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Machine Learning
mlops
ML Pipeline
Automates the workflow of data ingestion, preprocessing, model training, evaluation, and deployment.
Intent & Description
An ML Pipeline structures the end-to-end flow of data and model operations as a series of modular, sequential stages. This guarantees reproducibility, simplifies debugging, and enables continuous training/retraining (CT) loops.
Real-world Use Case
Implementing automated weekly retraining loops for e-commerce recommendation systems or fraud detection models.
Advantages
- Ensures consistent data transformation between training and online inference.
- Highly modular and reusable steps.
- Simplifies tracking of data lineage, parameters, and versioning.
Disadvantages
- Can introduce significant engineering overhead for small, experimental models.
- Debugging intermediate steps in a running pipeline can be complex.
Implementation Example
# A modular Python ML Pipeline implementation
class PipelineStep:
def transform(self, data):
raise NotImplementedError()
class DataIngestion(PipelineStep):
def transform(self, data):
print("Ingesting raw data...")
return data + " -> [Ingested]"
class FeatureEngineering(PipelineStep):
def transform(self, data):
print("Extracting features...")
return data + " -> [Features]"
class ModelTraining(PipelineStep):
def transform(self, data):
print("Training model...")
return data + " -> [Model Trained]"
class MLPipeline:
def __init__(self):
self.steps = []
def add_step(self, step):
self.steps.append(step)
return self
def execute(self, raw_input):
current_data = raw_input
for step in self.steps:
current_data = step.transform(current_data)
return current_data
# Usage
pipeline = (MLPipeline()
.add_step(DataIngestion())
.add_step(FeatureEngineering())
.add_step(ModelTraining()))
print(pipeline.execute("Raw Telemetry"))