Machine Learning
An index and topic collection covering machine learning APIs, MLOps platforms, model serving infrastructure, and inference providers. Machine learning APIs span the full ML lifecycle — from data labeling, experiment tracking, and model training to model registries, hosted inference, and vector search. This collection brings together hyperscaler ML platforms (Amazon SageMaker, Google Vertex AI, Azure Machine Learning), open-source MLOps frameworks (MLflow, Kubeflow, ZenML, DVC), GPU inference providers (Together AI, Fireworks AI, Replicate, Groq, Modal, Baseten), vector databases (Pinecone, Weaviate, Milvus, Qdrant, Chroma), and model hubs (Hugging Face) that together power production machine learning at scale.
Machine Learning is profiled on the APIs.io network. Tagged areas include Machine Learning, MLOps, Model Serving, Inference, and AutoML.
The Machine Learning catalog on APIs.io includes 1 JSON-LD context.
Machine Learning’s developer surface includes developer portal and 1 more developer resources.
Kin Score
Kin Score
How this is scored →scored 2026-10-23 · rubric v0.25.0
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Features 8
Notable capabilities this provider offers.
Hosted Model Inference
ML APIs from providers like Hugging Face, Replicate, Together AI, Fireworks AI, and Groq expose pre-trained and fine-tuned models behind HTTP endpoints so developers can call in...
Model Training and Fine-Tuning
Platforms like Amazon SageMaker, Google Vertex AI, Azure Machine Learning, and OpenPipe expose APIs for launching training jobs, configuring hyperparameters, and fine-tuning fou...
Experiment Tracking and Model Registry
MLflow, Weights & Biases, Comet, Neptune.ai, and ClearML provide APIs to log experiments, track metrics, compare runs, and register approved model versions for downstream deploy...
Vector Search and Embeddings
Vector databases like Pinecone, Weaviate, Milvus, Qdrant, and Chroma expose APIs to index embeddings and run nearest-neighbor search powering retrieval-augmented generation and ...
Model Serving and Deployment
Serving frameworks like KServe, vLLM, Ray Serve, Baseten, and TrueFoundry provide APIs to deploy models as scalable HTTP or gRPC endpoints with autoscaling, batching, and routing.
ML Pipeline Orchestration
Kubeflow Pipelines, ZenML, and DVC expose APIs to define, version, and execute ML pipelines spanning data preparation, training, evaluation, and deployment stages.
Data Labeling and Annotation
Label Studio and similar platforms expose APIs for managing labeling projects, importing data, assigning tasks to annotators, and exporting labeled datasets for model training.
LLM Gateway and Routing
LiteLLM, Portkey, and similar gateways provide unified APIs that route requests across multiple LLM providers with fallback, caching, rate limiting, and observability.
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Semantic Vocabularies 1
JSON-LD contexts and semantic vocabularies used across these APIs.
Machine Learning Context
JSON-LDJSON Schema 2
Standalone JSON Schema definitions for this provider's data models.
InferenceRequest
JSON SCHEMAModel
JSON SCHEMAJSON Structure 2
JSON Structure definitions describing this provider's data shapes.
Machine Learning Inference Request Structure
JSON STRUCTUREMachine Learning Model Structure
JSON STRUCTUREExamples 2
Example request and response payloads for these APIs.
Use Cases 8
What developers build with this provider.
Retrieval-Augmented Generation
Combining a vector database (Pinecone, Weaviate, Qdrant) with an embeddings API and an LLM inference endpoint to ground model responses in private knowledge bases.
Fine-Tuning Foundation Models
Using SageMaker, Vertex AI, OpenPipe, or Together AI APIs to fine-tune open foundation models on proprietary datasets and deploy the resulting model behind a managed inference e...
Scalable Model Inference at the Edge
Deploying optimized models through Groq, Modal, Replicate, or Baseten to serve high-throughput, low-latency inference for chatbots, recommendation systems, and content generation.
End-to-End MLOps Automation
Using Kubeflow, MLflow, Weights & Biases, and ZenML to track experiments, register approved models, trigger retraining, and promote models to production via API.
Multimodal Application Development
Composing image, audio, video, and text models from Hugging Face, Replicate, and Fireworks AI through standard inference APIs to build multimodal user experiences.
Semantic Search and Recommendations
Indexing product catalogs, documents, or media in vector databases like Milvus or Vespa and exposing semantic search APIs to power discovery and personalization.
Model Observability and Cost Control
Using gateways like Portkey and LiteLLM alongside observability platforms to monitor inference latency, cost-per-request, and routing decisions across multiple model providers.
Distributed Training at Scale
Running large-scale distributed training jobs on Ray, Anyscale, Determined AI, or Databricks via API, including hyperparameter tuning and GPU cluster orchestration.
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Integrations 8
Pre-built integrations with other platforms and tools.
Hugging Face
Model hub and inference API hosting hundreds of thousands of open-source transformer models, datasets, and Spaces with managed Inference Endpoints.
Amazon SageMaker
End-to-end ML platform on AWS for building, training, deploying, and monitoring models, including SageMaker Studio, JumpStart foundation models, and managed endpoints.
Google Vertex AI
Unified ML platform on Google Cloud covering AutoML, custom training, Model Registry, Pipelines, and Generative AI Studio for foundation models like Gemini.
MLflow
Open-source platform for ML lifecycle management with APIs for experiment tracking, model registry, and deployment across many backends.
Weights & Biases
Experiment tracking, evaluations, model registry, and LLM observability platform with rich APIs for logging metrics and managing models.
Replicate
API platform for running open-source models in the cloud with simple per-second pricing and one-line deployment of custom Cog containers.
Together AI
Inference and fine-tuning platform for open foundation models, exposing OpenAI-compatible APIs for chat, completion, and embeddings.
Pinecone
Managed vector database for high-scale similarity search, hybrid search, and metadata filtering powering production RAG applications.
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Resources
Get Started 1
Portal, sign-up, and the first successful call
Build 1
SDKs, sample code, and the tooling you integrate with
Source (apis.yml)
Work with this as data
Every provider here is available over the APIs.io API and to AI agents over MCP.