Launches Flagship Product After Having Completed the Amazon Alexa Accelerator Program

Share Article, the first infrastructure-agnostic machine learning (ML) platform that enables data scientists to track, compare, monitor and optimize ML models, today announced it launched its flagship product and raised $2.3 million in seed funding., the first infrastructure-agnostic machine learning (ML) platform that enables data scientists to track, compare, monitor and optimize ML models, today announced it launched its flagship product and raised $2.3 million in seed funding. The investment was led by Trilogy Equity Partners, alongside Two Sigma Ventures, Founders Co-Op, Fathom Capital, Techstars Ventures and angel investors. The New York City-based company came out of the Amazon Alexa Accelerator program, powered by TechStars, and was created in partnership with Amazon’s Alexa fund in fall 2017.

Yuval Neeman, Partner at Trilogy Equity Partners who previously led Microsoft’s Developer division, will join’s Board of Directors and Matt Jacobus, Two Sigma's Former CTO and a Venture Partner at Two Sigma Ventures, will join as a board observer. will use the financing to expand its team of software engineers and data scientists to continue to develop its product. is the first platform built for ML that enables engineers and data scientists to efficiently maintain their preferred workflow and tools, while easily tracking previous work and collaborate throughout the iterative process. also optimizes models with bayesian hyperparameter optimization - a type of algorithm - which saves time typically spent on manual tuning ML models. As a result, users have increased visibility of data science and ML results and progress throughout an organization.

Machine learning is rapidly gaining traction as a powerful tool within organizations, and executives recognize that using ML to power artificial intelligence will be a key competitive advantage. However, most companies quickly realize that extracting value from ML teams can be challenging.

“Machine learning sits between software engineering and business intelligence, and as such requires different processes and methodologies - in software engineering, code is everything,” said Gideon Mendels, Founder and CEO of “However, in machine learning, code is just a small part of the process, as teams need to maintain more complex workflows, including datasets and models. This is where comes in - we optimize the machine learning process by providing the software that works with a customer’s existing infrastructure, and gives developers better machine learning models.”

Unlike other platforms, manages and monitors customers’ existing systems and infrastructure (e.g., existing code, databases, software and servers) instead of requiring customers to migrate their existing codebase, datasets and DevOps tools. Within 15 seconds and one line of code customers get started on To date, data scientists from 30+ leading industry companies and universities have developed over 6,000 ML models on the platform.

“At Trilogy, we look for exceptional founders seeking to solve a fundamental problem and from the first meeting with Gideon and Nimrod, we were struck by how smart, creative and pragmatic they were,” said Yuval Neeman, Partner at Trilogy Equity Partners s. “Data scientist and machine learning practitioners, while using a very different workflow than coding, still need tools similar to Github, Atlassian suite or Microsoft’s Visual Studio. We think enterprise customers will want to invest in making their hardest-to-find and highest-paid researchers more productive. We believe’s novel approach makes it a compelling product.”

The founding team, led by Gideon Mendels and Nimrod Lahav, Co-founder and Chief Technology Officer, previously worked in ML at Google and In their previous roles, they noticed many teams revert to emails and homebrewed solutions to manage their ML workflows; they recognized the need for a platform to help ML maintain complex datasets, parameters, experiments, and trained models.

"When our team saw that adding a single line of code could give us real-time metrics and charts detailing how our models converged while saving our experiments, we realized how quickly could accelerate the modeling process and catch on," said Matt Jacobus, Venture Partner, Two Sigma Ventures. "Every time you run an experiment, snapshots your model code, parameters, code, configuration, and tracks it, allowing data scientists to visualize and organize their work at virtually no cost." provides free and unlimited access to its platform for open source projects and academia to encourage reproducibility and knowledge sharing within the ML community.

About is the first infrastructure-agnostic machine learning (ML) platform that allows ML and data science teams to automatically track datasets, code changes, experimentation history and production models, creating efficiency, transparency, and reproducibility. Based in New York City, supercharges machine learning teams, helping them build better ML models and improve productivity.

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