handymanServices & Tools
extensionCommon Features
extensionHosted 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 inference without managing GPUs.
extensionModel 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 foundation models on custom data.
extensionExperiment 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 deployment.
extensionVector 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 semantic search.
extensionModel 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.
extensionML Pipeline Orchestration
Kubeflow Pipelines, ZenML, and DVC expose APIs to define, version, and execute ML pipelines spanning data preparation, training, evaluation, and deployment stages.
extensionData 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.
extensionLLM 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.
task_altUse Cases
task_altRetrieval-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.
task_altFine-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 endpoint.
task_altScalable 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.
task_altEnd-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.
task_altMultimodal 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.
task_altSemantic 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.
task_altModel 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.
task_altDistributed 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.
integration_instructionsIntegrations
integration_instructionsHugging Face
Model hub and inference API hosting hundreds of thousands of open-source transformer models, datasets, and Spaces with managed Inference Endpoints.
integration_instructionsAmazon SageMaker
End-to-end ML platform on AWS for building, training, deploying, and monitoring models, including SageMaker Studio, JumpStart foundation models, and managed endpoints.
integration_instructionsGoogle Vertex AI
Unified ML platform on Google Cloud covering AutoML, custom training, Model Registry, Pipelines, and Generative AI Studio for foundation models like Gemini.
integration_instructionsMLflow
Open-source platform for ML lifecycle management with APIs for experiment tracking, model registry, and deployment across many backends.
integration_instructionsWeights & Biases
Experiment tracking, evaluations, model registry, and LLM observability platform with rich APIs for logging metrics and managing models.
integration_instructionsReplicate
API platform for running open-source models in the cloud with simple per-second pricing and one-line deployment of custom Cog containers.
integration_instructionsTogether AI
Inference and fine-tuning platform for open foundation models, exposing OpenAI-compatible APIs for chat, completion, and embeddings.
integration_instructionsPinecone
Managed vector database for high-scale similarity search, hybrid search, and metadata filtering powering production RAG applications.
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