About neptune.ai

Provides a scalable experiment tracking platform designed for training foundation models, enabling real-time monitoring, visualization of massive datasets, and precise comparison of thousands of metrics without downsampling. It addresses inefficiencies in existing tools by ensuring 100% data accuracy, responsive UI for large-scale runs, and features like run forking to optimize resource usage and reduce training costs.

<problem> Training foundation models involves monitoring a massive number of metrics, including those at the layer level, which can be difficult to visualize and analyze efficiently with existing experiment trackers. Current tools often lack the responsiveness and accuracy needed to handle the scale of data generated during these training runs, leading to missed errors and wasted resources. </problem> <solution> Neptune provides a scalable experiment tracking platform designed specifically for training foundation models, enabling real-time monitoring and debugging. The platform ensures 100% data accuracy without downsampling, offering a responsive UI for large-scale runs and the ability to visualize thousands of metrics in milliseconds. With features like forking of runs, users can test multiple configurations simultaneously and restart failed training sessions from any previous step, optimizing resource usage and reducing training costs. Neptune's architecture is built for maximum scalability, ingesting 100k data points per second asynchronously, and can be deployed on-premises or in a private cloud to maintain data security. </solution> <features> - Responsive web app for rendering large (100k+) runs tables and comparing thousands of metrics on a single chart - No data downsampling, ensuring 100% accurate visualizations - Forking of runs to test multiple configs and restart failed training sessions from any saved step - Ability to track metrics across all layers to isolate and address issues quickly, such as vanishing or exploding gradients - Asynchronous data ingestion (based on Kafka) capable of ingesting 100k data points per second - Native API and 30+ integrations with training frameworks (PyTorch, TensorFlow, Keras), HPO frameworks (Optuna), and automation frameworks (Apache Airflow, Kedro, ZenML) - Self-hosted deployment options for on-premises or private cloud environments - Role-based access control (RBAC) and SSO authentication for secure collaboration </features> <target_audience> The primary users are AI researchers, ML team leads, and ML platform engineers involved in training foundation models, as well as enterprises and academic researchers. </target_audience>

What does neptune.ai do?

Where is neptune.ai located?

neptune.ai is based in Warsaw, Poland.

When was neptune.ai founded?

neptune.ai was founded in 2017.

How much funding has neptune.ai raised?

neptune.ai has raised 18700000.

Location

Warsaw, Poland

Founded

2017

Funding

18700000

Employees

90 employees

Major Investors

Almaz Capital

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Executive Summary

neptune.ai 30K+

Crunchbase

Founded 2017 – Warsaw, Poland

Funding

Estimated Funding

$10M+

Major Investors

Almaz Capital

Team (75+)

No team information available.

Company Description

Problem

Training foundation models involves monitoring a massive number of metrics, including those at the layer level, which can be difficult to visualize and analyze efficiently with existing experiment trackers. Current tools often lack the responsiveness and accuracy needed to handle the scale of data generated during these training runs, leading to missed errors and wasted resources.

Solution

Neptune provides a scalable experiment tracking platform designed specifically for training foundation models, enabling real-time monitoring and debugging. The platform ensures 100% data accuracy without downsampling, offering a responsive UI for large-scale runs and the ability to visualize thousands of metrics in milliseconds. With features like forking of runs, users can test multiple configurations simultaneously and restart failed training sessions from any previous step, optimizing resource usage and reducing training costs. Neptune's architecture is built for maximum scalability, ingesting 100k data points per second asynchronously, and can be deployed on-premises or in a private cloud to maintain data security.

Features

Responsive web app for rendering large (100k+) runs tables and comparing thousands of metrics on a single chart

No data downsampling, ensuring 100% accurate visualizations

Forking of runs to test multiple configs and restart failed training sessions from any saved step

Ability to track metrics across all layers to isolate and address issues quickly, such as vanishing or exploding gradients

Asynchronous data ingestion (based on Kafka) capable of ingesting 100k data points per second

Native API and 30+ integrations with training frameworks (PyTorch, TensorFlow, Keras), HPO frameworks (Optuna), and automation frameworks (Apache Airflow, Kedro, ZenML)

Self-hosted deployment options for on-premises or private cloud environments

Role-based access control (RBAC) and SSO authentication for secure collaboration

Target Audience

The primary users are AI researchers, ML team leads, and ML platform engineers involved in training foundation models, as well as enterprises and academic researchers.