Data scientists on a cloud with the new Machine Learning Lab from deepsense.ai
This September brings the premiere of the new Neptune, the Machine Learning Lab for all data scientists who value convenience and speed at work. Neptune not only supports the development of machine learning and deep learning models, but will also make their productionization as easy as posting pictures on Instagram!
deepsense.ai has announced the release of the new, enhanced version of Neptune – a powerful toolkit that enables data scientists to conveniently build machine learning models in a user-friendly environment. Neptune minimizes time-consuming, complex technology stack and infrastructure management and allows data scientists to choose frameworks they prefer, including Keras, Tensorflow, Pytorch and others. What’s more, this year Neptune is going to offer a widely anticipated functionality – model deployment and maintenance which will let data science teams work even more efficiently and save some sleepless nights.
The recent version of Neptune focuses on making data scientists’ work as convenient as possible. The platform features interactive prototyping with Jupyter Notebooks and enables easy machine learning experiment tracking and reproduction – all done in popular public clouds. Neptune supports agile collaboration in teams and offers a polished UI with friendly charts that make it easy to monitor model training, instead of analyzing logs. According to deepsense.ai CTO Piotr Niedźwiedź, who has led the platform’s development, “Neptune is like a Kaggle platform for every-day challenges – users can share and compare their results in leaderboards and choose the best models for further development and deployment. It works great for individual data scientists as well as for whole teams”.
Neptune features interactive prototyping with Jupyter Notebooks and enables easy machine learning experiment tracking and reproduction – all done in popular public clouds.
deepsense.ai launched an early-access version of Neptune a year ago at the Strata+Hadoop World conference in New York. Over the year almost three hundred early adopters were invited to test the platform and contribute to its development. Niedźwiedź adds, “From the beginning it was crucial for us to build a tool that would address data scientists’ current and future needs. So we carefully followed our early adopter community feedback and worked out a holistic, state-of-the-art approach for Neptune. It’s now a platform that perfectly serves modern data scientists, who can finally focus on delivering best-in-class results while working with any technology and library they could dream of”
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