Data Science
🚀 Proven Modular Ml Pipeline Architecture In 3 Phases: That Will Supercharge Expert!
Hey there! Ready to dive into Modular Ml Pipeline Architecture In 3 Phases? 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! Feature Engineering Pipeline Architecture - Made Simple!
Feature engineering serves as the foundation of any reliable machine learning system. This phase handles data preparation, cleaning, and transformation while maintaining consistency across training and inference. A well-structured feature pipeline ensures reproducibility and efficient feature reuse.
Let’s break this down together! Here’s how we can tackle this:
from typing import Dict, List
import pandas as pd
import numpy as np
class FeaturePipeline:
def __init__(self):
self.feature_store = {}
self.transformers = {}
def add_transformer(self, name: str, transform_fn):
"""Register a new feature transformer"""
self.transformers[name] = transform_fn
def process_features(self, data: pd.DataFrame) -> Dict[str, np.ndarray]:
"""Process raw data through transformation pipeline"""
features = {}
for name, transformer in self.transformers.items():
features[name] = transformer(data)
self.feature_store[name] = features[name]
return features
# Example Usage
pipeline = FeaturePipeline()
# Add custom transformers
pipeline.add_transformer(
"numeric_scaler",
lambda df: (df.select_dtypes(include=[np.number]) - df.mean()) / df.std()
)
# Process features
raw_data = pd.read_csv("data.csv")
features = pipeline.process_features(raw_data)🚀
🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Feature Store Implementation - Made Simple!
The feature store acts as a centralized repository for computed features, ensuring consistency between training and inference. This example provides versioning, caching, and retrieval mechanisms for efficient feature management.
This next part is really neat! Here’s how we can tackle this:
import joblib
from datetime import datetime
from pathlib import Path
class FeatureStore:
def __init__(self, storage_path: str = "feature_store"):
self.storage_path = Path(storage_path)
self.storage_path.mkdir(exist_ok=True)
self.feature_metadata = {}
def save_feature(self, name: str, feature_data: np.ndarray):
"""Save feature with versioning"""
version = datetime.now().strftime("%Y%m%d_%H%M%S")
feature_path = self.storage_path / f"{name}_v{version}.joblib"
joblib.dump(feature_data, feature_path)
self.feature_metadata[name] = {
'version': version,
'path': str(feature_path),
'shape': feature_data.shape
}
def load_feature(self, name: str, version: str = 'latest'):
"""Load feature from store"""
if version == 'latest':
version = self.feature_metadata[name]['version']
feature_path = self.storage_path / f"{name}_v{version}.joblib"
return joblib.load(feature_path)
# Example Usage
store = FeatureStore()
features = np.random.randn(1000, 10)
store.save_feature("customer_embeddings", features)
loaded_features = store.load_feature("customer_embeddings")🚀
✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Training Pipeline Architecture - Made Simple!
The training pipeline orchestrates model development through automated stages including data splitting, model initialization, training loops, and validation. This example emphasizes reproducibility and proper experiment tracking.
Let’s make this super clear! Here’s how we can tackle this:
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import mlflow
class TrainingPipeline:
def __init__(self, model, feature_store):
self.model = model
self.feature_store = feature_store
mlflow.set_experiment("ml_training")
def train(self, features: Dict[str, np.ndarray], labels: np.ndarray,
test_size: float = 0.2):
"""Execute training pipeline with experiment tracking"""
with mlflow.start_run():
# Log parameters
mlflow.log_params({
"model_type": type(self.model).__name__,
"test_size": test_size
})
# Split data
X = np.hstack(list(features.values()))
X_train, X_test, y_train, y_test = train_test_split(
X, labels, test_size=test_size
)
# Train and evaluate
self.model.fit(X_train, y_train)
y_pred = self.model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
# Log metrics
mlflow.log_metric("accuracy", accuracy)
mlflow.sklearn.log_model(self.model, "model")
return accuracy
# Example Usage
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
pipeline = TrainingPipeline(model, store)
accuracy = pipeline.train(features, labels)🚀
🔥 Level up: Once you master this, you’ll be solving problems like a pro! Model Registry Implementation - Made Simple!
The model registry maintains versions of trained models, facilitating deployment and rollback capabilities. This example provides model metadata tracking and standardized serialization formats.
