The ML Developer Platform

About Us

Euclidn accelerates AI developers in creating superior models with speed. Effortlessly monitor experiments, version data, iterate on datasets, assess model performance, replicate models, and comprehensively oversee your end-to-end ML workflows.

Streamline your full ML workflow

In today's data-driven world, efficiently managing machine learning workflows is critical for achieving meaningful results. Our platform is designed to streamline your entire ML workflow from start to finish. Whether you're a seasoned data scientist or just starting in AI, our user-friendly tools make it easy to manage data, train models, and monitor performance. We provide a seamless experience, allowing you to focus on the core aspects of your AI projects while our platform takes care of the rest

With our platform, you can effortlessly track experiments, manage datasets, evaluate model performance, and reproduce successful models. We understand the importance of efficiency in ML development, and our tools are tailored to boost your productivity. Embrace the future of AI development with confidence, knowing that our platform has your back, simplifying complex workflows and ensuring you achieve your machine learning goals faster than ever before



What sets Euclidn apart


Seamless integration with automatic tracking and versioning.

  • Simplify your workflow: Achieve efficient tracking, versioning, and visualization of your work using only five lines of code, making project management hassle-free.
  • Model consistency: Ensure reliability by effortlessly reproducing any model checkpoints, maintaining consistency and accuracy throughout your AI projects.
  • Real-time performance monitoring: Stay in control with real-time CPU and GPU usage monitoring, optimizing resource allocation and enhancing overall project performance.
Business data analysis

Transform your data into actionable insights through visualization

  • Centralized Visualization: Access live metrics, datasets, logs, code, and system stats from a centralized location, providing a comprehensive view of your project's performance.
  • Collaborative Analysis: Foster collaborative analysis within your team to collectively uncover key insights, promoting a deeper understanding of the data and enhancing decision-making.
  • Efficient Development: Compare and debug with ease, while the iterative building process accelerates project development, enabling teams to make informed decisions and drive progress effectively.
Performance Skill Experience Accomplishment Concept

Enhance performance to instill confidence in your evaluation and deployment processes

  • Collaborative Model Experimentation: Teams work together to experiment and find the best-performing model for their projects, fostering collective learning and improvement.
  • Comprehensive Model Evaluation: Evaluate models, discuss and resolve bugs, and demonstrate consistent progress, ensuring the quality and reliability of your AI projects.
  • Transparent Reporting: Keep stakeholders informed with customizable reports, promoting transparency and effective communication throughout the development cycle.

Simplify your full ML workflow with.........



Monitoring and recording the progress and results of experiments



Shared data visualizations and insights accessible to multiple users, facilitating team collaboration and decision-making



Systematic tracking and management of data and model versions to ensure data integrity and reproducibility in AI projects



Dynamic and user-friendly data representations that allow users to explore and analyze data interactively, gaining deeper insights



The process of fine-tuning the parameters that define the behavior of machine learning algorithms to achieve optimal model performance



Streamlining and executing various machine learning processes and tasks without manual intervention, improving efficiency and productivity



Comprehensive oversight of a machine learning model's development, deployment, monitoring, and maintenance throughout its entire lifecycle



Continuous monitoring and analysis of machine learning models in production to ensure performance, reliability, and quick issue detection and resolution



A tool that enables the creation of user-friendly and interactive machine learning applications without extensive coding knowledge

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