Cisco’s AI/ML Networking Journey

Event: AI Field Day 1

Appearance: Cisco Presents at AI Field Day 1

Company: Cisco

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Personnel: JP Vasseur

In this presentation, JP Vasseur PhD, Cisco Fellow and head of ML and Data Science engineering, presents the journey of Cisco’s AI applications from 2013 to 2020. This journey began with self-learning networks, then proceeded to AI network analytics for wireless automated detection and root cause analysis, and now to ML-based security classification and behavioral spoofing detection. He also demonstrates Cisco AI Endpoint Analytics on Cisco ISE.


Building Big Data AI Applications on Intel Analytics Zoo

Event: AI Field Day 1

Appearance: Intel Presents Analytics Zoo at AI Field Day 1

Company: Intel

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Personnel: Jason Dai

Jason Dai, Senior Principal Engineer, discusses practical applications of Intel’s Analytics Zoo, an open source software platform for big data AI. This presentation focuses on four applications: Recommendation, time series analysis, computer vision, and natural language processing. His first example is the food recommendation engine used by Burger King, which uses a transformer cross transformer (TxT) model leveraging HDFS, Apache Spark and MXNet, and Ray. Next, Dai discusses how SK Telecom’s time series based network quality prediction solution is able to run up to 6x faster using Analytics Zoo on Intel Xeon and presents a similar use case of wind power prediction using Analytics Zoo by GoldWind, which improved accuracy to 79% with a 4x training speedup. When it comes to computer vision, Dai presents industrial inspection by Midea and KUKA and AI-assisted radiology with Dell EMC. Finally, natural language processing (NLP) is presented, using a chatbot in Microsoft Azure and job recommendation engine in Talroo as examples.


Intel Analytics Zoo Technical Overview and Case Studies

Event: AI Field Day 1

Appearance: Intel Presents Analytics Zoo at AI Field Day 1

Company: Intel

Video Links:

Personnel: Jason Dai

Jason Dai, Senior Principal Engineer, gives a deeper technical overview of Intel’s Analytics Zoo, an open source software platform for big data AI. Analytics Zoo uses distributed TensorFlow/PyTorch on Apache Spark, and Dai discusses how it is used in network quality prediction by SK Telecom. Next he focuses on RayOnSpark to run Ray programs directly on the big data platform, giving the example of Burger King’s fast food recommendation engine which leverages an end-to-end training pipeline with RayOnSpark. Turning to the ML workflow question, Dai discusses how a scalable AutoML allows time series prediction, using the example of Tencent Cloud’s TI-One ML platform. Finally, Dai presents the Zouwu open source time series framework on Analytics Zoo.


Intel Analytics Zoo: Software Platform for Big Data AI

Event: AI Field Day 1

Appearance: Intel Presents Analytics Zoo at AI Field Day 1

Company: Intel

Video Links:

Personnel: Jason Dai

Jason Dai, Senior Principal Engineer, presents Intel’s Analytics Zoo, a software platform for big data AI. Just as BigDL enables distributed, high-performance deep learning, Analytics Zoo is a unified big data AI platform for TensorFlow, PyTorch, Keras, BigDL, OpenVINO, Ray, and Apache Spark. This open source project reflects the transformation of big data, which now includes AI. After a brief case study, Dai presents the overall architecture of Analytics Zoo and discusses seamless scaling of analytics and AI from laptop to distributed big data.


Intel’s Transformation for the Intelligence Era

Event: AI Field Day 1

Appearance: Intel Presents Analytics Zoo at AI Field Day 1

Company: Intel

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Personnel: Fadi Zuhayri

Fadi Zuhayri, Sr. Director, Big Data AI, introduces Intel’s transformation for the the intelligence era. He begins with some background, as compute democratization has brought technology to the exascale era. He also presents the drivers for transformation, 5G, artificial intelligence (AI), and the intelligent edge, and how these have driven the technology pillars addressed by Intel. Intel’s analytics and AI strategy focuses on diverse hardware, software, and ecosystems from edge to cloud, providing a foundation for AI workloads. Next, he looks at Intel’s oneAPI which provides an abstraction layer to support any application or middleware on any hardware, and Intel DevCloud, which makes it easier to learn and develop software.


