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Observabilidad y monitoreo de LLM de código abierto
Gratis
Sagify
Sagify accelerates machine learning and LLM deployment on AWS SageMaker with minimal configuration. Streamline training, tuning, and deployment using a unified, no-code-friendly interface.
About Sagify
Simplificación de la implementación del aprendizaje automático
Sagify es una herramienta intuitiva para desarrolladores que simplifica la creación e implementación de aplicaciones de aprendizaje automático (ML) y modelos de lenguaje grandes (LLM) en AWS SageMaker. Ofrece una interfaz de línea de comandos clara y una estructura modular para que los usuarios puedan centrarse en el desarrollo de modelos, no en la infraestructura.
Designed for ML Engineers and Data Teams
Whether you’re a solo developer, part of a data science team, or building AI products at scale, Sagify offers a practical framework to move from prototype to production faster, without managing low-level cloud configurations.
Core Capabilities of Sagify
Del código al modelo implementado en un día
Sagify lets you train, tune, and deploy models with a single command. You only need to write your model logic—Sagify takes care of provisioning, scaling, hyperparameter tuning, and deployment to AWS SageMaker.
Puerta de enlace unificada para modelos de lenguaje grandes
Sagify includes an LLM Gateway that connects to both proprietary models (like OpenAI or Anthropic) and open-source models (like LLaMA or Stable Diffusion). This lets you use different models via a single REST API, reducing integration overhead.
Machine Learning Automation on AWS
Integración completa con AWS SageMaker
Sagify deeply integrates with SageMaker, allowing automated Docker builds, training jobs, model deployments, and batch inference through simple CLI commands. It supports spot instances, resource tagging, and hyperparameter optimization.
One-Line Deployment of Foundation Models
Puede implementar Hugging Face, OpenAI o modelos de base personalizados mediante plantillas predefinidas, sin necesidad de escribir código ni configurar la infraestructura manualmente.
LLM Infrastructure Without the Headaches
RESTful API for LLMs
LLM Gateway ofrece una interfaz consistente para enviar solicitudes, recibir finalizaciones, generar imágenes o extraer incrustaciones entre múltiples proveedores. Esto es ideal para aplicaciones que necesitan cambiar o probar el rendimiento de LLM sin reescribir la lógica del backend.
Local and Cloud Hosting Options
Sagify supports running the LLM Gateway locally via Docker or deploying it to AWS Fargate. This flexibility allows you to prototype locally and scale in production effortlessly.
Advanced ML Use Cases
Batch Inference for High-Volume Workflows
Sagify supports large-scale batch processing of ML or embedding jobs using S3 and AWS SageMaker. Ideal for recommendation systems, search indexing, and offline predictions.
Optimización de hiperparámetros incorporada
With support for Bayesian optimization, you can fine-tune your models for better performance. Sagify provides all the tools needed to define parameter ranges, set objectives, and monitor results directly through AWS.
Herramientas para desarrolladores y extensibilidad
SDK y CLI
Sagify includes both a Python SDK and a full-featured CLI. This dual interface allows you to automate workflows within your apps or manage experiments interactively from the terminal.
Arquitectura modular para personalización
The tool is built around a modular structure, making it easy to replace or extend components such as model logic, endpoints, or training configurations without affecting the overall pipeline.