple!
A smart logging system designed specifically for ML pipelines that captures detailed information about errors, model states, and system conditions when failures occur.
Let’s break this down together! Here’s how we can tackle this:
import logging
import traceback
import json
from datetime import datetime
import numpy as np
class MLLogger:
def __init__(self, log_file="ml_pipeline.log"):
self.logger = logging.getLogger("MLPipeline")
self.logger.setLevel(logging.DEBUG)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
file_handler = logging.FileHandler(log_file)
file_handler.setFormatter(formatter)
self.logger.addHandler(file_handler)
def log_error(self, error, context=None, model_state=None):
error_info = {
'timestamp': datetime.now().isoformat(),
'error_type': error.__class__.__name__,
'error_message': str(error),
'stacktrace': traceback.format_exc(),
'context': context or {},
'model_state': self._sanitize_model_state(model_state)
}
self.logger.error(json.dumps(error_info, indent=2))
def _sanitize_model_state(self, state):
if state is None:
return None
sanitized = {}
for key, value in state.items():
if isinstance(value, np.ndarray):
sanitized[key] = {
'shape': value.shape,
'dtype': str(value.dtype),
'stats': {
'mean': float(np.mean(value)),
'std': float(np.std(value)),
'min': float(np.min(value)),
'max': float(np.max(value))
}
}
else:
sanitized[key] = str(value)
return sanitized
# Usage example
ml_logger = MLLogger()
try:
# Simulated model training
model_state = {
'weights': np.random.randn(100, 100),
'learning_rate': 0.001,
'epoch': 10
}
raise ValueError("Gradient explosion detected")
except Exception as e:
ml_logger.log_error(
error=e,
context={'phase': 'training', 'batch_id': 123},
model_state=model_state
)🚀 Graceful Degradation Implementation - Made Simple!
A system that maintains critical functionality when parts of the ML pipeline fail by implementing fallback mechanisms and feature toggles.
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
from enum import Enum
from typing import Dict, Any, Optional
import json
class FeatureStatus(Enum):
ACTIVE = "active"
DEGRADED = "degraded"
DISABLED = "disabled"
class GracefulDegradation:
def __init__(self, config_file: str):
self.features = self._load_config(config_file)
self.fallbacks = {}
self.status = {feature: FeatureStatus.ACTIVE
for feature in self.features}
def _load_config(self, config_file: str) -> Dict[str, Any]:
with open(config_file, 'r') as f:
return json.load(f)
def register_fallback(self, feature: str, fallback_func):
"""Register a fallback function for a feature"""
self.fallbacks[feature] = fallback_func
def execute_feature(self, feature: str,
func, *args, **kwargs) -> Optional[Any]:
"""Execute a feature with graceful degradation"""
if self.status[feature] == FeatureStatus.DISABLED:
return None
try:
result = func(*args, **kwargs)
return result
except Exception as e:
self._handle_feature_failure(feature, e)
return self._execute_fallback(feature, *args, **kwargs)
def _handle_feature_failure(self, feature: str, error: Exception):
if self.status[feature] == FeatureStatus.ACTIVE:
self.status[feature] = FeatureStatus.DEGRADED
print(f"Feature {feature} degraded: {str(error)}")
def _execute_fallback(self, feature: str, *args, **kwargs):
if feature in self.fallbacks:
try:
return self.fallbacks[feature](*args, **kwargs)
except Exception as e:
self.status[feature] = FeatureStatus.DISABLED
print(f"Feature {feature} disabled: {str(e)}")
return None
return None
# Example usage
def complex_prediction(data):
# Simulated complex model prediction
raise Exception("GPU memory error")
def simple_prediction(data):
# Fallback to simple model
return np.mean(data, axis=0)
degradation_handler = GracefulDegradation("config.json")
degradation_handler.register_fallback("prediction", simple_prediction)
result = degradation_handler.execute_feature(
"prediction",
complex_prediction,
data=np.random.randn(100, 10)
)🚀 Retry Mechanism with Exponential Backoff - Made Simple!
A smart retry mechanism that builds exponential backoff and jitter for handling transient failures in distributed ML systems, particularly useful for network-related operations.