AI/ML Customer Success Stories on Red Hat OpenShift

Event: AI Field Day 1

Appearance: Red Hat Presents at AI Field Day 1

Company: Red Hat

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Personnel: Abhinav Joshi

Abhinav Joshi, Senior Manager, Product Marketing, OpenShift Business Unit, explains how organizations globally have achieved key business goals by operationalizing AI/ML projects on Red Hat OpenShift, broader portfolio, open source technologies and partner ecosystem.


Operationalize ML Models with Red Hat OpenShift

Event: AI Field Day 1

Appearance: Red Hat Presents at AI Field Day 1

Company: Red Hat

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Personnel: Tushar Katarki

Tushar Katarki, Senior Manager, Product Management, OpenShift Business Unit, discusses how companies can leverage DevOps capabilities in Red Hat OpenShift (industry leading Kubernetes platform) to operationalize ML models into production.


Speed Up AI/ML Projects with Red Hat OpenShift Hybrid Cloud Platform

Event: AI Field Day 1

Appearance: Red Hat Presents at AI Field Day 1

Company: Red Hat

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Personnel: Abhinav Joshi, Tushar Katarki

Abhinav Joshi, Senior Manager, Product Marketing, OpenShift Business Unit, presents the value of containers, Kubernetes, and DevOps powered Red Hat OpenShift (industry leading Kubernetes Platform), broader portfolio, open source AI/ML tooling, and a broad AI/ML ISV and infrastructure ecosystem to help solve them.


Red Hat AI/ML Market Trends, Desired Architecture, and Execution Challenges

Event: AI Field Day 1

Appearance: Red Hat Presents at AI Field Day 1

Company: Red Hat

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Personnel: Tushar Katarki

Tushar Katarki, Senior Manager, Product Management, OpenShift Business Unit, presents the AI/ML use cases across industries, desired capabilities in the end to end solution architecture, and potential execution challenges with operationalizing AI/ML at scale.


Live Demo of Audio and Visual Classification with BrainChip Akida

Event: AI Field Day 1

Appearance: BrainChip Presents at AI Field Day 1

Company: BrainChip

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Personnel: Anil Mankar, Chris Anastasi

Anil Mankar, Co-Founder and Chief Development Officer, demonstrates audio and visual classification with the BrainChip Akida development board.


Neural Networks Optimized to Run on BrainChip Akida Technology

Event: AI Field Day 1

Appearance: BrainChip Presents at AI Field Day 1

Company: BrainChip

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Personnel: Anil Mankar

Anil Mankar, Co-Founder and Chief Development Officer, presents the various neural networks optimized to operate on the BrainChip Akida processor. He begins with auditory classification, discussing keyword spotting in an always on microphone. He then presents visual processing, using person detection as an example. Next he discusses olfactory detection using the Fox 3000 olfactory data set and detecting volatile organic compounds. The next sense is taste classification using an “e-tongue”. Finally he presents somatosensory classification with an example of electric motor ball bearing fault diagnosis.


BrainChip Akida Architecture and Evaluation Board Introduction

Event: AI Field Day 1

Appearance: BrainChip Presents at AI Field Day 1

Company: BrainChip

Video Links:

Personnel: Anil Mankar

Anil Mankar, Co-Founder and Chief Development Officer, provides a tour of the BrainChip Akida NPU architecture and IP solution. He also discusses the Akida software development environment (ADE) and training workflow before turning to a presentation of the first Akida based products. The AKD1000 NSoC is a chip that incorporates an Akida Neuron fabric along with all necessary supporting interfaces and an M-Class CPU. The chip is packaged in an M.2 PCIe form factor evaluation board and enables efficient processing of various sensor modalities, as demonstrated later in this session.


Introducing the BrainChip Akida Event-Domain Neural Processor

Event: AI Field Day 1

Appearance: BrainChip Presents at AI Field Day 1

Company: BrainChip

Video Links:

Personnel: Anil Mankar

Anil Mankar, Co-Founder and Chief Development Officer, introduces the BrainChip Akida ultra low power edge AI solution. He begins with a discussion of the event-based architecture of the Akida technology, and how it addresses the challenges of edge computing. The Neuromorphic design enables distributed computation and event-based processing, communication, and learning. Akida utilizes low-bit precision to reduce memory and bandwidth requirements yet still delivers accuracy in leading models.


