The probability that two data scientists use the same tools is nearly zero. They like what they like, and across a team they might use every tool in the book: open-source analytics engines, various languages and frameworks, and many editors and IDEs. It can be difficult to get them to give up their favorite workflows, and it can be an even bigger burden to ask them to adopt an entirely new heavier-weight platform for model development and collaboration.
Just like everyone else, data scientists want to use the tools that make them the most productive. The problem is, as an organization scales, it’s unrealistic to have developers and data scientists across machine learning teams using homebrewed solutions and reverting to email to manage complex machine workflows, datasets, and production models.
While some organizations may get great mileage out of tightly integrated workflow solutions and standalone platforms, we’ve seen that in other cases machine learning teams just need better tools that match their preferred workflows — not entirely new ones. Some data scientists and developers love the flexibility of different languages, tools, and workflows, which is why we were so excited when we first met Gideon, Nimrod, and the team at Comet.ml.
Comet.ml is the first infrastructure- and workflow-agnostic machine learning platform. With 15 seconds and one line of code, Comet.ml seamlessly integrates into sets of disparate tools and libraries, allowing teams to quickly start tracking their experiments and optimizing their models without compromising on the flexibility and fluidity of their preferred workflows.
Every time you run an experiment, Comet snapshots your model code, parameters, code, configuration, and tracks it, allowing data scientists to visualize and organize their work at virtually no cost. Comet is dedicated to automating large parts of the machine-learning process, from tracking and comparing experiments to collaboration across teams to optimizing hyperparameters — with no heavyweight platform or workflow changes required.
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 Comet could accelerate the modeling process and catch on. This technology, and the amazing team that is building it, is why we’re excited to announce we’re investing in Comet.ml’s $2.3M seed round alongside Trilogy Ventures, Founders Co-Op, Fathom Capital, Techstars Ventures, and other angel investors.