aid: machine-learning name: Machine Learning kind: topic description: >- 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. type: Index image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg tags: - Machine Learning - MLOps - Model Serving - Inference - AutoML - Embeddings - Vector Database - Foundation Models url: >- https://raw.githubusercontent.com/api-evangelist/machine-learning/refs/heads/main/apis.yml created: '2026-05-19' modified: '2026-05-19' specificationVersion: '0.19' apis: [] network: - name: Amazon SageMaker url: https://raw.githubusercontent.com/api-evangelist/amazon-sagemaker/refs/heads/main/apis.yml - name: Anyscale url: https://raw.githubusercontent.com/api-evangelist/anyscale/refs/heads/main/apis.yml - name: Azure Databricks url: https://raw.githubusercontent.com/api-evangelist/azure-databricks/refs/heads/main/apis.yml - name: Azure Machine Learning url: https://raw.githubusercontent.com/api-evangelist/microsoft-azure-machine-learning/refs/heads/main/apis.yml - name: Baseten url: https://raw.githubusercontent.com/api-evangelist/baseten/refs/heads/main/apis.yml - name: ClearML url: https://raw.githubusercontent.com/api-evangelist/clearml/refs/heads/main/apis.yml - name: Chroma url: https://raw.githubusercontent.com/api-evangelist/chroma/refs/heads/main/apis.yml - name: Comet url: https://raw.githubusercontent.com/api-evangelist/comet-ml/refs/heads/main/apis.yml - name: DagsHub url: https://raw.githubusercontent.com/api-evangelist/dagshub/refs/heads/main/apis.yml - name: Databricks url: https://raw.githubusercontent.com/api-evangelist/databricks/refs/heads/main/apis.yml - name: Dataiku url: https://raw.githubusercontent.com/api-evangelist/dataiku/refs/heads/main/apis.yml - name: DeepInfra url: https://raw.githubusercontent.com/api-evangelist/deepinfra/refs/heads/main/apis.yml - name: Determined AI url: https://raw.githubusercontent.com/api-evangelist/determined-ai/refs/heads/main/apis.yml - name: DVC url: https://raw.githubusercontent.com/api-evangelist/dvc/refs/heads/main/apis.yml - name: Fireworks AI url: https://raw.githubusercontent.com/api-evangelist/fireworks-ai/refs/heads/main/apis.yml - name: Google Vertex AI url: https://raw.githubusercontent.com/api-evangelist/google-vertex-ai/refs/heads/main/apis.yml - name: Groq url: https://raw.githubusercontent.com/api-evangelist/groq/refs/heads/main/apis.yml - name: Hugging Face url: https://raw.githubusercontent.com/api-evangelist/hugging-face/refs/heads/main/apis.yml - name: Hyperbolic url: https://raw.githubusercontent.com/api-evangelist/hyperbolic/refs/heads/main/apis.yml - name: Jina AI url: https://raw.githubusercontent.com/api-evangelist/jina-ai/refs/heads/main/apis.yml - name: KServe url: https://raw.githubusercontent.com/api-evangelist/kserve/refs/heads/main/apis.yml - name: Kubeflow url: https://raw.githubusercontent.com/api-evangelist/kubeflow/refs/heads/main/apis.yml - name: Label Studio url: https://raw.githubusercontent.com/api-evangelist/label-studio/refs/heads/main/apis.yml - name: Lepton AI url: https://raw.githubusercontent.com/api-evangelist/lepton-ai/refs/heads/main/apis.yml - name: LiteLLM url: https://raw.githubusercontent.com/api-evangelist/litellm/refs/heads/main/apis.yml - name: Marqo url: https://raw.githubusercontent.com/api-evangelist/marqo/refs/heads/main/apis.yml - name: Microsoft Azure AI Foundry url: https://raw.githubusercontent.com/api-evangelist/azure-ai-foundry/refs/heads/main/apis.yml - name: Milvus url: https://raw.githubusercontent.com/api-evangelist/milvus/refs/heads/main/apis.yml - name: MLflow url: https://raw.githubusercontent.com/api-evangelist/mlflow/refs/heads/main/apis.yml - name: Modal url: https://raw.githubusercontent.com/api-evangelist/modal/refs/heads/main/apis.yml - name: Neptune.ai url: https://raw.githubusercontent.com/api-evangelist/neptune-ai/refs/heads/main/apis.yml - name: Novita AI url: https://raw.githubusercontent.com/api-evangelist/novita-ai/refs/heads/main/apis.yml - name: OpenPipe url: https://raw.githubusercontent.com/api-evangelist/openpipe/refs/heads/main/apis.yml - name: Pinecone url: https://raw.githubusercontent.com/api-evangelist/pinecone/refs/heads/main/apis.yml - name: Portkey url: https://raw.githubusercontent.com/api-evangelist/portkey/refs/heads/main/apis.yml - name: Qdrant url: https://raw.githubusercontent.com/api-evangelist/qdrant/refs/heads/main/apis.yml - name: Ray url: https://raw.githubusercontent.com/api-evangelist/ray/refs/heads/main/apis.yml - name: Replicate url: https://raw.githubusercontent.com/api-evangelist/replicate/refs/heads/main/apis.yml - name: SiliconFlow url: https://raw.githubusercontent.com/api-evangelist/siliconflow/refs/heads/main/apis.yml - name: Together AI url: https://raw.githubusercontent.com/api-evangelist/together-ai/refs/heads/main/apis.yml - name: TrueFoundry url: https://raw.githubusercontent.com/api-evangelist/truefoundry/refs/heads/main/apis.yml - name: Typesense url: https://raw.githubusercontent.com/api-evangelist/typesense/refs/heads/main/apis.yml - name: Vespa url: https://raw.githubusercontent.com/api-evangelist/vespa/refs/heads/main/apis.yml - name: vLLM url: https://raw.githubusercontent.com/api-evangelist/vllm/refs/heads/main/apis.yml - name: Weaviate url: https://raw.githubusercontent.com/api-evangelist/weaviate/refs/heads/main/apis.yml - name: Weights & Biases url: https://raw.githubusercontent.com/api-evangelist/weights-and-biases/refs/heads/main/apis.yml - name: ZenML url: https://raw.githubusercontent.com/api-evangelist/zenml/refs/heads/main/apis.yml - name: Zilliz url: https://raw.githubusercontent.com/api-evangelist/zilliz/refs/heads/main/apis.yml common: - type: Portal url: https://apievangelist.com - type: GitHubOrganization url: https://github.com/api-evangelist - type: Features data: - name: Hosted Model Inference description: 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 inference without managing GPUs. - name: Model Training and Fine-Tuning description: Platforms like Amazon SageMaker, Google Vertex AI, Azure Machine Learning, and OpenPipe expose APIs for launching training jobs, configuring hyperparameters, and fine-tuning foundation models on custom data. - name: Experiment Tracking and Model Registry description: MLflow, Weights & Biases, Comet, Neptune.ai, and ClearML provide APIs to log experiments, track metrics, compare runs, and register approved model versions for downstream deployment. - name: Vector Search and Embeddings description: Vector databases like Pinecone, Weaviate, Milvus, Qdrant, and Chroma expose APIs to index embeddings and run nearest-neighbor search powering retrieval-augmented generation and semantic search. - name: Model Serving and Deployment description: 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. - name: ML Pipeline Orchestration description: Kubeflow Pipelines, ZenML, and DVC expose APIs to define, version, and execute ML pipelines spanning data preparation, training, evaluation, and deployment stages. - name: Data Labeling and Annotation description: Label Studio and similar platforms expose APIs for managing labeling projects, importing data, assigning tasks to annotators, and exporting labeled datasets for model training. - name: LLM Gateway and Routing description: LiteLLM, Portkey, and similar gateways provide unified APIs that route requests across multiple LLM providers with fallback, caching, rate limiting, and observability. - type: UseCases data: - name: Retrieval-Augmented Generation description: Combining a vector database (Pinecone, Weaviate, Qdrant) with an embeddings API and an LLM inference endpoint to ground model responses in private knowledge bases. - name: Fine-Tuning Foundation Models description: 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 endpoint. - name: Scalable Model Inference at the Edge description: Deploying optimized models through Groq, Modal, Replicate, or Baseten to serve high-throughput, low-latency inference for chatbots, recommendation systems, and content generation. - name: End-to-End MLOps Automation description: Using Kubeflow, MLflow, Weights & Biases, and ZenML to track experiments, register approved models, trigger retraining, and promote models to production via API. - name: Multimodal Application Development description: Composing image, audio, video, and text models from Hugging Face, Replicate, and Fireworks AI through standard inference APIs to build multimodal user experiences. - name: Semantic Search and Recommendations description: Indexing product catalogs, documents, or media in vector databases like Milvus or Vespa and exposing semantic search APIs to power discovery and personalization. - name: Model Observability and Cost Control description: Using gateways like Portkey and LiteLLM alongside observability platforms to monitor inference latency, cost-per-request, and routing decisions across multiple model providers. - name: Distributed Training at Scale description: Running large-scale distributed training jobs on Ray, Anyscale, Determined AI, or Databricks via API, including hyperparameter tuning and GPU cluster orchestration. - type: Integrations data: - name: Hugging Face description: Model hub and inference API hosting hundreds of thousands of open-source transformer models, datasets, and Spaces with managed Inference Endpoints. - name: Amazon SageMaker description: End-to-end ML platform on AWS for building, training, deploying, and monitoring models, including SageMaker Studio, JumpStart foundation models, and managed endpoints. - name: Google Vertex AI description: Unified ML platform on Google Cloud covering AutoML, custom training, Model Registry, Pipelines, and Generative AI Studio for foundation models like Gemini. - name: MLflow description: Open-source platform for ML lifecycle management with APIs for experiment tracking, model registry, and deployment across many backends. - name: Weights & Biases description: Experiment tracking, evaluations, model registry, and LLM observability platform with rich APIs for logging metrics and managing models. - name: Replicate description: API platform for running open-source models in the cloud with simple per-second pricing and one-line deployment of custom Cog containers. - name: Together AI description: Inference and fine-tuning platform for open foundation models, exposing OpenAI-compatible APIs for chat, completion, and embeddings. - name: Pinecone description: Managed vector database for high-scale similarity search, hybrid search, and metadata filtering powering production RAG applications. maintainers: - FN: Kin Lane email: kin@apievangelist.com humanURL: https://apievangelist.com