Let’s break this down together! Here’s how we can tackle this:
from typing import Optional
import json
from datetime import datetime
class ModelRegistry:
def __init__(self, registry_path: str = "model_registry"):
self.registry_path = Path(registry_path)
self.registry_path.mkdir(exist_ok=True)
self.metadata_file = self.registry_path / "metadata.json"
self.load_metadata()
def load_metadata(self):
"""Load existing model metadata"""
if self.metadata_file.exists():
with open(self.metadata_file, 'r') as f:
self.metadata = json.load(f)
else:
self.metadata = {}
def register_model(self, model, name: str,
metrics: Dict[str, float],
production: bool = False):
"""Register new model version"""
version = datetime.now().strftime("%Y%m%d_%H%M%S")
model_path = self.registry_path / f"{name}_v{version}.joblib"
# Save model and metadata
joblib.dump(model, model_path)
self.metadata[name] = {
'version': version,
'path': str(model_path),
'metrics': metrics,
'production': production
}
with open(self.metadata_file, 'w') as f:
json.dump(self.metadata, f, indent=2)
def load_model(self, name: str, version: Optional[str] = None):
"""Load model from registry"""
if version is None:
version = self.metadata[name]['version']
model_path = self.registry_path / f"{name}_v{version}.joblib"
return joblib.load(model_path)
# Example Usage
registry = ModelRegistry()
registry.register_model(
model,
"customer_classifier",
metrics={"accuracy": accuracy},
production=True
)🚀 Inference Pipeline Architecture - Made Simple!
The inference pipeline handles real-time and batch predictions while ensuring consistent feature processing. This example includes prediction caching, monitoring, and automated performance tracking to maintain reliability at scale.
Let me walk you through this step by step! Here’s how we can tackle this:
import redis
from typing import Union, List
import numpy as np
class InferencePipeline:
def __init__(self, model_registry, feature_store):
self.model_registry = model_registry
self.feature_store = feature_store
self.cache = redis.Redis(host='localhost', port=6379, db=0)
self.prediction_metrics = []
def predict(self, input_data: Union[pd.DataFrame, List[Dict]],
model_name: str) -> np.ndarray:
"""Execute inference pipeline with caching and monitoring"""
# Generate cache key
cache_key = f"{model_name}_{hash(str(input_data))}"
# Check cache
if self.cache.exists(cache_key):
return np.frombuffer(self.cache.get(cache_key))
# Load model and process features
model = self.model_registry.load_model(model_name)
# Transform input data to DataFrame if needed
if isinstance(input_data, list):
input_data = pd.DataFrame(input_data)
# Process features consistently
features = {}
for feature_name in self.feature_store.feature_metadata.keys():
transformer = self.feature_store.load_transformer(feature_name)
features[feature_name] = transformer(input_data)
# Make prediction
X = np.hstack(list(features.values()))
prediction = model.predict(X)
# Cache result
self.cache.set(cache_key, prediction.tobytes())
# Log metrics
self.prediction_metrics.append({
'timestamp': datetime.now(),
'model': model_name,
'input_shape': X.shape,
'latency': time.time() - start_time
})
return prediction
# Example Usage
inference = InferencePipeline(registry, store)
new_data = pd.read_csv("new_customers.csv")
predictions = inference.predict(new_data, "customer_classifier")🚀 Automated Model Retraining System - Made Simple!
This example creates an automated system for detecting model drift and triggering retraining pipelines when performance degrades. It integrates monitoring metrics with automated decision making for maintenance.
Let me walk you through this step by step! Here’s how we can tackle this:
from sklearn.metrics import mean_squared_error
import schedule
import time
class ModelMonitor:
def __init__(self, inference_pipeline, training_pipeline,
drift_threshold: float = 0.1):
self.inference = inference_pipeline
self.training = training_pipeline
self.drift_threshold = drift_threshold
self.performance_history = []
def calculate_drift(self, ground_truth: np.ndarray,
predictions: np.ndarray) -> float:
"""Calculate model drift based on recent performance"""
current_error = mean_squared_error(ground_truth, predictions)
if self.performance_history:
baseline_error = np.mean(self.performance_history[-10:])
drift = abs(current_error - baseline_error) / baseline_error
return drift
return 0.0
def check_and_retrain(self, new_data: pd.DataFrame,
ground_truth: np.ndarray):
"""Monitor performance and trigger retraining if needed"""
predictions = self.inference.predict(new_data, "production_model")
drift = self.calculate_drift(ground_truth, predictions)
if drift > self.drift_threshold:
print(f"Drift detected: {drift:.2f}. Initiating retraining...")
