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PLANNING FOR
PRODUCTION-LEVEL
PERFORMANCE AND SCALE

MACHINE LEARNING INFRASTRUCTURE

MACHINE LEARNING INFRASTRUCTURE SOLUTIONS

As huge amounts of processing power and large data repositories have become more affordable, a rich environment for the advancement of machine learning and deep learning has emerged. Machine learning applications are being created and implemented across a wide range of processes, replacing or improving human input, and addressing problems never before tackled because of the sheer size of the data.

SUCCESSFUL MACHINE LEARNING PROGRAMS THINK BIG FROM THE START

The larger the data set is, the more potential value machine learning brings. Ironically, as the data set grows, so does the possibility that a project will run into problems related to cost and complexity of scaling.

Where successful machine learning programs typically stumble is in not planning for their success.  Prototypes of programs that start by using mid-range enterprise storage or by adding drives to servers often find that these approaches are not sustainable when they need to move to a fast production ramp.

THE RIGHT DATA INFRASTRUCTURE CAN MAKE ALL THE DIFFERENCE

DDN customers are leveraging machine learning techniques to speed results and improve competitiveness, profitability, customer service, business intelligence, and research effectiveness.

The performance and flexible sizing of DDN systems make them particularly well-suited for large-scale machine learning programs. They have the power to feed massive training sets to high core count systems as well as the mixed I/O capabilities necessary to handle data efficiently for CPU, GPU, and mixed multi-algorithm environments from simple linear regressions to deep neural nets. With appliances that can start at a few 100 TB and grow to ~10 PB in a single rack, DDN’s machine learning customers have the scale to go from test bed to production ramp and beyond in a single platform.

DDN CUSTOMERS ARE CHANGING EVERYTHING FROM HOW WE LEARN AND TRAVEL TO HOW WE MANAGE OUR HEALTH

A few examples of how our customers are leveraging DDN storage solutions for machine learning applications include:

  • Smart cities planning for tourism – city government and academic research cooperation
  • Fraud detection for wire transfers and credit card transactions at a large U.S. bank
  • Digital assistant / natural language processing at a Fortune 100 SaaS
  • Route optimization, pricing, and informed consumer metrics for autonomous vehicles
  • Near real-time affinity marketing and fraud detection for online payments

USE CASES

BENEFITS

Our comprehensive and seamless portfolio of end-to-end storage technologies provides an extremely flexible data lifecycle management set of tools that support any machine learning environment requiring the highest levels of performance and scale.

  • Infinite Memory Engine® solutions drive faster time to results with game-changing latency reduction
  • GRIDScaler® and EXAScaler® file system appliances offer best-in-class analytics, parallel file systems, and NAS for the most data-intensive and performance-demanding environments
  • SFA14KX® block-storage products can scale to nearly 7PB of capacity in a single rack
  • WOS® object-based archive and cloud solutions are specifically designed to address relentless data growth in the most adaptable and cost-efficient manner