Success! You're on the list.

Success! You're on the list.

Github for machine learning, raises $8M Series A funding

In a recent development,, which builds developer tools for machine learning practitioners, raised $8 million in a Series A funding. The financing round was led by global VC firm Almaz Capital also invested recently in Refurbed and Minut, alongside participation from previous investors btov Partners, Rheingau Founders, and TDJ Pitango.

With the latest funding, the total investment raised by is close to $13 million since its inception. The startup will use the latest investment to deliver an even better experience for developers by rapidly expanding its product and engineering teams across Europe.

Neptune’s platform is used the most by machine learning engineers and corporate customers of over 100 paying companies, including The New Yorker, InstaDeep and Roche. 

In an exclusive interview with TFN, Jakub Czakon, one of the co-founders and CMO of, says, “Currently, we have around 20k-25k users, including data scientists and machine learning engineers, and we’re growing at about 4x a year. We want to capitalise on mid-market, and for that, we aim to focus on the developer’s experience. At the same time, we are concentrating on Neptune’s hiring and also the product side.”

The company plans to grow its customer base further and aims to become the most-used tool for globally storing and organising model metadata. At the same time, with a total headcount of over 30 right now, according to Czakon, Neptune plans to grow double the team size by 2023.

Backstory of was founded in 2017 in Warsaw by Piotr Niedźwiedź, Jakub Czakon, Paulina Prachnio and Piotr Łusakowski, who met in deepsense. 

The startup’s story debuted when they won Kaggle’s Right Whale Recognition competition as a part of the team.

Piotr Niedzwiedz is an accomplished competitive programmer (top 30 worldwide in Google Code Jam 2009) and a serial entrepreneur who founded two successful software companies Codilime (Software-defined Networks) and (Machine learning). 

While most companies in the MLOps space try to go wider and become platforms that solve all the problems of machine learning teams,’s strategy is to go deeper and become the best-in-class tool for model metadata storage and management. 

Piotr Niedzwiedz explains, “In a more mature software development space, there are almost no end-to-end platforms. So why should machine learning, which is even more complex, be any different? We believe that by focusing on providing the best developer experience for experiment tracking and model registry, we can become the foundation of any MLOps tool stack.” 

Github for machine learning!

With, one can replace folder structures, spreadsheets, and naming conventions with a single source of truth where all the model building metadata is organised, easy to find, share, and query.  

The company not only manages the infrastructure needed to log and store the metadata, but also provides a central place for teams to view, organise, share, compare, query and collaborate on all metadata generated during an entire machine learning lifecycle. With this tool, companies can get the most out of their resources by keeping a record of all the paths they have taken and it automatically creates versions to backtrack failed runs. Eventually, these help engineers establish the dataset and parameters that were used, and help them to recover quickly.

Just as Github transformed how software engineers log, store, manage and share their code, is on a mission to become the Github for machine learning.

Talking about the same, Czakon adds “When you create applications, you write code to create those applications, and there are different new features, you’re adding, collaborating with different people and different things and then you need to place to sort of like combine it all together, right, and keep those different versions of code that produce different versions of applications. So in software, many tools make this process very neat, clean, manageable, you know, under control, and in machine learning.”

He added, “That’s where Neptune comes in. We focus on managing all of those different metadata risks created as you build the models.”

Piotr Niedzwiedz, co-founder of, adds: “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 confidently. So when ML engineers at my previous company showed me a tool they built for experiment tracking, I knew it had massive potential. Fast forward to today, and we are one of the most popular tools for experiment tracking and model registry on the market. Thanks to the backing of Almaz Capital and our other investors we will continue building a better product that machine-learning engineers and data scientists can use far into the future.”

Commenting on the investment, Pavel Bogdanov, general partner at lead investor Almaz Capital, said: “As more companies adopt machine learning, the demand for tools that help operationalise and control model development and deployment grew substantially in 2021. Yet the world is still at the infancy of machine learning adoption, and we expect the MLOps market only to grow from here. What we liked about the team was the clear vision to create the best-in-class, foundational component of the MLOps tool stack instead of trying to solve this problem end to end. With a fast-growing customer base and a focus on providing the best developer experience, we believe they can become the go-to solution for model metadata management for machine learning teams everywhere.”

Daniel Star, managing partner TDJ Pitango Ventures, adds: “The MLOps tools market is rapidly evolving from individual experiments to group collaboration. Neptune today provides the best toolkit for machine learning development teams to collaborate and achieve better outcomes. We have been with Neptune since 2018 and I am proud of what this team has achieved. We strongly believe in further scaling of the business, especially with such a dynamically growing global market.”

Ronert Obst, Head of Data Science at international retailer, the New Yorker, said: “What we like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualising the output adds a lot of value for us.”

Related Posts

Get daily funding news briefings in the tech world delivered right to your inbox.

Enter Your Email
join our newsletter. thank you