Videos Archive - Page 28 of 280

Company: Run:ai, VAST Data

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Personnel: Neeloy Bhattacharyya, Sandeep Brahmarouthu

In this discussion, Neeloy Bhattacharyya from VAST Data and Sandeep Brahmarouthu from Run:ai explore the complexities of deploying AI for high-value use cases at scale, focusing on the movement and management of data throughout the AI pipeline. They identify a common challenge in organizations where the processes of data preparation and model training and inference are often separated, leading to inefficiencies. They emphasize the importance of understanding data provenance and lineage to leverage AI effectively, especially for innovative use cases.

VAST Data’s approach involves simplifying the AI data pipeline by integrating data capture, preparation, training, and model serving processes more closely, highlighting the inefficiencies of traditional data storage and processing methods. Bhattacharyya introduces the concept of “data adjacency,” where certain functions are more efficiently run closer to where the data is stored to improve processing times and outcomes.

Brahmarouthu discusses Run:ai’s role in managing GPU resources for AI workloads, addressing the challenge of efficiently scheduling and utilizing GPUs across different teams and projects within an organization. He highlights the importance of Kubernetes in managing these resources, despite its limitations for AI-specific workloads, and how Run:ai enhances Kubernetes to better serve AI applications.

The conversation also touches on the operational challenges of deploying AI within enterprises, including the need for a DevOps model that accommodates the experimental nature of AI. They discuss the importance of infrastructure and technology partnerships, like the one between VAST Data and Run:ai, in creating efficient, scalable AI deployment strategies.

VAST Data DASE Architecture Optimized for Supermicro Hyperscale

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Company: Supermicro, VAST Data

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Personnel: John Mao, Lawrence Lam

John Mao from VAST Data and Lawrence Lam from Supermicro discuss their companies’ strategic partnership, focusing on AI and storage solutions. They highlight Supermicro’s significant presence in AI and its technological leadership and the company’s growth overall, particularly in AI and data centers. Supermicro’s early investment in GPU technology, despite AI and machine learning not being popular at the time, positioned the company ahead of its competitors. The partnership announcement includes the development of a joint solution combining VAST Data’s platform software with optimized Supermicro infrastructure, and a full-stack AI reference design and architecture to be revealed at GTC in March.

The conversation also touches on the importance of fast storage models to feed data to accelerators for AI training and the shift towards liquid cooling in data centers to overcome the physical limits of cooling GPUs, aiming for higher utilization ratios. Mao explains how VAST Data’s software-defined solution is being adapted for hyperscale optimization, catering to web scale and hyperscale customers with considerations for serviceability and scalability. The partnership aims to leverage Supermicro’s hardware innovations with VAST Data’s software to address the evolving needs of AI infrastructure, indicating a future where GPU servers might incorporate Bluefield optimization for enhanced performance.

Running VAST Data End to End on NVIDIA BlueField DPUs

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Company: NVIDIA, VAST Data

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Personnel: John Kim, John Mao

John Mao of VAST Data and John Kim of NVIDIA discuss their collaboration to integrate the data platform solution with NVIDIA’s BlueField Data Processing Units (DPUs). They discuss the benefits and technical aspects of using DPUs in data centers, emphasizing the acceleration and offloading of infrastructure tasks such as networking, storage, and security.

The conversation also highlights the implementation of the data platform leveraging BlueField-3 DPUs, showcasing how it brings storage and data closer to the compute layer, improving efficiency, security, and quality of service in large AI infrastructure deployments. They touch on the potential for power savings, the integration with NVIDIA’s software framework DOCA for block storage services, and the broader implications for service providers and enterprises.

Operationalizing AI at Scale with VAST Data

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Company: VAST Data

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Vimeo: Operationalizing AI at Scale with VAST Data
YouTube: Operationalizing AI at Scale with VAST Data

Personnel: John Mao, Neeloy Bhattacharyya

John Mao of VAST Data introduces the company, highlighting its focus on AI workloads and its growth since its founding in 2016. VAST Data has achieved significant success, including raising a Series C round at a $9.1 billion valuation, doubling and tripling software sales annually, and maintaining cash flow positivity. The company has deployed 10 exabytes of data globally, with a 60% business focus on HPC and AI workloads.

Mao announces partnerships with cloud service providers specializing in AI workloads, such as Lambda, Core42, and Genesis Cloud, and mentions enterprise customers like Zoom and Pixar using VAST for AI/ML workloads. He shares the company’s origins, founded on the vision of creating a data center scale computer or “thinking machine,” and outlines the roadmap from storage systems to data management capabilities and transactional storage systems.

