Details
Studio.ml (https://meet.meetup.com/wf/click?upn=DtKvkirJMjJK-2FpI8UHiQTj9xnStKDsZSBFsp528SQSE-3D_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESggMSc2DS3SjeBx-2BC96IjOXliD6t5odjuKI49xE-2BY3Y3AxPdBln4yJ3C5OqQI7j5HP2SkyUmdEng1hZRivnAfZpNkE32tpjqWIjBjUB6zHs7v-2BZCnRe2xDX88RrK9CoO-2FcVkwKmxGjZcpph5VMFfTn-2FA-3D-3D) is an open source project dedicated to helping machine learning (ML) professionals, academics, businesses and anyone else interested in ML model building, accelerate and simplify their experiments.
Studio.ml is an early-stage, ML model management framework written in Python that was developed to minimize the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments.
Most of the features are compatible with any Python ML framework including Keras (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTtwwxYQZA5OlMJLGCprn6rjg-3D_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESggPnAfTC3h2OFnzUEXUV5Pe4uS2U8ob-2BED9j5IQdeOG4-2Fku8GEQyPLpKqqK9erBniHd9KCsaIKjY73fhcFPd75v7G-2FiBaAurjq-2FUHP81mb-2F020-2BKpioM9HDb3tDZuE8VEQ8uqUM3ItrItH6npTYKaEQ-3D-3D), TensorFlow, (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt8lpj-2BSCitCNgaXfFwOYWtMHLmDRoynWhqCulrvkns6O_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESg41D-2B6q7SYHuiHrLI-2FpzhRInAhaKqgS0kawVJG9DpKg9hZSlxelau3AJQA5FKDZZ-2BcuDw9hG4CzfWDztw9nfDeGMcKyrfB7aanHYZltX8ozLQKgTlb9FMwfrHPF3CJ7qHS9OR-2BZ7f1x4xqWNqSY-2B7ag-3D-3D) PyTorch (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTtw3-2Fbj7kx978WGNCb6Ka1wy8rKGQbe6iBTzOnP1dSBZl_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESgDVzjfZp3A8dQNo09fiXwZjv8sbIXB4wcu2TgOwOkbtNyY3qSDSxreMEzeQIoCWVAKOFiOlr-2FCufX-2Fat8hlW442vIWYbMkV4ps2D-2B-2FvPd9fLXET6nd-2By-2FWq-2BMRaqS0qQB3uxmeTzqBKRCXw-2BkevVJag-3D-3D), and scikit-learn (https://meet.meetup.com/wf/click?upn=DtKvkirJMjJK-2FpI8UHiQTj4ojQ8sirF3GbmKcavqH5yyQ46wxAZ1j1t9W0CBFTgD_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESgfqfSlfOHaTYQql-2B48Jq-2BeqKv0OOZC40WCEsCNNvmUnwi0IjnHBL5UieSJgmTpSrDYsURt5F8U3A5RSJsdM9mGu9hrY8PJ74Emv4Rj1mRGcsTYtVOZzFpAMZQk4Af4m6WpvgPGPBi4yFUopPk4Zh-2FQw-3D-3D), with additional features available for Keras and TensorFlow.
So far, using Studio.ml you can:
Capture experiment information- Python environment, files, dependencies and logs- without modifying the experiment code
Monitor and organize experiments using a web dashboard that integrates with TensorBoard (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt8lpj-2BSCitCNgaXfFwOYWtNCLHmVm8lrQ-2BJF1s8cU32uRiVnEm873nNXO0xA0K27w94bnwWLZAta5qhzkSXPdLk-3D_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESgNeVKjkbuP0zfDrePZ0ilLd9cVNHB9xPSdGEFixiXK2boT-2FHUAsil7fqvWTfgQ4hGd8a1cxGw85glzwDo8G73lbSVjPU2WtH9QwLuV8BKd7ARqV-2B09Lt4Yt3g5RznP1p8i9AakNBpsuXssp-2FqemLK6w-3D-3D)
Run experiments locally, remotely, or in the cloud (Google Cloud (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt62-2BwWwK90QVK56UVQE6snTfPUH83msf-2BlDhApESMJpm_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESgN-2B6Lkv1l4PkCMRU0oLOqK8czz-2FcPi9kJY7bQKL48x2NTGZKi1XbOIbttETj3Wn60Vm3hPgSgtSMEKDD0LejovN8xqKDSpQxfx238twipcSeWc6c4AtjmRVuEYY6xoJGZ3SY3LF5BsU79RbCZwBRwyg-3D-3D) or Amazon EC2 (https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt7bx9GNKvW8lJEnIPWi66VS7CqcAT0NBfGFDqWnzfSq6_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESg54EnkFgDBjyrA82smT0IxL5qrxNPDGUMFLv9rxdz-2BwFhNfpyU7pWDElOGxl8TX4QEGa7ERJr2N1vXzheJazmP01jN4NnkKrZa2kJlTZdo5-2Fg0HAlHU0AA8-2F5zF4d3NNuF2qSyCJsyNyxXKt5h1-2Bt-2FQ-3D-3D))
Manage artifacts
Perform hyperparameter search
Create customizable Python environments for remote execution
Access the model library to reuse models that have already been created
Speaker
Peter Zhokhov is a Senior Software Engineer in the Sentient core platform group. He has a PhD in Physics from Texas A&M.
About Sentient
Sentient Technologies is one of the world’s leading artificial intelligence companies, creating breakthrough AI products and research thanks to its advanced AI platform. It has pioneered ways to combine deep learning, evolutionary computation, neuroevolution and other forms of AI, and distribute it across many data centers and on thousands of GPUs and CPUs, a scale unavailable to most companies. Sentient’s scale and expertise has allowed it to create AI solutions for e-commerce, digital media and finance, as well as partner with renowned institutions, like MIT and Oxford, on projects tackling complex problems in healthcare and agriculture.
https://www.sentient.ai/our-story/
