RocketML on Azure

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


Azure HPC cloud Infrastructure

Azure H-series and N-series VMs comes with InfiniBand, which ensures optimized and consistent RDMA performance.

RocketML HPC Machine Learning

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


The use of high performance computing ( HPC ) has contributed significantly and increasingly to scientific progress, industrial competitiveness, national and regional security, and the quality of human life. However, due to its complexity, its use has been limited to trained HPC engineers. RocketML intends to change that by abstracting the complexities and enabling ALL Scientists including Data Scientists to benefit from HPC value proposition. On RocketML, engineers and scientist can scale both vertically (bigger machines) and horizontally (more machines in parallel) by just a click of button for both high throughput and tightly coupled HPC workloads.


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 

Key Values

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.

Ever expanding global coverage