VAST Data’s architecture, the disaggregated shared everything model, separates logic from storage, allowing for scalability, reliability, and economic efficiency. This architecture underpins the company’s new capabilities, including a SQL-compliant, transactional, and scalable database called VAST Database, and the VAST Data Engine for event-driven processing. Mao discusses the integration of Apache Spark and Kafka into their platform, enabling containerized compute engines alongside storage. This approach allows for complex data workflows, such as triggering functions upon data ingestion for processing and metadata generation, aiming to provide a comprehensive data platform that extends beyond traditional storage solutions.

Nile Service Block – Unique Capabilities to Redefine the Foundation of Wired and Wireless Networks in the Enterprise

Appearance: Nile Presents at Networking Field Day 34

Company: Nile

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Personnel: Suresh Katukam

In this deep dive, Suresh Katukam from team Nile reviews the determinstic system design, campus zero trust security, and comprehensive data collection capabilities of the Nile Service Block. Purpose built to be delivered as a service for enterprise wired and wireless connectivity across campus and branch locations, Nile Service Block acts as the foundation of the Nile Access Service.

Live Demo – Closed Loop Automation for the Design and Installation of the Nile Service Block

Appearance: Nile Presents at Networking Field Day 34

Company: Nile

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Personnel: Ozer Dondurmacioglu, Shiv Mehra

In this live demo, Shiv Mehra from Nile demonstrates how the design and installation process for a Nile Service Block can be fully automated from site survey, site data gathering, bill-of-material creation, network topology maps, installation instructions and more. Before the demo, Shiv Mehra from Nile also reviews the fundamentals behind the Nile Service Block: vertically integrated Nile wired and wireless network purpose built to be delivered as a service.

Building the Next-Gen Enterprise Network with Nile – New Requirements, Guaranteed Outcomes

Appearance: Nile Presents at Networking Field Day 34

Company: Nile

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Personnel: Ozer Dondurmacioglu, Suresh Katukam

By incorporating day -N to day N operations within the wired and wireless network technology stack, it is in fact possible to guarantee performance for coverage, capacity and availability. With a brand new approach, team Nile is trying to completely eliminate manual workflows in designing, installing and maintaining an enterprise network. Tune in to learn why Nile is converting an enterprise network along with all its environmental context to a collection of data sets to automate its operations.

Delegate Roundtable: Is Cloud Networking More Cloud Or More Networking

Appearance: Networking Field Day 34 Delegate Roundtable Discussion

Company: Tech Field Day

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Personnel: Tom Hollingsworth

Join the Networking Field Day delegates along with Tom Hollingsworth as they discuss the advent of Cloud Networking and how it impacts current networking teams. The discussion encompasses the way that cloud networking is handled and abstracted as well as who should be responsible for performing those tasks and who ultimately does them. In addition, the delegates debate the skill set of a full stack engineer along with the gap between traditional networking learning and modern cloud environments.

Aviz Networks Network Copilot and Networking Enterprise Suite Technical Overview

Appearance: Introducing Aviz Networks at Networking Field Day 34

Company: Aviz Networks

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Personnel: Chid Perumal

In this video, witness live demos of our Open Network Enterprise Suite (ONES) and Network Copilot, showcasing their transformative impact on network management and operations. Explore our suite of products, including the Fabric Test Automation Suite (FTAS) to ensure the quality of SONiC deployments, ONES for advanced network management, Open Packet Broker (OPB) for enhanced network visibility, and Network Copilot for smarter networks.

Aviz Networks Overview and Networking 3.0 Unpacked

Appearance: Introducing Aviz Networks at Networking Field Day 34

Company: Aviz Networks

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Vimeo: Aviz Networks Overview and Networking 3.0 Unpacked
YouTube: Aviz Networks Overview and Networking 3.0 Unpacked

Personnel: Ilona Gabinsky

In this introduction, learn how to unlock the potential of your network with our data-centric Networking 3.0 stack, designed for open, cloud, and AI-first networks. The Aviz Networking 3.0 stack redefines the landscape with a vendor-agnostic approach, supporting a wide range of ASICs, switches, clouds, and LLMs. At Aviz we enable open-source SONiC for NOS and open-source LLMs for generative AI. In this video, witness live demos of our Open Network Enterprise Suite (ONES) and Network Copilot, showcasing their transformative impact on network management and operations. Explore our suite of products, including the Fabric Test Automation Suite (FTAS) to ensure the quality of SONiC deployments, ONES for advanced network management, Open Packet Broker (OPB) for enhanced network visibility, and Network Copilot for smarter networks.

