⚙️ MemoLearning Machine Learning Pipelines

Build automated, scalable, and reproducible machine learning workflows and systems

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Machine Learning Pipelines Curriculum

12
Core Units
~85
Pipeline Concepts
10+
Tools & Frameworks
20+
Best Practices
1

Pipeline Fundamentals

Understand the core concepts of ML pipelines and why they are essential for production systems.

  • What are ML pipelines
  • Benefits of pipeline automation
  • Pipeline components and stages
  • Data flow and dependencies
  • Reproducibility and versioning
  • Pipeline vs script differences
  • Common pipeline patterns
  • Industry best practices
2

Scikit-learn Pipelines

Master Scikit-learn's Pipeline class for creating simple yet powerful ML workflows.

  • Pipeline class basics
  • Transformer and estimator steps
  • ColumnTransformer usage
  • Feature union techniques
  • Pipeline composition
  • Cross-validation with pipelines
  • Hyperparameter tuning
  • Custom transformer creation
3

Data Preprocessing Pipelines

Build robust data preprocessing workflows that handle cleaning, transformation, and feature engineering.

  • Data validation and cleaning
  • Missing value handling
  • Feature scaling and normalization
  • Categorical encoding
  • Feature selection automation
  • Outlier detection and treatment
  • Text preprocessing pipelines
  • Image preprocessing workflows
4

Feature Engineering Automation

Automate feature creation, selection, and transformation processes within pipeline workflows.

  • Automated feature generation
  • Feature interaction creation
  • Polynomial and mathematical features
  • Time-based feature extraction
  • Domain-specific feature engineering
  • Feature selection pipelines
  • Feature store integration
  • Dynamic feature updates
5

Model Training Pipelines

Create automated workflows for model training, validation, and hyperparameter optimization.

  • Training workflow design
  • Automated model selection
  • Hyperparameter tuning automation
  • Cross-validation integration
  • Early stopping and callbacks
  • Model checkpointing
  • Distributed training
  • Experiment tracking
6

Pipeline Orchestration

Learn workflow orchestration tools and frameworks for managing complex ML pipelines.

  • Apache Airflow
  • Kubeflow Pipelines
  • MLflow Projects
  • Prefect workflows
  • Azure ML Pipelines
  • AWS Step Functions
  • Google Cloud Composer
  • Pipeline scheduling and triggers
7

Model Deployment Pipelines

Automate model deployment and serving through CI/CD pipelines and containerization.

  • Continuous integration for ML
  • Continuous deployment strategies
  • Docker containerization
  • Kubernetes deployment
  • Model serving frameworks
  • API endpoint creation
  • Blue-green deployments
  • Canary releases
8

Real-time and Batch Pipelines

Design pipelines for both real-time inference and batch processing scenarios.

  • Streaming data pipelines
  • Real-time feature computation
  • Batch processing optimization
  • Lambda and Kappa architectures
  • Event-driven pipelines
  • Message queue integration
  • Data freshness management
  • Latency optimization
9

Pipeline Monitoring and Logging

Implement comprehensive monitoring, logging, and alerting for ML pipeline health and performance.

  • Pipeline health monitoring
  • Data quality checks
  • Model performance tracking
  • Error handling and recovery
  • Logging best practices
  • Alerting systems
  • Dashboard creation
  • Debugging pipeline failures
10

Testing ML Pipelines

Develop comprehensive testing strategies for ML pipelines including unit, integration, and end-to-end tests.

  • Unit testing for transformers
  • Integration testing strategies
  • End-to-end pipeline testing
  • Data validation testing
  • Model performance testing
  • Regression testing
  • Load and stress testing
  • Test automation frameworks
11

Scalability and Performance

Optimize ML pipelines for scalability, performance, and efficient resource utilization.

  • Parallel processing strategies
  • Distributed computing integration
  • Memory optimization techniques
  • Caching and memoization
  • Resource allocation
  • Performance profiling
  • Bottleneck identification
  • Auto-scaling configurations
12

Production Pipeline Management

Manage production ML pipelines with versioning, rollbacks, and operational excellence practices.