# Retrain model with updated data
features = self.feature_store.process_features(new_data)
accuracy = self.training.train(features, ground_truth)
# Update production model if improved
if accuracy > self.current_best_accuracy:
self.model_registry.register_model(
self.training.model,
"production_model",
metrics={"accuracy": accuracy},
production=True
)
print(f"New model deployed with accuracy: {accuracy:.2f}")
# Example Usage
monitor = ModelMonitor(inference, training_pipeline)
schedule.every().hour.do(
monitor.check_and_retrain,
new_data=get_latest_data(),
ground_truth=get_latest_labels()
)
while True:
schedule.run_pending()
time.sleep(60)🚀 Feature Cross-Validation System - Made Simple!
A reliable system for validating feature quality and stability across different data distributions. This example helps identify unreliable features and ensures consistency between training and inference.
Let me walk you through this step by step! Here’s how we can tackle this:
from sklearn.model_selection import KFold
from scipy import stats
class FeatureValidator:
def __init__(self, n_splits: int = 5, stability_threshold: float = 0.05):
self.n_splits = n_splits
self.stability_threshold = stability_threshold
self.validation_results = {}
def validate_feature_stability(self,
feature_data: np.ndarray,
feature_name: str):
"""Validate feature stability across different data splits"""
kf = KFold(n_splits=self.n_splits, shuffle=True)
distributions = []
for train_idx, val_idx in kf.split(feature_data):
train_dist = feature_data[train_idx]
val_dist = feature_data[val_idx]
# Perform Kolmogorov-Smirnov test
ks_statistic, p_value = stats.ks_2samp(train_dist, val_dist)
distributions.append({
'ks_statistic': ks_statistic,
'p_value': p_value
})
# Analyze stability
avg_p_value = np.mean([d['p_value'] for d in distributions])
is_stable = avg_p_value > self.stability_threshold
self.validation_results[feature_name] = {
'is_stable': is_stable,
'avg_p_value': avg_p_value,
'distributions': distributions
}
return is_stable
# Example Usage
validator = FeatureValidator()
features = feature_store.load_feature("customer_embeddings")
is_stable = validator.validate_feature_stability(
features,
"customer_embeddings"
)
if not is_stable:
print(f"Warning: Feature 'customer_embeddings' shows instability")
print(f"Average p-value: {validator.validation_results['customer_embeddings']['avg_p_value']:.4f}")🚀 Distributed Feature Processing - Made Simple!
Implementing distributed feature processing using PySpark for handling large-scale datasets smartly. This system lets you parallel feature computation while maintaining data consistency across multiple nodes.
Ready for some cool stuff? Here’s how we can tackle this:
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import *
import numpy as np
class DistributedFeatureProcessor:
def __init__(self, app_name: str = "distributed_features"):
self.spark = SparkSession.builder \
.appName(app_name) \
.config("spark.executor.memory", "4g") \
.getOrCreate()
def register_feature_udf(self, feature_name: str, feature_fn):
"""Register feature computation as Spark UDF"""
return_type = ArrayType(DoubleType())
@udf(returnType=return_type)
def feature_udf(*cols):
return feature_fn(*cols).tolist()
self.spark.udf.register(f"feature_{feature_name}", feature_udf)
return feature_udf
def compute_features(self, data_path: str, feature_configs: dict):
"""Distributed feature computation"""
df = self.spark.read.parquet(data_path)
for feature_name, config in feature_configs.items():
input_cols = config['input_columns']
feature_fn = config['function']
# Register and apply transformation
feature_udf = self.register_feature_udf(
feature_name,
feature_fn
)
df = df.withColumn(
feature_name,
feature_udf(*input_cols)
)
return df
# Example Usage
def compute_embeddings(text_col):
# Simplified embedding computation
return np.random.randn(100)
processor = DistributedFeatureProcessor()
feature_configs = {
'text_embeddings': {
'input_columns': ['text_column'],
'function': compute_embeddings
}
}
features_df = processor.compute_features(
"s3://data/customers.parquet",
feature_configs
)🚀 Real-time Feature Service - Made Simple!
Implementation of a high-performance feature serving system for real-time applications. This service provides fast feature retrieval and computation with automatic caching and load balancing.
