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

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

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

When an organization conducts experiments with machine learning (ML) models, each iteration it passes produces metadata such as references and insights from the datasets used, code versions, environment changes, hardware, evaluation and testing metrics, and predictions. This information is constantly evolving, leaving a complex trace of the history of transcription. Therefore, when something goes wrong, it becomes very difficult for ML engineers to de-identify the cause of the problem and when.

“When I came into machine learning from software engineering, I was surprised by the messy experimentation practices, the lack of control over building models and the missing ecosystem of tools to help people submit models with confidence. It was a stark contrast to the system,” said Piotr Niedźwiedź, founder of Neptune.ai, to Venturebeat. The software development environment, where you have mature tools to develop, observe, or coordinate to work in production.

To solve the challenge, Niedźwiedź has taken Neptune.ai out of its previous company, providing organizations with a dedicated metadata store that provides a central place to record, store, display, organize, share, compare, and query all metadata generated during the lifecycle of a machine learning model. .

Neptune
Neptune dashboard

The founder said that the repository enables ML developers to easily fall back on ML experiments and have complete control over their model development efforts — without worrying about dealing with impractical folder structures, spreadsheets, and common naming conventions today. It provides organizations with unprecedented insight into the evolution of their models and also saves time and money by automating metadata bookkeeping.

Previously, companies had to hire more people to implement logging tools, maintain databases, or teach people how to use them.

growth

Since its launch, Neptune.ai has connected more than 20,000 ML engineers and 100 commercial clients, including Roche, NewYorker, Nnaisense and InstaDeep. The founder said that usage of the platform has grown eightfold over the past eight months while revenue has gone up fourfold.

However, it is not the only player that offers tools to help artificial intelligence (AI) developers. Commercial and open source platforms such as Weights and Biases, TensorBoard, and Comet are also active in the same space, helping companies track, compare, and reproduce their ML experiences.

Niedźwiedź noted that “Neptune wins (against these platforms) in flexibility and customizability, great developer experience and focus on a single component solution of the MLops (Model Metadata Management) stack in depth.”

“While most companies in the MLops space are trying to scale at scale to become platforms that solve all the problems of machine learning teams, we want to go deeper and become the best-in-class component for storing and managing exemplary metadata,” he added.

The latest funding round, led by Almaz Capital, will help the company advance towards this goal. It will grow its production and engineering teams to further improve the metadata store and increase the workflow of ML engineers and data scientists.

Nidodio said the plan will focus in the coming months on improving the platform’s organization and visualization and comparison capabilities for specific sectors of machine learning, including computer vision, time-series prediction, and reinforcement learning, as well as supporting the base model’s log use cases and creating more integration with tools in the ecosystem. MLops.

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