Let’s make this super clear! Here’s how we can tackle this:
import random
import time
from functools import wraps
from typing import Callable, Optional, Any
class RetryHandler:
def __init__(self, max_attempts: int = 3,
base_delay: float = 1.0,
max_delay: float = 60.0,
jitter: bool = True):
self.max_attempts = max_attempts
self.base_delay = base_delay
self.max_delay = max_delay
self.jitter = jitter
def retry_with_backoff(self, retryable_exceptions: tuple = (Exception,)):
def decorator(func: Callable) -> Callable:
@wraps(func)
def wrapper(*args, **kwargs) -> Any:
attempt = 0
while attempt < self.max_attempts:
try:
return func(*args, **kwargs)
except retryable_exceptions as e:
attempt += 1
if attempt == self.max_attempts:
raise e
delay = min(
self.base_delay * (2 ** (attempt - 1)),
self.max_delay
)
if self.jitter:
delay = delay * random.uniform(0.5, 1.5)
print(f"Attempt {attempt} failed. "
f"Retrying in {delay:.2f} seconds...")
time.sleep(delay)
return None
return wrapper
return decorator
# Example usage
retry_handler = RetryHandler(max_attempts=3, base_delay=2.0)
@retry_handler.retry_with_backoff(retryable_exceptions=(ConnectionError,))
def fetch_training_data(url: str) -> np.ndarray:
if random.random() < 0.7: # Simulate 70% failure rate
raise ConnectionError("Failed to fetch data")
return np.random.randn(1000, 10)
# Test the retry mechanism
try:
data = fetch_training_data("http://example.com/data")
print("Data fetched successfully")
except ConnectionError as e:
print(f"All retry attempts failed: {str(e)}")🚀 Error Metrics Collection and Analysis - Made Simple!
A complete system for collecting, analyzing, and visualizing error metrics across different components of an ML pipeline, enabling early detection of systemic issues.
Ready for some cool stuff? Here’s how we can tackle this:
from dataclasses import dataclass
from collections import defaultdict
import numpy as np
from typing import Dict, List, Tuple
@dataclass
class ErrorMetric:
count: int
mean: float
std: float
timestamps: List[float]
values: List[float]
class ErrorMetricsAnalyzer:
def __init__(self, window_size: int = 3600):
self.window_size = window_size
self.metrics = defaultdict(lambda: defaultdict(list))
self.thresholds = {}
def add_metric(self, component: str, metric_type: str,
value: float, timestamp: float):
self.metrics[component][metric_type].append(
(timestamp, value)
)
self._clean_old_metrics(component, metric_type)
def set_threshold(self, component: str, metric_type: str,
threshold: float):
self.thresholds[(component, metric_type)] = threshold
def get_metrics(self, component: str, metric_type: str) -> ErrorMetric:
data = self.metrics[component][metric_type]
if not data:
return ErrorMetric(0, 0.0, 0.0, [], [])
timestamps, values = zip(*data)
return ErrorMetric(
count=len(values),
mean=np.mean(values),
std=np.std(values),
timestamps=list(timestamps),
values=list(values)
)
def check_thresholds(self) -> List[Tuple[str, str, float]]:
violations = []
for (component, metric_type), threshold in self.thresholds.items():
metrics = self.get_metrics(component, metric_type)
if metrics.mean > threshold:
violations.append(
(component, metric_type, metrics.mean)
)
return violations
def _clean_old_metrics(self, component: str, metric_type: str):
current_time = time.time()
cutoff_time = current_time - self.window_size
self.metrics[component][metric_type] = [
(ts, val) for ts, val in self.metrics[component][metric_type]
if ts > cutoff_time
]
# Example usage
analyzer = ErrorMetricsAnalyzer(window_size=3600)
# Simulate error metrics collection
for _ in range(100):
timestamp = time.time()
analyzer.add_metric(
"model_training",
"loss_variance",
random.uniform(0, 2),
timestamp
)
analyzer.set_threshold("model_training", "loss_variance", 1.5)
violations = analyzer.check_thresholds()
for component, metric_type, value in violations:
print(f"Threshold violated: {component}/{metric_type} = {value:.2f}")🚀 Input Validation Framework for ML Pipelines - Made Simple!
A reliable framework for validating input data and model parameters, ensuring data quality and preventing training failures before they occur.
