Ultimate optimisation de modèle Solutions for Everyone

Discover all-in-one optimisation de modèle tools that adapt to your needs. Reach new heights of productivity with ease.

optimisation de modèle

  • Create and deploy machine learning models with ApXML's automated workflows.
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    What is ApX Machine Learning?
    ApXML offers automated workflows for building and deploying machine learning models, making it easier for users to work with tabular data analysis, predictions, and custom language models. With comprehensive courses, fine-tuning capabilities, model deployment via APIs, and access to powerful GPUs, ApXML combines knowledge and tools to support users at every stage of their machine learning journey.
  • A platform to prototype, evaluate, and improve LLM applications rapidly.
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    What is Inductor?
    Inductor.ai is a robust platform aimed at empowering developers to build, prototype, and refine Large Language Model (LLM) applications. Through systematic evaluation and constant iteration, it facilitates the development of reliable, high-quality LLM-powered functionality. With features like custom playgrounds, continuous testing, and hyperparameter optimization, Inductor ensures that your LLM applications are always market-ready, streamlined, and cost-effective.
  • LossLens AI is an AI-powered assistant analyzing machine learning training loss curves to diagnose issues and suggest hyperparameter improvements.
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    What is LossLens AI?
    LossLens AI is an intelligent assistant designed to help machine learning practitioners understand and optimize their model training processes. By ingesting loss logs and metrics, it generates interactive visualizations of training and validation curves, identifies divergence or overfitting issues, and provides natural language explanations. Leveraging advanced language models, it offers context-aware hyperparameter tuning suggestions and early stopping advice. The agent supports collaborative workflows through a REST API or web interface, enabling teams to iterate faster and achieve better model performance.
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