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Observabilidade e monitoramento de LLM de código aberto
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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
Simplificando a implantação do aprendizado de máquina
O Sagify é uma ferramenta amigável ao desenvolvedor que elimina a complexidade de criar e implantar aplicativos de aprendizado de máquina (ML) e modelos de linguagem de grande porte (LLM) no AWS SageMaker. Ele oferece uma interface de linha de comando limpa e uma estrutura modular para que os usuários possam se concentrar no desenvolvimento do modelo, e não na infraestrutura.
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
Do código ao modelo implantado em um dia
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.
Gateway unificado para grandes modelos de linguagem
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
Integração completa com 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
Você pode implantar o Hugging Face, o OpenAI ou modelos de base personalizados usando modelos predefinidos — não há necessidade de escrever código ou configurar a infraestrutura manualmente.
LLM Infrastructure Without the Headaches
RESTful API for LLMs
O LLM Gateway oferece uma interface consistente para enviar prompts, receber conclusões, gerar imagens ou extrair embeddings entre vários provedores. Isso é ideal para aplicativos que precisam alternar ou testar o desempenho do LLM sem reescrever a lógica do 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.
Otimização de hiperparâmetros integrada
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.
Ferramentas de desenvolvedor e extensibilidade
SDK e 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.
Arquitetura Modular para Personalização
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.