Neptune.ai raises $8M to streamline ML model development


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Neptune.ai, a Polish startup that helps companies manage model metadata, today announced it has raised $8 million in Series A funding.

When an organization experiments with machine learning (ML) models, each iteration they go through results in metadata such as references and insights from the datasets being used, code versions, environmental changes, hardware, evaluation and testing metrics, and predictions. This information is constantly evolving, leaving a complex trail of version history. So if something goes wrong, it becomes incredibly difficult for the ML engineers to figure out what caused the problem and when.

“When I came to machine learning from software engineering, I was surprised by the messy experimentation practices, lack of control over model building, and a missing ecosystem of tools to help people deliver models with confidence. It was a stark contrast to the software development ecosystem, where you have mature tools for devops, observability or orchestration to work in production,” Piotr Niedźwiedź, founder of the Neptune.ai, told Venturebeat.

To solve the challenge, Niedźwiedź has removed Neptune.ai from its previous company, giving companies a dedicated metadata store that provides a central place to log, store, display, organize, share all metadata , compare and query during the lifecycle of a machine learning model .

The repository, the founder said, allows ML developers to easily go back to ML experiments and have full control over their model development efforts — without having to worry about dealing with folder structures, cumbersome spreadsheets, and naming conventions common today. . It provides enterprises with unprecedented visibility into the evolution of their models and also saves time and money by automating metadata accounting.

Previously, companies had to hire additional people to deploy loggers, maintain databases, or teach people how to use them.

Grow

Since its launch, Neptune.ai has attracted more than 20,000 ML engineers and 100 commercial customers, including Roche, NewYorker, Nnaisense and InstaDeep. Use of the platform has increased eightfold in the past eight months, while revenue has quadrupled, the founder said.

However, it is not the only player offering tools to help artificial intelligence (AI) developers. Commercial and open source platforms such as Weights and Biases, TensorBoard and Comet also operate in the same space, allowing companies to track, compare and reproduce their ML experiments.

“Neptune wins (against these platforms) on flexibility and customizability, great developer experience, and focus on solving one part of the MLops stack (model metadata management) really in-depth,” Niedźwiedź noted.

“While most companies in the MLops space are trying to go broader and become platforms that solve all the problems of ML teams, we want to go deeper and become the best-in-class component for model metadata storage and management” , he added.

The latest funding round, led by Almaz Capital, will help the company achieve this goal. It will expand its product and engineering teams to further improve metadata storage and increase the workflows of ML engineers and data scientists.

In the coming months, Niedźwiedź said, the plan is to focus on improving the platform’s organization, visualization and comparison capabilities for specific machine learning verticals, including computer vision, time series prediction and reinforcement learning, as well as supporting of core model use cases and creating more integrations with tools in the MLops ecosystem.

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This post Neptune.ai raises $8M to streamline ML model development

was original published at “https://venturebeat.com/2022/04/12/neptune-ai-raises-8m-to-streamline-ml-model-development/”

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