Microservices

TECHNICAL DEFINITION

Microservices architecture decomposes complex AI applications into a suite of small, loosely coupled, independently deployable services, each responsible for a specific business capability (e.g., model inference, data preprocessing, user authentication), enhancing agility and fault isolation.

BACKGROUND

Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered, pay-as-you-go basis.

SYNONYMS & ALIASES

• Service-oriented architecture (SOA - lighter)
• distributed services
• modular services

USAGE NOTE

Many modern AI platforms adopt microservices to allow independent development and deployment of different AI components.

DEVELOPERS

Organizations developing technology related to Microservices.

• Amazon Web Services (AWS)

  AWS provides a comprehensive suite of cloud services, including serverless computing (Lambda), container orchestration (ECS, EKS), and machine learning platforms (SageMaker), that enable AI engineers to build, deploy, and scale AI models and prompt-based applications as microservices.

• Microsoft Azure

  Azure offers extensive cloud infrastructure and services like Azure Kubernetes Service, Azure Functions, and Azure Machine Learning, which are used by AI engineers to develop, deploy, and manage AI applications and prompt-serving systems using microservice architectures.

• Google Cloud Platform (GCP)

  GCP provides robust infrastructure, including Google Kubernetes Engine, Cloud Functions, and Vertex AI, allowing AI engineers to design, deploy, and manage AI models and prompt-driven services using scalable microservice patterns.

• Hugging Face

  While known for models and libraries, Hugging Face also provides inference APIs and tools for deploying Transformer models, which are often served as microservices, critical for AI engineering in natural language processing and prompt design.

• Seldon

  Seldon specializes in MLOps, providing an open-source platform (Seldon Core) for deploying, monitoring, and managing machine learning models on Kubernetes. This enables AI engineers to serve models as scalable microservices, essential for production AI and prompt-based systems.

• Verta.ai

  Verta.ai offers an MLOps platform that facilitates the development, deployment, and management of machine learning models. It supports the operationalization of AI models, often by deploying them as versioned and scalable microservices, crucial for robust AI engineering workflows.

• DataRobot

  DataRobot provides an enterprise AI platform that helps organizations build, deploy, and manage AI systems. Their platform supports the operationalization of machine learning models, frequently by packaging and serving them as microservices for scalable AI solutions.

• Databricks

  The Databricks Lakehouse Platform, with its MLOps capabilities including MLflow, enables AI engineers to manage the lifecycle of machine learning models. This often involves deploying and monitoring models as microservices for scalable and reliable production AI systems.