This next part is really neat! Here’s how we can tackle this:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import asyncio
import aioredis
import uvicorn
class FeatureService:
def __init__(self):
self.app = FastAPI()
self.redis = None
self.feature_processors = {}
self.setup_routes()
async def initialize(self):
"""Initialize Redis connection pool"""
self.redis = await aioredis.create_redis_pool(
'redis://localhost'
)
async def get_cached_feature(self, feature_key: str):
"""Retrieve cached feature vector"""
cached = await self.redis.get(feature_key)
if cached:
return np.frombuffer(cached, dtype=np.float32)
return None
def setup_routes(self):
@self.app.post("/compute_features")
async def compute_features(request: dict):
feature_key = f"feature_{hash(str(request))}"
# Check cache
cached = await self.get_cached_feature(feature_key)
if cached is not None:
return {"features": cached.tolist()}
# Compute features
features = {}
for name, processor in self.feature_processors.items():
features[name] = await processor(request)
# Cache new features
await self.redis.set(
f"{feature_key}_{name}",
np.array(features[name], dtype=np.float32).tobytes(),
expire=3600 # 1 hour cache
)
return {"features": features}
def run(self):
"""Start feature service"""
uvicorn.run(self.app, host="0.0.0.0", port=8000)
# Example Usage
service = FeatureService()
async def text_embedding_processor(request):
text = request.get('text', '')
# Simplified embedding computation
return np.random.randn(100).tolist()
service.feature_processors['text_embedding'] = text_embedding_processor
if __name__ == "__main__":
service.run()🚀 Automated Model Performance Analysis - Made Simple!
A complete system for analyzing model performance across different data segments and time periods. This example helps identify performance degradation patterns and potential biases.
Let me walk you through this step by step! Here’s how we can tackle this:
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
class ModelAnalyzer:
def __init__(self):
self.performance_history = []
self.segment_performance = {}
def analyze_performance(self, y_true: np.ndarray,
y_pred: np.ndarray,
segments: dict = None):
"""Analyze model performance with detailed metrics"""
# Overall performance
report = classification_report(y_true, y_pred, output_dict=True)
conf_matrix = confusion_matrix(y_true, y_pred)
# Segment-wise analysis
if segments:
for segment_name, segment_mask in segments.items():
segment_true = y_true[segment_mask]
segment_pred = y_pred[segment_mask]
self.segment_performance[segment_name] = {
'report': classification_report(
segment_true,
segment_pred,
output_dict=True
),
'confusion_matrix': confusion_matrix(
segment_true,
segment_pred
)
}
# Store historical performance
self.performance_history.append({
'timestamp': datetime.now(),
'overall_report': report,
'confusion_matrix': conf_matrix,
'segment_performance': self.segment_performance.copy()
})
return {
'overall_report': report,
'segment_performance': self.segment_performance
}
def plot_confusion_matrix(self, segment: str = None):
"""Plot confusion matrix for specified segment"""
if segment and segment in self.segment_performance:
matrix = self.segment_performance[segment]['confusion_matrix']
title = f"Confusion Matrix - {segment}"
else:
matrix = self.performance_history[-1]['confusion_matrix']
title = "Overall Confusion Matrix"
plt.figure(figsize=(10, 8))
sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues')
plt.title(title)
plt.show()
# Example Usage
analyzer = ModelAnalyzer()
# Define segments
segments = {
'high_value_customers': customer_value > 1000,
'new_customers': account_age < 30
}
# Analyze performance
results = analyzer.analyze_performance(y_true, y_pred, segments)
analyzer.plot_confusion_matrix('high_value_customers')🚀 Feature Selection and Importance Analysis - Made Simple!
This example provides a systematic approach to evaluate feature importance and select best feature sets using multiple statistical methods. The system helps identify the most influential features for model performance.
