ML Pipelines

Data Pipeline Architecture and Design

• What you Need to Know

  • ETL/ELT Pipeline Patterns for ML

    • Extract, Transform, Load (ETL) vs Extract, Load, Transform (ELT) strategies
    • Batch processing vs stream processing for ML data
    • Data pipeline orchestration and scheduling frameworks
    • Resources:
      • Apache Airflow Documentation - Workflow orchestration platform for data pipelines
      • Prefect Documentation - Modern data workflow orchestration
      • Luigi Pipeline Framework - Python pipeline building framework

  • Data Ingestion and Collection

    • Real-time data streaming with Apache Kafka
    • Batch data ingestion from databases and APIs
    • Data lake and data warehouse integration patterns
    • Resources:
      • Apache Kafka Documentation - Distributed streaming platform
      • Apache Spark Structured Streaming - Real-time data processing
      • Delta Lake - Reliable data lakes with ACID transactions

  • Data Validation and Quality Assurance

    • Schema validation and data type enforcement
    • Statistical data profiling and anomaly detection
    • Data quality monitoring and alerting systems
    • Resources:
      • Great Expectations - Data validation and documentation framework
      • Pandera - Statistical data validation for pandas
      • Apache Griffin - Data quality solution for big data

Feature Engineering Pipelines

• What you Need to Know

  • Automated Feature Engineering

    • Feature extraction and transformation pipelines
    • Time-series feature engineering and windowing
    • Text and image feature engineering automation
    • Resources:
      • Feature-engine - Feature engineering library for Python
      • TSfresh - Automated time series feature extraction
      • Featuretools - Automated feature engineering framework

  • Feature Store Implementation

    • Online and offline feature serving architecture
    • Feature versioning and lineage tracking
    • Feature monitoring and drift detection
    • Resources:
      • Feast Feature Store - Open-source feature store for ML
      • Tecton Feature Platform - Enterprise feature store concepts
      • AWS SageMaker Feature Store - Managed feature store service

  • Feature Pipeline Optimization

    • Distributed feature computation with Spark
    • Feature caching and materialization strategies
    • Feature pipeline performance monitoring
    • Resources:
      • Apache Spark ML Pipelines - Scalable ML pipeline construction
      • Dask for Feature Engineering - Parallel computing for feature pipelines
      • Ray for Distributed ML - Distributed computing framework

Training Pipeline Automation

• What you Need to Know

  • Automated Model Training Workflows

    • Training pipeline orchestration and scheduling
    • Distributed training and parallel processing
    • Hyperparameter optimization integration
    • Resources:
      • Kubeflow Pipelines - ML workflows on Kubernetes
      • Apache Beam - Unified batch and stream processing
      • MLflow Projects - Reproducible ML project packaging

  • Distributed Training Systems

    • Multi-GPU and multi-node training coordination
    • Parameter server and all-reduce architectures
    • Fault tolerance and recovery mechanisms
    • Resources:
      • Horovod - Distributed deep learning training framework
      • PyTorch Distributed - Distributed training with PyTorch
      • TensorFlow Distributed - TensorFlow distributed strategies

  • Continuous Training and Model Updates

    • Automated retraining triggers and schedules
    • Incremental learning and online model updates
    • Model performance monitoring and retraining decisions
    • Resources:
      • Continuous Training Patterns - Continuous delivery for ML systems
      • MLflow Model Registry - Model lifecycle management
      • Evidently AI - ML model monitoring and retraining

Pipeline Orchestration Frameworks

• What you Need to Know

  • Apache Airflow for ML Workflows

    • DAG design patterns for ML pipelines
    • Custom operators for ML tasks
    • Integration with ML frameworks and cloud services
    • Resources:
      • Airflow ML Tutorial - Building ML workflows with Airflow
      • Airflow ML Operators - ML-specific Airflow operators
      • Astronomer Airflow - Managed Airflow platform

  • Kubeflow for Kubernetes-Native ML

    • Kubeflow Pipelines for ML workflow orchestration
    • Katib for hyperparameter tuning
    • KFServing for model serving
    • Resources:
      • Kubeflow Documentation - End-to-end ML platform on Kubernetes
      • Kubeflow Pipelines SDK - Python SDK for pipeline development
      • Kubeflow Examples - Real-world Kubeflow implementations

  • Cloud-Native Pipeline Solutions

    • AWS Step Functions for serverless ML workflows
    • Azure ML Pipelines for integrated ML workflows
    • Google Cloud Composer for managed Airflow
    • Resources:
      • AWS Step Functions - Serverless workflow orchestration
      • Azure ML Pipelines - Azure ML workflow management
      • Google Cloud Composer - Managed Apache Airflow service

