Machine Learning Workflows

Machine learning development is an iterative lifecycle accommodating data variability.

Lifecycle Architecture

The development lifecycle consists of sequential phases and feedback loops:

skinparam backgroundColor transparent
start
:Data Collection;
repeat
:Exploratory Data Analysis (EDA);
:Data Preprocessing & Cleaning;
:Model Training & Selection;
backward:Refine Features or Architecture;
repeat while (Meets Performance Goal?) is (no) not (yes)
:Final Validation;
:Model Deployment;
stop

Three Core Workflows

Developing machine learning systems involves three distinct architectural flows depending on the operational stage:

1. The Experimental Flow (Exploration & Prototyping)

Focused on data discovery. Data scientists work in interactive environments (e.g., notebooks) to query raw sources, perform Exploratory Data Analysis (EDA) to locate distributions and outliers, engineer prototype features, and evaluate baseline hypotheses.

2. The Production Pipeline (Continuous Training)

Focused on automation and scalability. Notebook experiments are codified into structured, reproducible pipelines:

1. Validation: load and validate incoming data schemas.
2. Preprocessing: fit and execute transformations.
3. Training: automate hyperparameter search.
4. Registry: evaluate targets and register valid binaries.

3. The Inference Flow (Batch vs. Real-time)

Deployment layouts depend on latency and throughput constraints:

• Batch: offline, scheduled predictions on large datasets, maximizing throughput.
• Real-time: on-demand predictions (e.g., via APIs) with minimal latency (under 100ms).

Example: Chaining Workflows via Pipelines

Scikit-learn Pipeline objects bundle preprocessing steps and estimators, preventing data leakage during validation:

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

df = pd.DataFrame({'x': [1.0, np.nan, 3.0, 4.0, 5.0]})
y = np.array([0, 0, 1, 1, 1])

# Chain imputation, scaling, and classifier
pipeline = Pipeline([
  ('imputer', SimpleImputer(strategy='median')),
  ('scaler', StandardScaler()),
  ('classifier', LogisticRegression())
])

pipeline.fit(df, y)
pred = pipeline.predict(df)
print("Predictions:", pred)
print("Pipeline steps:", [name for name, _ in pipeline.steps])

Predictions: [0 0 1 1 1]
Pipeline steps: ['imputer', 'scaler', 'classifier']

Exercise

Test your understanding of ML pipeline architectures:

References & Further Reading

• Géron, A. (2022). Hands-On Machine Learning (3rd ed.). O’Reilly Media.