Let’s make this super clear! Here’s how we can tackle this:
from sklearn.feature_selection import mutual_info_classif
from sklearn.ensemble import RandomForestClassifier
import shap
import numpy as np
class FeatureImportanceAnalyzer:
def __init__(self, feature_names: List[str]):
self.feature_names = feature_names
self.importance_scores = {}
self.selected_features = set()
def analyze_importance(self, X: np.ndarray, y: np.ndarray):
"""Analyze feature importance using multiple methods"""
# Mutual Information
mi_scores = mutual_info_classif(X, y)
self.importance_scores['mutual_info'] = dict(
zip(self.feature_names, mi_scores)
)
# Random Forest importance
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X, y)
rf_importance = rf.feature_importances_
self.importance_scores['random_forest'] = dict(
zip(self.feature_names, rf_importance)
)
# SHAP values
explainer = shap.TreeExplainer(rf)
shap_values = explainer.shap_values(X)
shap_importance = np.abs(shap_values).mean(axis=0)
self.importance_scores['shap'] = dict(
zip(self.feature_names, shap_importance)
)
def select_features(self, threshold: float = 0.05):
"""Select features based on importance scores"""
selected = set()
for method, scores in self.importance_scores.items():
# Normalize scores
max_score = max(scores.values())
normalized_scores = {
k: v/max_score for k, v in scores.items()
}
# Select features above threshold
method_selected = {
k for k, v in normalized_scores.items()
if v > threshold
}
selected.update(method_selected)
self.selected_features = selected
return list(selected)
def plot_importance(self, method: str = 'random_forest'):
"""Plot feature importance scores"""
scores = self.importance_scores[method]
sorted_features = sorted(
scores.items(),
key=lambda x: x[1],
reverse=True
)
plt.figure(figsize=(12, 6))
features, values = zip(*sorted_features)
plt.barh(features, values)
plt.title(f'Feature Importance ({method})')
plt.xlabel('Importance Score')
plt.show()
# Example Usage
analyzer = FeatureImportanceAnalyzer(feature_names)
analyzer.analyze_importance(X_train, y_train)
selected_features = analyzer.select_features(threshold=0.05)
analyzer.plot_importance(method='shap')🚀 Model Version Control System - Made Simple!
A complete version control system for ML models that tracks model artifacts, hyperparameters, and performance metrics. This example lets you reproducible experiments and easy model rollbacks.
This next part is really neat! Here’s how we can tackle this:
import git
from dataclasses import dataclass
from typing import Dict, Any
import yaml
import hashlib
@dataclass
class ModelVersion:
version_id: str
params: Dict[str, Any]
metrics: Dict[str, float]
feature_set: List[str]
timestamp: datetime
class ModelVersionControl:
def __init__(self, repo_path: str):
self.repo_path = Path(repo_path)
self.repo = git.Repo.init(repo_path)
self.versions_file = self.repo_path / "versions.yaml"
self.load_versions()
def load_versions(self):
"""Load existing version history"""
if self.versions_file.exists():
with open(self.versions_file, 'r') as f:
self.versions = yaml.safe_load(f)
else:
self.versions = {}
def save_model_version(self,
model,
params: Dict[str, Any],
metrics: Dict[str, float],
feature_set: List[str]):
"""Save new model version with metadata"""
# Generate version ID
version_id = hashlib.sha256(
str(params).encode()
).hexdigest()[:8]
# Save model artifacts
model_path = self.repo_path / f"models/model_{version_id}.joblib"
model_path.parent.mkdir(exist_ok=True)
joblib.dump(model, model_path)
# Create version entry
version = ModelVersion(
version_id=version_id,
params=params,
metrics=metrics,
feature_set=feature_set,
timestamp=datetime.now()
)
# Update versions file
self.versions[version_id] = version.__dict__
with open(self.versions_file, 'w') as f:
yaml.dump(self.versions, f)
# Commit changes
self.repo.index.add([
str(model_path.relative_to(self.repo_path)),
str(self.versions_file.relative_to(self.repo_path))
])
self.repo.index.commit(
f"Model version {version_id} - "
f"accuracy: {metrics.get('accuracy', 0):.3f}"
)
return version_id
def load_model_version(self, version_id: str):
"""Load specific model version"""
if version_id not in self.versions:
raise ValueError(f"Version {version_id} not found")
model_path = self.repo_path / f"models/model_{version_id}.joblib"
model = joblib.load(model_path)
return model, self.versions[version_id]
def compare_versions(self, version_id1: str, version_id2: str):
"""Compare two model versions"""
v1 = self.versions[version_id1]
v2 = self.versions[version_id2]
# Compare metrics
metric_diff = {
k: v2['metrics'][k] - v1['metrics'][k]
for k in v1['metrics']
}
# Compare feature sets
feature_changes = {
'added': set(v2['feature_set']) - set(v1['feature_set']),
'removed': set(v1['feature_set']) - set(v2['feature_set'])
}
return {
'metric_differences': metric_diff,
'feature_changes': feature_changes,
'param_changes': {
k: (v1['params'].get(k), v2['params'].get(k))
for k in set(v1['params']) | set(v2['params'])
if v1['params'].get(k) != v2['params'].get(k)
}
}
# Example Usage
vc = ModelVersionControl("ml_models")
version_id = vc.save_model_version(
model,
params=model.get_params(),
metrics={'accuracy': 0.95},
feature_set=['f1', 'f2', 'f3']
)
comparison = vc.compare_versions('abc123', version_id)🚀 Batch Inference Pipeline - Made Simple!
