SwiftStack for distributed, at scale
AI, Machine Learning data pipelines

Leverage your most valuable asset - your data:
monetize new business models, glean predictive insights and enable competitive differentiation

Predictive to Prescriptive to Cognitive Analytics

Monetize your data at petabyte scale, with In-place AI, Machine Learning (ML) and Deep Learning(DL) enabled by SwiftStack

AI/ML/DL Storage Workflow phases and their I/O challenges

AI/ML Workflow Phases

Why Swiftstack for AI/ML/DL Data pipelines?

Cloud Native Architecture ✓

SwiftStack, with it’s cloud native and object oriented architecture, is a transformative storage platform for AI, ML, DL Workflows. Traditional distributed locking filesystems or storage controller based architectures are inherently not designed for these new distributed workloads and fall short of performance, scale and economical value.

Ingest

  • Enables 100 GB/s+ high ingest with massive concurrency and throughput to match the GPU Compute layer parallelism.

  • Provides broad application and protocol support for enterprise applications (Ingest with Posix compliant, native NFS and CIFS) as well as cloud native applications ( Ingest with S3, Swift)

Enrich

  • Provides rich metadata tagging for contextualizing, supervised learning and other workflows

  • Elasticsearch integration

  • Leverages 1space for lifecycle Management with metadata tagging

Train

  • In-place neural net training, with native S3 support for Tensorflow like frameworks

  • Massive read bandwidth

  • Architectural separation of compute and storage enables massive scalability of training datasets.

  • 1space enables Cloud-bursting for best and cost efficient on-prem or across clouds GPU compute farms

Infer

  • Enables Inferencing at the Edge or Core

Retain

  • Policy based Lifecycle Management with metadata tagging, cloud tier, governance, massive scale with best economics.

Operationalize your AI/ML/DL workflows with SwiftStack reference design

Choice of workflow Integration for Line of Business personas

  • Nvidia GPU Cloud (NGC), with Kubernetes Orchestration and Docker Registry
  • Google Kubeflow - composable, portable, scalable ML stack for Kubernetes
  • Swiftstack client with Elastisearch integration for metadata querying

Choice of Compute services

  • Nvidia DGX-1, DGX-2
  • Cisco C480 ML

with containers and Kubernetes orchestration engine

Choice of Network services

  • L2/L3/Overlay flat networks
  • service mesh support

Multi-Cloud Data Platform reference design powered by SwiftStack

The reference design is validated between SwiftStack ecosystem partners, VAR’s and SwiftStack PS team.

SwiftStack and Nvidia GPU Cloud NGC
   

Use Cases

AI/ML/DL solutions are currently used across several vertical use cases:

Autonomous Vehicles

Autonomous Vehicles
  • Assisted Driving / ADAS
  • Telematics
  • Predictive Maintenance

Healthcare

Healthcare
  • Cancer anomaly detection
  • Pharma
  • Medical imaging
  • Pharma drug research

Manufacturing

Manufacturing
  • Predictive maintenance
  • Process optimization
  • Demand forecasting

Media and Entertainment

Media and Entertainment
  • Targeted Marketing
  • Content classification

Retail

Retail
  • Customer Churn
  • Cataloging
  • Supply Chain Management

 

 

Resources

Case Study

Case Study | Cloud9 Software

Leading SaaS Provider Leverages SwiftStack to Consolidate NAS-based Silos.

Download
 

Blog Post

CIO Q&A with a Pharmaceutical Perspective

by Mario Blandini

Want More Information Let Us Know