RocketML on AWS

RocketML partners with AWS to provide the industry-leading SaaS, secure, cloud-based high performance computing (HPC) Machine Learning platform. Integrated with all AWS global data centers and over 200+ turn-key data science software applications, RocketML on AWS allows users to instantly scale out computationally complex machine learning pipelines.

“We are pleased to welcome RocketML as one of our launch ISVs for the new AWS Marketplace for Machine Learning””Our customers want easy-to-use AI solutions running on Amazon SageMaker, and RocketML has provided a wide variety of algorithms customers can choose from directly from the Amazon SageMaker Console.”

Dave McCann, Vice President, AWS Marketplace and Catalog Services,

Benefits of RocketML on AWS

Global Scale

EXPERIENCE THE POWER OF HPC, TOGETHER

AWS HPC cloud Infrastructure

HPC on AWS eliminates the wait times and long job queues often associated with limited on-premises HPC resources.

RocketML HPC Machine Learning

Highly scalable Distributed Deep Learning software framework for large models, large data and big compute.

DEMOCRATIZE HPC

NO HPC SKILLS REQUIRED

All the HPC complexities (like MPI, Job Schedulers) are abstracted without compromising performance.

No Cloud Skills required

Scientists can spin up clusters on web based UI without logging into Cloud consoles.

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РAditya Balu, Post-Doc Machine Learning Researcher, Iowa State University 

Why RocketML HPC

Time to Solution

Deep Learning is a key part of modern product development - Accelerate innovation and time to solution with RocketML.

Shorten training times, experiment more

RocketML HPC software infrastructure is highly scalable, saving both time and cost.

No labels? No problems

RocketML HPC software infrastructure supports all types of modern frontier Deep Learning methods, ranging from Fully Supervised Learning to Semi-Supervised, Self-Supervised, Weak-Supervised, and Unsupervised.

lowest TCO on cloud

Unlike Horovod, a most commonly used distributed deep learning framework, increasing batch sizes won't deteriorate convergence on RocketML, enabling better run times, accuracies and in turn more than 50% in cost savings.

RocketML develops and releases

Highly Scalable Machine Learning Algorithms

Dense Gradient Boosting Regression

A Gradient Boosted Decision Tree for regression on dense data set like CSV without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.

Video Embeddings using SVD

Video Embeddings using Singular Value Decomposition (SVD) can be used for semantic search, dimentioanlity reduction, and anomaly detection. Give a batch of videos singular values and vectors are computed using these algorithms.

Image Embeddings using SVD

Image Embeddings using Singular Value Decomposition (SVD) can be used for semantic search, dimensionality reduction, and anomaly detection. Given a batch of images, singular values and vectors are computed using this algorithm.

Text Latent Semantic Analysis

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.

Dense GB Classification

A Gradient Boosted Decision Tree for classification on sparse data set like CSV without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.

Sparse GB Classification

A Gradient Boosted Decision Tree for classification on sparse data set like LibSVM without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.

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