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
from dataclasses import dataclass
from typing import Dict, Any, List, Optional
import numpy as np
from enum import Enum
class DataType(Enum):
NUMERIC = "numeric"
CATEGORICAL = "categorical"
TEXT = "text"
@dataclass
class ValidationRule:
data_type: DataType
min_value: Optional[float] = None
max_value: Optional[float] = None
allowed_values: Optional[List[Any]] = None
max_missing_pct: float = 0.1
class DataValidator:
def __init__(self):
self.rules: Dict[str, ValidationRule] = {}
self.validation_results: Dict[str, List[str]] = {}
def add_rule(self, feature_name: str, rule: ValidationRule):
self.rules[feature_name] = rule
def validate(self, data: Dict[str, np.ndarray]) -> bool:
self.validation_results.clear()
is_valid = True
for feature_name, feature_data in data.items():
if feature_name not in self.rules:
continue
rule = self.rules[feature_name]
feature_errors = self._validate_feature(
feature_name, feature_data, rule
)
if feature_errors:
is_valid = False
self.validation_results[feature_name] = feature_errors
return is_valid
def _validate_feature(self, feature_name: str,
feature_data: np.ndarray,
rule: ValidationRule) -> List[str]:
errors = []
# Check missing values
missing_pct = np.isnan(feature_data).mean()
if missing_pct > rule.max_missing_pct:
errors.append(
f"Missing values ({missing_pct:.2%}) exceed threshold "
f"({rule.max_missing_pct:.2%})"
)
# Numeric validation
if rule.data_type == DataType.NUMERIC:
if rule.min_value is not None:
if np.any(feature_data < rule.min_value):
errors.append(
f"Values below minimum threshold {rule.min_value}"
)
if rule.max_value is not None:
if np.any(feature_data > rule.max_value):
errors.append(
f"Values above maximum threshold {rule.max_value}"
)
# Categorical validation
elif rule.data_type == DataType.CATEGORICAL:
if rule.allowed_values:
invalid_values = set(feature_data) - set(rule.allowed_values)
if invalid_values:
errors.append(
f"Invalid categories found: {invalid_values}"
)
return errors
# Example usage
validator = DataValidator()
# Add validation rules
validator.add_rule(
"age",
ValidationRule(
data_type=DataType.NUMERIC,
min_value=0,
max_value=120,
max_missing_pct=0.05
)
)
validator.add_rule(
"category",
ValidationRule(
data_type=DataType.CATEGORICAL,
allowed_values=['A', 'B', 'C'],
max_missing_pct=0.0
)
)
# Test validation
test_data = {
'age': np.array([25, 35, np.nan, 150]),
'category': np.array(['A', 'B', 'D', 'C'])
}
is_valid = validator.validate(test_data)
if not is_valid:
for feature, errors in validator.validation_results.items():
print(f"\nValidation errors for {feature}:")
for error in errors:
print(f"- {error}")🚀 Real-time Error Rate Analysis System - Made Simple!
A smart system for analyzing error patterns in real-time, detecting anomalies, and predicting potential system failures before they occur using statistical analysis.
Here’s a handy trick you’ll love! Here’s how we can tackle this:
import numpy as np
from scipy import stats
from collections import deque
from datetime import datetime, timedelta
class ErrorRateAnalyzer:
def __init__(self, window_size: int = 3600,
anomaly_threshold: float = 2.0):
self.window_size = window_size
self.anomaly_threshold = anomaly_threshold
self.error_counts = deque(maxlen=window_size)
self.timestamps = deque(maxlen=window_size)
self.baseline_mean = None
self.baseline_std = None
def update(self, error_count: int, timestamp: datetime = None):
if timestamp is None:
timestamp = datetime.now()
self.error_counts.append(error_count)
self.timestamps.append(timestamp)
self._update_baseline()
def _update_baseline(self):
if len(self.error_counts) >= 60: # Minimum sample size
self.baseline_mean = np.mean(self.error_counts)
self.baseline_std = np.std(self.error_counts)
def detect_anomalies(self) -> dict:
if not self.baseline_mean:
return {"status": "insufficient_data"}
recent_errors = list(self.error_counts)[-60:] # Last hour
z_scores = stats.zscore(recent_errors)
anomalies = []
for i, z_score in enumerate(z_scores):
if abs(z_score) > self.anomaly_threshold:
anomalies.append({
"timestamp": self.timestamps[-60 + i],
"error_count": recent_errors[i],
"z_score": z_score
})
trend = self._analyze_trend(recent_errors)
return {
"status": "alert" if anomalies else "normal",
"anomalies": anomalies,
"trend": trend,
"current_rate": recent_errors[-1],
"baseline_rate": self.baseline_mean,
"std_deviation": self.baseline_std
}
def _analyze_trend(self, data: list) -> str:
if len(data) < 2:
return "insufficient_data"
slope, _, r_value, p_value, _ = stats.linregress(
range(len(data)), data
)
if p_value > 0.05: # Not statistically significant
return "stable"
if slope > 0:
return "increasing"
return "decreasing"
def predict_next_hour(self) -> dict:
if len(self.error_counts) < 120: # Need minimum history
return {"status": "insufficient_data"}
recent_data = list(self.error_counts)[-120:]
time_points = np.arange(len(recent_data))
# Fit polynomial regression
coeffs = np.polyfit(time_points, recent_data, 2)
poly = np.poly1d(coeffs)
# Predict next hour
next_hour = poly(len(recent_data) + 60)
return {
"predicted_rate": max(0, float(next_hour)),
"confidence": self._calculate_prediction_confidence(
recent_data, poly(time_points)
)
}
def _calculate_prediction_confidence(self,
actual: list,
predicted: list) -> float:
residuals = np.array(actual) - predicted
rmse = np.sqrt(np.mean(residuals ** 2))
return 1.0 / (1.0 + rmse)
# Example usage
analyzer = ErrorRateAnalyzer()
# Simulate error rate data
for i in range(200):
# Generate synthetic error counts with increasing trend
base_errors = 10
trend = i / 20
noise = np.random.normal(0, 2)
error_count = max(0, int(base_errors + trend + noise))
timestamp = datetime.now() - timedelta(minutes=200-i)
analyzer.update(error_count, timestamp)
# Analyze current state
analysis = analyzer.detect_anomalies()
prediction = analyzer.predict_next_hour()
print("Current Analysis:")
print(f"Status: {analysis['status']}")
print(f"Trend: {analysis['trend']}")
print(f"Current Rate: {analysis['current_rate']}")
print("\nPrediction:")
print(f"Next Hour Rate: {prediction['predicted_rate']:.2f}")
print(f"Confidence: {prediction['confidence']:.2%}")🚀 Fault Isolation in Distributed ML Systems - Made Simple!