This example handles large-scale batch predictions smartly with automatic data partitioning and parallel processing. The system includes progress tracking and error handling for reliable production deployments.
Here’s where it gets exciting! Here’s how we can tackle this:
from concurrent.futures import ProcessPoolExecutor
from dataclasses import dataclass
from typing import Iterator, List
import pandas as pd
@dataclass
class BatchPredictionConfig:
batch_size: int = 1000
max_workers: int = 4
timeout: int = 3600
class BatchInferencePipeline:
def __init__(self, model_registry, feature_store,
config: BatchPredictionConfig):
self.model_registry = model_registry
self.feature_store = feature_store
self.config = config
self.prediction_logs = []
def partition_data(self, data: pd.DataFrame) -> Iterator[pd.DataFrame]:
"""Split data into manageable batches"""
for i in range(0, len(data), self.config.batch_size):
yield data.iloc[i:i + self.config.batch_size]
def process_batch(self, batch: pd.DataFrame, model_name: str):
"""Process single batch of data"""
try:
# Load model
model = self.model_registry.load_model(model_name)
# Compute features
features = {}
for feature_name in self.feature_store.get_feature_names():
transformer = self.feature_store.load_transformer(feature_name)
features[feature_name] = transformer(batch)
# Make predictions
X = np.hstack([
features[name] for name in sorted(features.keys())
])
predictions = model.predict(X)
return {
'success': True,
'predictions': predictions,
'batch_size': len(batch)
}
except Exception as e:
return {
'success': False,
'error': str(e),
'batch_size': len(batch)
}
async def run_batch_prediction(self,
data: pd.DataFrame,
model_name: str):
"""Run batch prediction pipeline"""
results = []
failed_batches = []
with ProcessPoolExecutor(
max_workers=self.config.max_workers
) as executor:
# Submit batch processing jobs
future_to_batch = {
executor.submit(
self.process_batch, batch, model_name
): i
for i, batch in enumerate(self.partition_data(data))
}
# Collect results
for future in concurrent.futures.as_completed(
future_to_batch,
timeout=self.config.timeout
):
batch_idx = future_to_batch[future]
result = future.result()
if result['success']:
results.append(result['predictions'])
else:
failed_batches.append({
'batch_idx': batch_idx,
'error': result['error']
})
# Combine predictions
if results:
final_predictions = np.concatenate(results)
else:
final_predictions = np.array([])
# Log prediction run
self.prediction_logs.append({
'timestamp': datetime.now(),
'model_name': model_name,
'total_records': len(data),
'successful_predictions': len(final_predictions),
'failed_batches': failed_batches
})
return final_predictions, failed_batches
# Example Usage
config = BatchPredictionConfig(batch_size=1000, max_workers=4)
pipeline = BatchInferencePipeline(registry, feature_store, config)
predictions, failures = await pipeline.run_batch_prediction(
large_dataset,
"production_model_v1"
)
if failures:
print(f"Failed batches: {len(failures)}")
for failure in failures:
print(f"Batch {failure['batch_idx']}: {failure['error']}")🚀 Additional Resources - Made Simple!
- Reliable Machine Learning Systems: https://arxiv.org/abs/2212.08697
- Feature Store Architectures: https://arxiv.org/abs/2210.15742
- ML Pipeline Design Patterns: https://arxiv.org/abs/2205.09330
- Best practices for searching ML-related papers:
- Google Scholar with keywords: “ML pipeline architecture”, “feature store design”, “ML system design”
- Papers With Code: https://paperswithcode.com/task/mlops
- ArXiv CS.LG category: https://arxiv.org/list/cs.LG/recent
🎊 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! 🚀