We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”

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Blog » General » Neptune.ai Named to the 2022 CB Insights AI 100 List of Most Promising AI Startups

Neptune.ai Named to the 2022 CB Insights AI 100 List of Most Promising AI Startups

It’s been only a couple of weeks since I announced that we raised an $8M series A, and here I am with more good news. 

Neptune.ai has been named to the 2022 CB Insights AI 100 List of Most Promising AI Startups. We’ve been recognized in the experiment tracking and version control category. 

CB Insights Top 100

The CB Insights team picked 100 private market vendors from a pool of over 7,000 companies. They were chosen based on factors including R&D activity, proprietary Mosaic scores, market potential, business relationships, investor profile, news sentiment analysis, competitive landscape, team strength, and tech novelty.

There are a ton of great startups in the top 100 – congrats to all of them! I can’t help but notice that there are only a few companies from Europe, so I’m happy that Neptune is one of them. 

Good to see some of our customers and users on the list (e.g. the InstaDeep team, who we even had a chance to create a case study with). 

Being noticed by CB Insights is motivating but also important for Neptune.ai as a company. After we landed on the report for the first time last year, over 100 VCs from all over the world approached us. It helped us a lot in raising the last investment round.

And what does it mean for Neptune users? 

You can expect that we’ll use this recognition to develop our tool and create a better developer experience. That includes:   

  • quicker feedback-to-feature loops,
  • improved experience of our web UI,
  • more integrations with the tools from the MLOps ecosystem,
  • even better documentation.

These past few weeks have been especially good for us. But I’m confident we won’t slow down.

We’re going to work even harder to continue making experiment tracking and model registry “just work” for ML teams around the world. 


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InstaDeep Case Study: Looking for Collaboration Features and One Central Place for All Experiments

5 mins read | Updated November 22th, 2021

InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain™ platform, to tackle the most complex challenges across a range of industries. 

InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel’s AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.

About the BioAI team

The BioAI team is the place at InstaDeep where Biology meets Artificial intelligence. At BioAI, they advance healthcare and push the boundaries of medical science through a combination of biology and machine learning expertise. They are currently building DeepChain™, their platform for protein design. They are also working with their customers in the bio sector to tackle the most challenging problems with the help of bioinformatics and machine learning.

Deepchain dashboard
DeepChain dashboard | Source

They apply the DeepChain™ protein design platform to engineer new sequences for protein targets using sophisticated optimization techniques such as reinforcement learning and evolutionary algorithms. They also leverage Language Models pre-trained on millions of protein sequences and train their own in-house protein language models. Finally, they use machine learning to predict protein structure from sequence.

Problem

Building complex software like DeepChain™, a platform for protein design, requires a lot of research with different moving parts. Customers demand various types of solutions that require new experiments and research every time. With several experiments running for different customers, it will be unavoidably daunting for a team of any size to keep track of the experiments while ensuring they remain productive.

Fazed with the thought of managing numerous experiments, Nicolas and the BioAI team encountered a series of challenges:

  • 1Experiment logs were all over the place
  • 2It was difficult to share experiment results
  • 3Machine learning researchers were dealing with infrastructure and operations
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