- +1 (503) 776-0317
- info@rocketml.net
End to End Machine Learning Platform for any size data
Connect to Data
Start with data on your laptop or connect to your data source or simply pick up a Kaggle dataset. Data and files persists, eliminating need for backing up your work.
Build Models & Apps
Explore data and start building models on your favorite tools like VS Code, Jupyter Notebook on 200+ python packages ranging from Tensorflow, Pytorch, Rapids, Dask without having to download, install, fiddle to make them work!
Track Experiments
All the experimental runs are tracked automatically with meta data and metrics enabling scientists to pick an optimum instance of an experiment that might be production worthy. As the Data Science process is very research-centric, it is common that you will have multiple experiments.
Test Models
Already Built Models save time and help Data Scientists build better models and ML applications. RocketML offers hundreds of already built models to speed up development of high value applications and data pipelines
Deploy Models
Scientists can deploy models with the single click of a button, without having to deal with model containerization, URL generation, routing, or security. RocketML takes care of all that behind the scenes.
Scale on GPU & CPU
On RocketML, scaling vertically and horizontally is just a click of a button. Users can select bigger machine or a cluster of machines depending on problem and data size.
Technology built for speed and success
Open and No Permission Environment
RocketML comes pre-configured with about 200 Tools and Frameworks ranging from Distributed Compute platform Dask, GPU optimized Rapids, Tensorflow, Tensorboard and PyTorch. Everything accessible via web browser in a Jupyter Notebook or VSCode Environment enabling variety of tasks ranging from Data Engineering, Exploratory Data Analysis and Model building at scale.
High Performance Compute System
With cluster computing-first philosophy, RocketML is built from ground up utilizing High Performance Compute technology stack like MPI, higher order optimization methods, distributed dense and sparse linear algebra and a unique set of high performance algorithms. The end result is customers and users save more than 50% time and money compared to any Distributed Machine Learning platform alternatives.
Fast Prototyping on Cloud
Building an ML based application is a lengthy process with many wheels within wheels. RocketML comes preloaded with state of art pre-build models. If there is an already build model available for use, it saves enormous amount of time and money for Data Scientists. With multi-cloud support, users can access best and tested pre-built models and advance their application rapidly. Distributed cloud system architecture further enables Data Scientists to connect various pipelines into a complete application that can be demonstrated to management before moving to production.
Request Demo or Access
Note: Businesses who leave spam messages will be permanently blacklisted