Catalog
concept#Artificial Intelligence#Machine Learning#Platform

ML Framework

Conceptual overview of software frameworks for machine learning that support model development, training, and deployment.

A machine learning framework is a structural software concept that bundles algorithms, abstractions, and runtime components to develop, train, and serve models.
Established
High

Classification

  • High
  • Technical
  • Technical
  • Intermediate

Technical context

Feature store and data warehouseCI/CD systems (e.g., Jenkins, GitHub Actions)Monitoring and observability tools

Principles & goals

Reproducibility: Training runs must be reproducibly documented.Separation of model logic and infrastructure configuration.Versioning of models, data, and configurations.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Outdated dependencies lead to maintenance burden.
  • Poor model performance due to incorrect default configurations.
  • Insufficient monitoring causes unexpected production failures.
  • Strict versioning of models, data, and configurations
  • Automated tests for training and inference paths
  • Monitor data drift and production performance

I/O & resources

  • Curated training and validation dataset
  • Compute resources (GPU/TPU/cluster)
  • Feature engineering and metadata
  • Versioned model artifact
  • Evaluation reports and metrics
  • Deployable service modules/containers

Description

A machine learning framework is a structural software concept that bundles algorithms, abstractions, and runtime components to develop, train, and serve models. It defines APIs, data pipelines, and infrastructure integrations as well as conventions for reproducibility, performance, and model lifecycle management in production systems. Organizations choose frameworks based on scalability, ecosystem, and operational requirements.

✔

  • Faster development through reusable abstractions.
  • Improved reproducibility and traceability of experiments.
  • Easier integration into production pipelines and monitoring.

✖

  • Lock-in effects due to proprietary APIs or ecosystems.
  • High resource requirements for large-scale training.
  • Complexity in interoperability between frameworks.

  • Training throughput

    Measure of processed samples per second during training.

  • Model accuracy

    Standard evaluation metrics such as accuracy, F1, or ROC-AUC.

  • Deployment frequency

    Frequency at which new model versions are rolled out to production.

TensorFlow at scale

Use of TensorFlow for distributed training and serving in production systems.

scikit-learn for classical ML pipelines

Use of scikit-learn for prototyping models and data-driven feature development.

PyTorch from research to production

PyTorch combines research-oriented development with production deployment via additions like TorchServe.

1

Assess needs and define selection criteria

2

Build a proof-of-concept with a representative pipeline

3

Establish integration into CI/CD, monitoring, and governance

⚠️ Technical debt & bottlenecks

  • Legacy training scripts without tests
  • Manual feature extraction pipelines
  • Unversioned model artifacts in production
data-qualityscalabilitylatency
  • Using a deep learning framework for very small datasets without regularization
  • Directly moving research code to production without tests
  • Ignoring data quality issues and bias checks
  • Hidden dependencies between library versions
  • Non-optimized I/O pipelines slow down training runs
  • Missing configuration standards lead to divergence in the team
Machine learning and statisticsSoftware engineering and DevOpsData engineering and feature engineering
Training scalabilityReproducibility of experimentsIntegrability with CI/CD and monitoring
  • • Available compute capacity and budget
  • • Regulatory requirements for data and models
  • • Compatibility with existing infrastructure