  • Pipeline versioning strategies
  • Rollback and recovery procedures
  • Configuration management
  • Security and compliance
  • Cost optimization
  • Operational runbooks
  • Team collaboration workflows
  • Documentation and maintenance

Unit 1: Pipeline Fundamentals

Understand the core concepts of ML pipelines and why they are essential for production systems.

What are ML Pipelines

Learn the fundamental concept of ML pipelines as automated workflows that orchestrate the entire machine learning process.

Automation Orchestration Workflow
An ML pipeline is a sequence of automated steps that transform raw data into predictions, encompassing data preprocessing, feature engineering, model training, and deployment.

Benefits of Pipeline Automation

Understand why automating ML workflows is crucial for production systems and team productivity.

• Reproducibility and consistency
• Reduced manual errors
• Faster iteration and deployment
• Better collaboration
• Scalable operations
# Manual approach (error-prone)
data = load_data()
cleaned_data = clean_data(data)
features = engineer_features(cleaned_data)
model = train_model(features)

# Pipeline approach (automated)
pipeline = Pipeline([
  ('cleaner', DataCleaner()),
  ('features', FeatureEngineer()),
  ('model', ModelTrainer())
])
pipeline.fit(data)

Pipeline Components and Stages

Learn the typical components that make up an ML pipeline and how they connect together.

Data Ingestion → Data Validation → Preprocessing → Feature Engineering → Model Training → Model Evaluation → Model Deployment → Monitoring
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

# Simple pipeline example
pipeline = Pipeline([
  ('scaler', StandardScaler()),
  ('classifier', RandomForestClassifier())
])

# Fit and predict in one step
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

Data Flow and Dependencies

Understand how data flows through pipeline stages and how to manage dependencies between steps.

# Define dependencies explicitly
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer

# Preprocessing depends on data types
numeric_features = ['age', 'income']
categorical_features = ['gender', 'category']

preprocessor = ColumnTransformer(
  transformers=[
    ('num', StandardScaler(), numeric_features),
    ('cat', OneHotEncoder(), categorical_features)
  ])

# Model depends on preprocessed features
pipeline = Pipeline([
  ('preprocessor', preprocessor),
  ('classifier', LogisticRegression())
])

Reproducibility and Versioning

Learn how pipelines enable reproducible ML workflows through versioning and environment management.

Git Docker MLflow
import mlflow
import mlflow.sklearn

# Version your pipeline
with mlflow.start_run():
  pipeline.fit(X_train, y_train)
  accuracy = pipeline.score(X_test, y_test)
  
  # Log parameters and metrics
  mlflow.log_param("model_type", "RandomForest")
  mlflow.log_metric("accuracy", accuracy)
  
  # Save the entire pipeline
  mlflow.sklearn.log_model(pipeline, "model")

Pipeline vs Script Differences

Compare traditional scripts with pipeline approaches and understand when to use each.

Scripts: Good for exploration and one-off analysis
Pipelines: Essential for production, reusability, and collaboration
# Script approach (hard to maintain)
def train_model_script():
  data = pd.read_csv('data.csv')
  # 50 lines of preprocessing...
  model = RandomForestClassifier()
  model.fit(processed_data, labels)
  return model

# Pipeline approach (maintainable)
class MLPipeline:
  def __init__(self):
    self.pipeline = self._build_pipeline()
  
  def _build_pipeline(self):
    return Pipeline([...])

Common Pipeline Patterns

Learn common patterns and architectures used in ML pipeline design.

• Linear Pipeline: Sequential steps
• Branching Pipeline: Parallel processing paths
• Ensemble Pipeline: Multiple models combined
• Feedback Pipeline: Iterative improvement

Industry Best Practices

Follow industry-proven practices for building robust and maintainable ML pipelines.

• Start simple, add complexity gradually
• Make pipelines testable and debuggable
• Version everything (code, data, models)
• Monitor pipeline health continuously
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