Data Processing and Transformation

• What you Need to Know

  • Big Data Processing with Apache Spark

    • Spark SQL for large-scale data transformations
    • Spark MLlib for distributed machine learning
    • Spark Streaming for real-time data processing
    • Resources:
      • Apache Spark Documentation - Unified analytics engine for big data
      • PySpark Tutorial - Python API for Spark
      • Spark ML Pipelines - Machine learning pipelines

  • Stream Processing for Real-Time ML

    • Apache Kafka Streams for stream processing
    • Apache Flink for complex event processing
    • Real-time feature computation and serving
    • Resources:
      • Kafka Streams - Stream processing library
      • Apache Flink - Stream processing framework
      • Confluent Platform - Enterprise Kafka platform

  • Data Transformation and Preprocessing

    • Scalable data cleaning and preprocessing
    • Feature scaling and normalization at scale
    • Categorical encoding and text processing pipelines
    • Resources:
      • Pandas on Ray - Scalable pandas operations
      • Dask DataFrame - Parallel pandas operations
      • Vaex - Out-of-core dataframe processing

Pipeline Testing and Validation

• What you Need to Know

  • Data Pipeline Testing

    • Unit testing for data transformations
    • Integration testing for end-to-end pipelines
    • Data quality testing and validation
    • Resources:
      • pytest for Data Pipelines - Python testing framework
      • Great Expectations Testing - Data validation testing
      • dbt Testing - Data transformation testing

  • ML Pipeline Validation

    • Model training pipeline testing
    • Cross-validation and holdout testing strategies
    • Pipeline performance and resource testing
    • Resources:
      • ML Testing Best Practices - Comprehensive ML testing guide
      • Model Validation Techniques - Cross-validation and model selection
      • Pipeline Performance Testing - ML pipeline testing examples

  • Continuous Integration for Pipelines

    • Automated pipeline testing in CI/CD
    • Pipeline deployment and rollback strategies
    • Environment-specific pipeline configuration
    • Resources:
      • GitHub Actions for ML - CI/CD for ML pipelines
      • GitLab CI/CD for Data Science - Pipeline automation examples
      • Jenkins for ML Pipelines - Pipeline automation with Jenkins

Pipeline Monitoring and Observability

• What you Need to Know

  • Pipeline Performance Monitoring

    • Execution time and resource utilization tracking
    • Pipeline failure detection and alerting
    • Bottleneck identification and optimization
    • Resources:
      • Prometheus for Pipeline Monitoring - Metrics collection for pipelines
      • Grafana Dashboards - Pipeline performance visualization
      • Apache Airflow Monitoring - Airflow observability

  • Data Quality Monitoring

    • Schema drift detection and alerting
    • Statistical data profiling and anomaly detection
    • Data freshness and completeness monitoring
    • Resources:
      • Monte Carlo Data Observability - Data reliability platform
      • Datadog Data Monitoring - Infrastructure and application monitoring
      • Evidently Data Drift - Data and model drift detection

  • Pipeline Logging and Debugging

    • Structured logging for pipeline components
    • Error tracking and root cause analysis
    • Pipeline lineage and dependency tracking
    • Resources:
      • Structured Logging Best Practices - Logging strategies
      • ELK Stack for Pipeline Logs - Log aggregation and analysis
      • Apache Atlas - Data governance and lineage

Pipeline Optimization and Scaling

• What you Need to Know

  • Performance Optimization Techniques

    • Pipeline parallelization and resource allocation
    • Caching strategies for intermediate results
    • Data partitioning and distribution optimization
    • Resources:
      • Spark Performance Tuning - Apache Spark optimization guide
      • Dask Performance - Distributed computing optimization
      • Pipeline Caching Strategies - Result caching in workflows

  • Auto-Scaling and Resource Management

    • Dynamic resource allocation based on workload
    • Cost optimization for cloud-based pipelines
    • Spot instance and preemptible VM usage
    • Resources:
      • Kubernetes Autoscaling - Pod autoscaling for ML workloads
      • AWS Batch for ML - Managed batch computing service
      • Google Cloud Dataflow - Serverless data processing

  • Pipeline Reliability and Fault Tolerance

    • Retry mechanisms and error handling
    • Checkpointing and recovery strategies
    • Circuit breaker patterns for pipeline components
    • Resources:
      • Fault Tolerance Patterns - Distributed systems reliability
      • Apache Beam Fault Tolerance - Stream processing reliability
      • Airflow Error Handling - Pipeline error recovery

Ready to Deploy? Continue to Module 3: Model Deployment to master production model serving, containerization, and scalable inference systems.