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.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.

How we profile Machine Learning

Each block below is one kind of artifact we hold for Machine Learning. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

Features 8

The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.

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 give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.

JSON-LD contexts and semantic vocabularies used across these APIs.

Machine Learning Context

7 classes · 25 properties

JSON-LD

JSON Schema 2

Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.

Standalone JSON Schema definitions for this provider's data models.

InferenceRequest

8 properties

JSON SCHEMA

Model

15 properties

JSON SCHEMA

JSON Structure 2

JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.

JSON Structure definitions describing this provider's data shapes.

Machine Learning Inference Request Structure

8 properties

JSON STRUCTURE

Machine Learning Model Structure

15 properties

JSON STRUCTURE

Examples 2

Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.

Example request and response payloads for these APIs.

Machine Learning Inference Request Example

8 fields

EXAMPLE

Machine Learning Model Example

15 fields

EXAMPLE

Use Cases 8

What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.

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 tell you where this provider already fits in a stack.

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

Every other property we hold for Machine Learning — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

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