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think beyond legacy. Transformation is inevitable
Efficient Machine Learning infrastructure enables many applications at TCOs enabling significant opportunities for discoveries and competitive advantage. Below is a very small sample of what is feasible on RocketML
With RocketML technology, insurance companies can move beyond their current level-2 automation. Insurance industry is information and document intensive. With RocketML unsupervised, large scale, continuous learning platform with built in integrations to popular insurance platforms, Insurance companies can modernize their applications quickly and easily
Machine learning has been utilized for decades to process, analyze and interpret Seismic image data. However modern Seismic image data is large, dense and won’t fit into modern GPU memory. RocketML’s HPC based distributed deep learning technology eliminates memory barriers and enables fast, accurate image segmentation and subsequent interpretations
The immune system is a uniquely challenging biological system to decipher. Our combinatorics-based data analysis has proven beneficial to immunologists leading to comprehensive, complete phenotype analysis of immune cells that was previously computational intractable. Immunologists can now address the most pressing needs, including new vaccines, improved cancer immunotherapies, and more
Frequently asked questions
RocketML enterprise version supports both Deep Learning and Traditional Machine Learning class of problems.
In addition, platform also supports data engineering tasks at scale. Typically data processing, transformation and cleaning tasks take up enormous amount of Data Scientists and Engineers time. RocketML makes performing these tasks at scale on GPU or CPU only clusters super easy. With built in functionality for deployment and integration of the models into Applications, RocketML truly the most Unified Science platform in the market.
RocketML is built to make Machine Learning on big data easy. RocketML supports on-the-fly compute cluster creation without preplanning, only when needed saving businesses enormous costs. Built ground up with HPC technologies, unlike other Distributed Machine learning tools, RocketML achieves strong scaling, a gold standard in HPC world, saving businesses greater than 50% in compute costs.
Yes. We support GPU instances on Azure and AWS cloud. We can deploy our software into private on-premise data centers with GPU and CPU clusters. With a click of a button a Data Scientist or Engineer can start a GPU instance and use it an elastic, on-demand fashion saving thousands of dollars without compromising productivity
Unsupervised learning is the next frontier in machine learning technology innovation. Unlike supervised learning, unsupervised learning methods don’t require labeled data, thereby reducing data pre-processing related tasks by more than 50%. Our platform supports state of the art unsupervised machine learning techniques ranging from Self-Supervised to Semi-Supervised.
While supervised learning has enabled many break throughs for both business productivity as well as decision making, Unsupervised learning is the best pathway to building Autonomous applications and insights.
Currently only the community edition is hosted. If an enterprises wants us to host our SaaS on cloud, we are happy to support the requirement at no extra cost. However, most of customers want us to deploy our SaaS into their private network be it on cloud or on-premise.
Yes. Our product is “highly secure” that not only protects Customers data but also their infrastructure. We have several security related innovation that makes our implementation highly desirable by regulated industries.
Apart from superior scaling performance from RocketML, our customers also enjoy a completely hosted solution for Dask, Spark, Rapids, Tensorflow, PyTorch etc. at no extra cost!
It is truly the most versatile data science platform for both data engineering and data science work