Definition #
Cloud services providing machine learning tools and infrastructure via APIs and managed platforms.
Key Characteristics #
- Pre-built model marketplace
- Auto-scaling GPU clusters
- MLOps integration
- Pay-as-you-go pricing
Why It Matters #
Enables enterprises to deploy AI without ML engineering teams (85% adoption growth in 2023).
Common Use Cases #
- Predictive maintenance systems
- Natural language processing
- Computer vision applications
Examples #
- Google Vertex AI
- AWS SageMaker
- Azure Machine Learning
FAQs #
Q: How does MLaaS differ from traditional ML?
A: Abstracts infrastructure management - focus on data/models vs. servers.
Q: Vendor lock-in risks?
A: Use ONNX format for model portability between platforms.