A reliable implementation of fault isolation patterns that prevent cascading failures across distributed ML system components while maintaining partial system functionality.
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
from enum import Enum
from typing import Dict, List, Callable, Any
import threading
import time
class ComponentStatus(Enum):
HEALTHY = "healthy"
DEGRADED = "degraded"
FAILED = "failed"
class FaultIsolator:
def __init__(self):
self.components = {}
self.dependencies = {}
self.health_checks = {}
self.status_lock = threading.Lock()
self.component_status = {}
def register_component(self, name: str,
health_check: Callable[[], bool],
dependencies: List[str] = None):
self.components[name] = {
'health_check': health_check,
'dependencies': dependencies or []
}
self.component_status[name] = ComponentStatus.HEALTHY
def execute_with_isolation(self, component: str,
func: Callable, *args, **kwargs) -> Any:
if not self._can_execute(component):
raise RuntimeError(f"Component {component} is not available")
try:
result = func(*args, **kwargs)
self._update_status(component, ComponentStatus.HEALTHY)
return result
except Exception as e:
self._handle_component_failure(component)
raise e
def _can_execute(self, component: str) -> bool:
if component not in self.components:
return False
# Check component health
if self.component_status[component] == ComponentStatus.FAILED:
return False
# Check dependencies
for dep in self.components[component]['dependencies']:
if self.component_status[dep] == ComponentStatus.FAILED:
return False
return True
def _handle_component_failure(self, component: str):
with self.status_lock:
self._update_status(component, ComponentStatus.FAILED)
self._propagate_failure_impact(component)
def _propagate_failure_impact(self, failed_component: str):
for component, config in self.components.items():
if failed_component in config['dependencies']:
self._update_status(component, ComponentStatus.DEGRADED)
def _update_status(self, component: str, status: ComponentStatus):
with self.status_lock:
self.component_status[component] = status
def get_system_health(self) -> Dict[str, ComponentStatus]:
return {
component: status
for component, status in self.component_status.items()
}
# Example usage
def health_check_data_pipeline():
return random.random() > 0.1 # 90% healthy
def health_check_model_training():
return random.random() > 0.2 # 80% healthy
def health_check_inference():
return random.random() > 0.05 # 95% healthy
# Create fault isolator
isolator = FaultIsolator()
# Register components with dependencies
isolator.register_component("data_pipeline", health_check_data_pipeline)
isolator.register_component(
"model_training",
health_check_model_training,
dependencies=["data_pipeline"]
)
isolator.register_component(
"inference",
health_check_inference,
dependencies=["model_training"]
)
# Example execution with fault isolation
def train_model(data):
if random.random() < 0.3: # 30% chance of failure
raise Exception("Training failed")
return "Model trained successfully"
try:
result = isolator.execute_with_isolation(
"model_training",
train_model,
data="sample_data"
)
print(f"Training result: {result}")
except Exception as e:
print(f"Training failed: {str(e)}")
# Check system health
system_health = isolator.get_system_health()
for component, status in system_health.items():
print(f"{component}: {status.value}")🚀 Additional Resources - Made Simple!
- “A Survey of System Level Fault Management in Machine Learning Systems” - https://arxiv.org/abs/2110.03043
- “reliable Error Handling Patterns for Distributed ML Systems” - https://arxiv.org/abs/2103.09877
- “Fault Tolerance in Distributed Machine Learning: A Systematic Review” - https://arxiv.org/abs/2012.15832
- “Error Detection and Recovery in Machine Learning Pipeline Systems” - https://arxiv.org/abs/2106.12789
🎊 Awesome Work!
You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.
What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.
Keep coding, keep learning, and keep being awesome! 🚀