Aruba AIOps Innovation: A Glimpse into the Future

Event: AI Field Day 1

Appearance: Aruba Presents at AI Field Day 1

Company: HPE Aruba Networking

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Personnel: Bernd Bandemer

In this interactive session, Bernd Bandemer, Director, Data Science, discusses what AI brings in the future, where Aruba is focused, and what value their customer’s IT teams and user community can expect.


Aruba UXI: An Introduction to AI Alerts

Event: AI Field Day 1

Appearance: Aruba Presents at AI Field Day 1

Company: HPE Aruba Networking

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Personnel: Fouad Zreik

Fouad Zreik, UXI Product Management, gives a view into how Aruba uses AI to tame the alert fatigue and user experience issues related to site specific web and cloud applications. The presentation includes a look at the UXI interface and discussion regarding the benefits of dynamic thresholds.


Saving IT Time with Aruba AIOps: Use Cases

Event: AI Field Day 1

Appearance: Aruba Presents at AI Field Day 1

Company: HPE Aruba Networking

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Personnel: Partha Narasimhan, Sri Ventkiteswaran

Sri Venkiteswaram, Director, Product Management, Aruba Central, gives an overview of how AI Insights in Aruba Central is evolving and how IT has improved their ability to quickly resolve issues related to user specific issues, outdoor clients, transmit power mismatches, WAN gateway issues, and wireless coverage holes. This presentation includes a demonstration of Aruba Central.


Data as the Foundation for Effective AIOps with Aruba

Event: AI Field Day 1

Appearance: Aruba Presents at AI Field Day 1

Company: HPE Aruba Networking

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Personnel: Jose Tellado, Partha Narasimhan

Jose Tellado, AIOps Chief Technologist, PhD, and HPE Fellow, gives an in-depth look at the importance of usable data, models and training, dynamic baselines for customer specific Wi-Fi/WAN/switch problem solving, and optimization guidance via peer comparisons.


Full-Service AIOps with Aruba ESP

Event: AI Field Day 1

Appearance: Aruba Presents at AI Field Day 1

Company: HPE Aruba Networking

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Personnel: Ash Chowdappa, Partha Narasimhan

In this interactive discussion, Ash Chowdappa, SVP & GM, Engineering, presents the Aruba vision for AIOps. The discussion focuses on how Aruba ESP and AIOps spans the entire Aruba portfolio, from wireless to switching, WAN, security, and User Experience Insight, for greater IT efficiency.


Risk Profiling with Juniper, Driven by Mist AI

Event: AI Field Day 1

Appearance: Juniper Networks Presents Mist AI at AI Field Day 1

Company: Juniper Mist AI, Juniper Networks

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Personnel: Jeff Aaron, Krystle Portocarrero

Risk Profiling ensures a reliable and secure AI-driven enterprise network architecture for the next decade.

Krystle Portocarrero, Product Manager, discusses how Juniper is using Mist AI to address the challenges of cyber security risk with Juniper Advanced Threat Prevention (ATP). She then discusses AI-driven risk profiling, which combines real-time actionable threat intel with AIOps for networks. The Field Day delegates join in the discussion of networking risk before Jeff Aaron, VP of Enterprise Marketing, returns to summarize the Juniper Mist AI Field Day session.


The Juniper Self-Driving Network: From Telemetry to Actions

Event: AI Field Day 1

Appearance: Juniper Networks Presents Mist AI at AI Field Day 1

Company: Juniper Mist AI, Juniper Networks

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Personnel: Bob Friday, Jisheng Wang, Sudheer Matta

Juniper, driven by Mist AI, is the only vendor to use its own AI/ML to drive end to end customer success with self-driving RRM, coverage hole detection, and Actions.

Jisheng Wang, Head of Data Science for Juniper, presents the pipeline from telemetry to actions for creating self-driving networks, using Radio Resource Management (RRM) as an example. The Field Day delegates discuss the practical applications of the technology with the Juniper presenters, including Bob Friday and Sudheer Matta. Wang shows how Mist AI can detect Wi-Fi coverage holes and Ethernet errors. Finally, Bob Friday, Founder and CTO of Mist, discusses using Marvis AI for customer support at Juniper.


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