Machine Learning Operations (MLOps) Platform

A Machine Learning Operations (MLOps) Platform is a software platform system that can support machine learning operations tasks.

• AKA: MLOps Platform, ML Operations Platform, Machine Learning Ops Platform, ML Platform.
• Context:
  • It can orchestrate Machine Learning Model Lifecycle through machine learning operations pipelines.
  • It can manage Machine Learning Model Version through machine learning operations version control systems.
  • It can automate Machine Learning Model Training through machine learning operations training infrastructures.
  • It can facilitate Machine Learning Feature Engineering through machine learning operations feature stores.
  • It can enable Machine Learning Model Deployment through machine learning operations serving systems.
  • ...
  • It can monitor Machine Learning Model Performance through machine learning operations monitoring tools.
  • It can track Machine Learning Experiment through machine learning operations experiment tracking systems.
  • It can ensure Machine Learning Model Reproducibility through machine learning operations artifact managements.
  • It can validate Machine Learning Model Quality through machine learning operations testing frameworks.
  • ...
  • It can range from being a Simple Machine Learning Operations Platform to being a Enterprise Machine Learning Operations Platform, depending on its machine learning operations capability scope.
  • It can range from being a Open-Source Machine Learning Operations Platform to being a Proprietary Machine Learning Operations Platform, depending on its machine learning operations licensing model.
  • It can range from being a Cloud-Native Machine Learning Operations Platform to being a Hybrid Machine Learning Operations Platform, depending on its machine learning operations deployment architecture.
  • ...
  • It can integrate Machine Learning Operations Components with data infrastructure systems.
  • It can support Machine Learning Operations Workflows with workflow orchestration engines.
  • It can implement Machine Learning Operations Best Practices with automated compliance checks.
  • It can provide Machine Learning Operations APIs for programmatic platform interactions.
  • It can enforce Machine Learning Operations Security Protocols for model and data protections.
  • ...
• Example(s):
  • Third-Party Machine Learning Operations Platforms, such as:
    • Cloud-Based Machine Learning Operations Platforms, such as:
      • Amazon SageMaker Platform for AWS machine learning operations.
      • Google Cloud VertexAI Platform for GCP machine learning operations.
      • Azure Machine Learning Platform for Azure machine learning operations.
      • Databricks Machine Learning Platform for unified data and ML operations.
    • Standalone Machine Learning Operations Platforms, such as:
      • MLflow Platform for open-source machine learning operations.
      • Kubeflow Platform for Kubernetes-based machine learning operations.
      • Weights & Biases Platform for machine learning operations experiment tracking.
      • Neptune.ai Platform for machine learning operations metadata management.
  • In-House Machine Learning Operations Platforms, such as:
    • Netflix Machine Learning Operations Platform (including Netflix Metaflow).
    • Uber Machine Learning Operations Platform (Uber Michelangelo).
    • Meta Machine Learning Operations Platform (Meta FBLearner).
    • Airbnb Machine Learning Operations Platform.
  • Specialized Machine Learning Operations Platforms, such as:
    • Computer Vision MLOps Platforms for computer vision model operations.
    • NLP MLOps Platforms for natural language processing operations.
    • Edge MLOps Platforms for edge device machine learning operations.
  • ...
• Counter-Example(s):
  • Data Analytics Platform, which lacks machine learning model lifecycle management.
  • Business Intelligence Platform, which lacks machine learning operations automation capability.
  • ETL Platform, which lacks machine learning model training and deployment features.
  • DevOps Platform, which lacks machine learning-specific operations tools.
  • Machine Learning Framework, which lacks end-to-end operations management capability.
• See: Machine Learning Platform, Machine Learning Engineering, MLOps, Machine Learning Infrastructure, Machine Learning Pipeline, Model Management System.

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