Forward Networks How Many Devices Do You Have – Network Lifecycle and Why You Need to Care

Appearance: Forward Networks Presents at Networking Field Day 34

Company: Forward Networks

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Personnel: Michael Wynston

Before Fiserv became a Forward Networks customer, the network team was faced with solving several enterprise network challenges: addressing CVE events, managing the perimeter, audits and compliance findings, and smoothly enabling automation. In this customer testimonial, Michael describes how Forward Enterprise uniquely delivered solutions for the known list of challenges, and also provided unexpected value by lowering the cost of data and validating network intent + network inventory.

The Power of A.I. in a Forward Networks Digital Twin

Appearance: Forward Networks Presents at Networking Field Day 34

Company: Forward Networks

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Personnel: Nikhil Handigol

Nikhil Handigol demonstrates how generative A.I. in unlocks network insights and shows the delegates how Forward Networks now seamlessly incorporates generative A.I. into its network digital twin software. Inside the most popular product feature, Network Query Engine, every user can perform natural language queries to access crucial network data in seconds.

Unlock Network Security and Reliability with a Forward Networks Digital Twin

Appearance: Forward Networks Presents at Networking Field Day 34

Company: Forward Networks

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Personnel: Mike Lossmann

In this product introduction, Mike Lossmann explains the secret sauce inside Forward Network’s flagship digital twin software, Forward Enterprise, and how network digital twin benefits more than just “networking people”. Forward Enterprise analyzes every possible network behavior, traces where every packet could ever go, and gives every user a queryable vendor-independent data model so NetOps, SecOps, CloudOps and even Compliance teams can get a mathematically-accurate view of the network.

Qlik AI Demo Showcase

Event: AI Field Day 4

Company: Qlik

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Vimeo: Qlik AI Demo Showcase
YouTube: Qlik AI Demo Showcase

Personnel: Sean Stauth

Global Director of AI and Machine Learning, Sean Stauth, demonstrates Qlik’s AI capabilities in market today. In this presentation, he showcase the strategic tenants of a trusted AI foundation, the turnkey benefits of AI-enhanced Solutions, and create custom solutions with self-service AI using real-world use cases with proven AI value: sales, operations, human resources, and IT/information security. Demonstrations include a mix of “day-in-the-life” data science/ML engineering development and end-user experiences with applications from both an analyst’s and executive’s points of view.

Stauth begins with a discussion of the strategic tenets of a trusted AI foundation, the benefits of AI-enhanced solutions, and the creation of custom solutions with self-service AI using real-world use cases in sales, operations, human resources, and IT/information security. His demonstrations cover a range of scenarios, including the use of Qlik’s supervised learning AutoML capability, which has been used to develop over 150,000 models.

Stauth demonstrates the “day-in-the-life” of data science and machine learning development, as well as end-user experiences with applications, providing perspectives from both analysts and executives. He shows how to use the AutoML platform to predict customer churn and build predictive applications with what-if scenarios. He also highlights the importance of explainability in AI models, which allows users to understand why predictions are made and ensures trust in the AI system.

Additionally, Stauth demonstrates the integration of generative AI with Qlik apps in operational use cases, the use of Qlik’s AI assistant with Microsoft Teams for collaboration, and key driver analysis built on top of the predictive AI engine. He emphasizes that the goal is not just to build models but to create apps and solutions that deliver real value to end users.

Stauth also addresses questions about the licensing and pricing of Qlik’s AI capabilities, the differentiation between various types of AI, and the importance of having guardrails and ethical considerations in place when using AI. The presentation concludes with a demonstration of generative AI integration using Amazon Bedrock, showcasing how natural language queries can be used to interact with data and derive insights.

Qlik AI-Enhanced and AI Roadmap Deep Dive

Event: AI Field Day 4

Company: Qlik

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Vimeo: Qlik AI-Enhanced and AI Roadmap Deep Dive
YouTube: Qlik AI-Enhanced and AI Roadmap Deep Dive

Personnel: Nick Magnuson, Ryan Welsh

In this presentation, VP of AI Strategy, Nick Magnuson, will share Qlik’s product direction for helping customers mitigate the risk, embrace the complexity, and scale the impact of AI by creating a trusted AI foundation, benefiting from AI-enhanced solutions, and creating custom solutions with self-service AI. Grounded in market and customer research, he shares how Qlik’s vision for helping customers integrate generative AI across their organizations. Joining Nick is Ryan Welsh, Field CTO and former CEO of Kyndi, an innovator in natural language processing, search, and generative AI, recently acquired by Qlik.

