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Cisco and SwiftStack are collaborating to launch edge-to-core-to-multi-cloud data management solution for mission-critical use cases like autonomous vehicles, healthcare clinical diagnostics, and telecom operations. Cisco and SwiftStack’s joint Reference Architecture delivers massive storage parallelism and throughput needed for ingest, training and inferencing; a scale-out global namespace for access to data whether on-premises or in one or more clouds; data services such as tagging, search, and metadata management to support AI/ML workflows; and Google’s Kubernetes, Kubeflow and TensorFlow support. Additionally, the solution extends to Google Cloud Platform (GCP) and AWS to take advantage of cloud-bursting and economies of scale, while data is secured on-premises.

Edge is eating the cloud: The killer app for AI/ML workflows!

Edge computing is emerging as the killer application for AI/ML, with staggering growth predictions. Fifty billion objects and devices are estimated to be connected to the Internet by 2020, with a $7 trillion market opportunity, across all major verticals combined. IDC expects the cognitive/AI market to grow significantly from $8 billion in 2016 to $47 billion in 2020, at a CAGR of 55%. The driverless car market will be $47 billion by 2025, per Bloomberg, and healthcare and discrete manufacturing will be the top verticals, followed by banking and retail, per Gartner.

Software is eating the world, data has become the new oil, and AI the new electricity

Data-driven applications leveraging AI, machine, and deep learning are moving away from rules-based to data-driven programing. Decisions are now made based on the big data sets on which they are trained. As an example, look at the transformative business models and market caps achieved by likes of Uber, Alibaba, Netflix and Airbnb.

The Infrastructure Challenge

“Infrastructure challenges are the primary inhibitor for broader adoption of AI/ML workflows,” said Amita Potnis, Research Director at IDC’s Infrastructure Systems, Platforms and Technologies Group, in her research note.

Traditional data center technology is not designed to handle the data volume, velocity, and variability of AI at production scale. This requires a very different model than traditional business applications. The massive amount of data required, and its ingestion speed, are fundamentally changing how applications behave. Applications are now being shaped by data and require high-performance systems that can adapt to these new workloads. This leaves your IT teams struggling to keep up. They are trying to keep up with data scientists who are constantly changing data sources, and software stacks that have changing infrastructure requirements. At the same time, the data scientists are struggling to turn machine learning into a competitive business tool.

The I/O Challenge

Feeding data to these applications becomes a continuous process of ingesting, feature extraction, training, hyper parameter optimization, inferencing, and lifecycle management. The accuracy and predictability of these data pipelines is only as good as the data set on which they train, and effectiveness is only as good as non-blocking, continuous access to these data sets, which makes I/O bottleneck the primary inhibitor.

 The Cisco SwiftStack solution Reference Architecture


“With the addition of the Cisco UCS® C480 ML M5 for machine learning, we now offer a complete array of computing options sized to each element of the AI lifecycle: data collection and analysis near the edge, data preparation and training in the data center core, and real-time inference at the heart of AI,” said Nishant Shrivastava, Sr. Manager of Compute Product Strategy and Market Expansion at Cisco. “Our joint solution with SwiftStack enables customers to accelerate insights and decisions, demystify ML Stacks and reduce cost and complexity.”

“Traditional IT architectures are not designed for these new distributed workloads and fall short of performance, scale, and value so we created a solution tailored to accommodate AI/ML pipelines,” said Shailesh Manjrekar, head of product and AI/ML solutions marketing at SwiftStack. “With our joint solution with Cisco, users can quickly adopt an AI/ML platform, easily use multi-cloud workflows and frameworks, and scale to petabytes of storage and hundreds of gigabytes of bandwidth”

SwiftStack is showcasing these solutions at Cisco Live, held through June 9-13 at the San Diego Convention Center in booth #2334 and Cisco POD #DC03 and #DC07. Please sign-up to meet with us.


IDC Market Note

SwiftStack Stakes Its Play in the AI/ML Market

By Amita Potis Research Director at IDC’s Infrastructure Systems Platforms and Technologies Group

IDC Market Note | SwiftStack Stakes Its Play in the AI/Ml Market

About Author

Shailesh Manjrekar

Shailesh Manjrekar

Shailesh has deep experience in infrastructure across storage and networking (EMC, NetApp, HGST, Brocade). As a thought leader on the how AI/ML/DL impacts infrastructure, Shailesh has worked on changes needed in the datacenter, the edge and the cloud. Shailesh serves as the Head of Product and Solutions Marketing for SwiftStack.