Magnuson outlines a three-pillar strategy for AI implementation: establishing a trusted AI foundation, infusing AI into their data integration and analytics products, and enabling customers to build custom AI solutions. He emphasizes the importance of data foundations, including data veracity, processing, observation, quality, integration, governance, and lineage. He also discusses the use of AI to make data foundation tasks more efficient, such as using natural language to generate SQL queries. He addresses questions about where the AI runs and clarifies that Qlik provides the pipeline for data movement, not a data warehouse, and mentions the acquisition of Talend for enhancing their portfolio.

The presentation includes a discussion about the use of AutoML, which has seen rapid adoption and has helped customers like ARH, a regional health system, save significant amounts of money by predicting patient no-shows and taking appropriate actions. Magnuson also touches upon the roadmap for AI, which includes advanced analytics and time series forecasting. He stresses the importance of feedback in shaping their AI strategy and notes the shift towards practical applications of generative AI in 2023.

Ryan Welsh, Field CTO and former CEO of Kyndi, speaks about the acquisition of Kyndi by Qlik and its implications. Kyndi specialized in natural language processing, search, and generative AI, and their technology aimed to provide trustworthy answers from unstructured text data. Welsh highlights the challenges of making large language models understand domain-specific content and explains how Kyndi’s technology addresses this issue.

Qlik AI Strategy Overview

Event: AI Field Day 4

Company: Qlik

Video Links:

Vimeo: Qlik AI Strategy Overview
YouTube: Qlik AI Strategy Overview

Personnel: Mary Kern, Nick Magnuson

A recognized leader in analytics and data integration and quality, Qlik is trusted by 40,000 customers and 500 non- profits worldwide to help them use data, analytics and AI to create value by solving theirs and the world’s greatest challenges. In this presentation, VP of Analytics and AI Portfolio Marketing, Mary Kern, introduces Qlik’s strategy for helping organizations mitigate the risk, embrace the complexity, and scale the impact of AI.

Kern highlights Qlik’s history of innovation in visual analytics and its continued efforts in analytics, data integration, data quality, AI, and automation. She notes that Qlik supports over 40,000 organizations and 500 NGOs worldwide in leveraging data and AI to solve significant challenges. Kern discusses how Qlik took a step back to understand what organizations want from generative AI, finding that companies seek a partner to help them safely embrace AI, manage its complexity, and scale its positive impact. Qlik aids customers in building a trusted foundation for AI through broad data connectivity, intelligent data pipelines, data transformation engines, robust data catalogs, and lineage for traceability and trust.

Qlik offers turnkey AI solutions, enabling organizations to scale data-driven decisions across their teams through automated insights, natural language experiences, proactive alerts, and notifications. They also provide self-service AI solutions, including AutoML, data science, and generative AI offerings, under the umbrella of Qlik Stage. This set of solutions is designed to bring AI strategies to life while minimizing risk, handling complexity, and maximizing AI’s impact. Kern explains that Qlik has been an AI-enhanced analytics leader for years, using early LLMs like BERT to deliver prompt experiences in analytics. Qlik’s Insight Advisor acts as an AI assistant, helping developers auto-generate visualizations and dashboards, and enabling business users to ask questions and receive relevant answers in various formats and languages.

Qlik’s acquisition of predictive and AutoML capabilities allows customers to scale their data science investments by creating machine learning models to solve business problems. Kern cites examples of customers using Qlik’s AI capabilities, such as Airbus, Schneider Electric, and JB Hunt. During the Q&A, Kern and her colleague Nick elaborate on Qlik’s AutoML, which makes modeling more accessible to technical users. They discuss Qlik’s ability to handle both structured and unstructured data, with a focus on integrating data from diverse sources to find patterns that humans cannot interpret alone. They emphasize Qlik’s agnostic approach to data sources and its support for direct queries and in-memory computation to manage data effectively. The conversation also touches on Qlik’s use of open-source algorithms and standardized practices in AutoML. The company aims to simplify the process from experimentation to deployment, allowing users to easily integrate AI and machine learning into their existing Qlik dashboards and decision-making processes. Qlik’s platform facilitates the ingestion of data, transformation, and deployment of AI solutions without requiring heavy customer engagement or complex hardware setups.

Wherever There’s Data, There’s Possibility with Qlik

Event: AI Field Day 4