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AI is disrupting so many domains and industries and by doing so, AI models and algorithms are becoming increasingly large and complex. This complexity is driven by the proliferation in size and diversity of localized data everywhere, which creates the need for a unified data fabric and/or federated learning. It could be argued that whoever wins the data race will win the AI race, which is inherently built on two premises: 1) Data is available in a central location for AI to have full access to it, 2) Compute is centralized and abundant. 

Edge AI though, defies these assumptions. If centralized (or in the cloud) AI is a superpower and super expert, edge AI is a community of many smart wizards. As humans, we can appreciate the power of cumulative knowledge over a central superpower. In this webinar, we will touch on: 
• The value and use cases of distributed edge AI 
• How data fabric on the edge differs from the cloud and its impact on AI
• Edge device data privacy trade-offs and distributed agency trends
• Privacy mechanisms for federated learning, inference, and analytics
• How interoperability between cloud and edge AI can happen

Why Distributed Edge Data is the Future of AI

With cloud data privacy regulations evolving worldwide and accelerated adoption of AI technologies such as ChatGPT, Large Language Models (LLMs) and more, companies must ensure data and AI models are compliant. Confidential AI is a new collaborative platform for data and AI teams to work with sensitive data sets and run AI models in a confidential environment. It includes infrastructure, software, and workflow orchestration to create a secure, on-demand work environment that meets organizations' privacy requirements and complies with regulatory mandates. In this session, you’ll learn:

• What confidential AI is and why it is important
• Changing privacy and compliance landscape for AI use and models
• Biggest data privacy threats and opportunities 
• Pragmatic examples and next steps

The Rise of Confidential AI

Since its ratification in late 2018, NVMe/TCP has gained a lot of attention due to its great performance characteristics and relatively low cost. Since then, the NVMe/TCP protocol has been enhanced to add features such as Discovery Automation, Authentication and Secure Channels that make it more suitable for use in enterprise environments. More recently, as customers evaluate their options and consider adopting NVMe/TCP for use in their environment, many find they need a bit more information before deciding how to move forward and are asking questions such as:
• How does NVMe/TCP stack up against my existing block storage protocol of choice in terms of performance?
• Should I use a dedicated storage network when deploying NVMe/TCP or is a converged network ok?
• How can I automate interaction with my IP-Based SAN?

Join us for an open discussion regarding these questions and more.

NVMe/TCP: Performance, Deployment and Automation

Data Fabric is an architecture, set of services and platform that standardizes and integrates data across the enterprise regardless of data location (On-Prem, Cloud, Multi Cloud, Hybrid Cloud), enabling self-service data access to support various applications, analytics, and use cases.  The data fabric leaves data where it lives and applies intelligent automation to govern, secure and bring AI to your data. This session will discuss:
• Different types of data sources for data fabric integration
• Unification of structured and unstructured data sources into the data fabric
• How to simplify data access by virtually connecting data end points across a hybrid cloud landscape
• Providing automatic enrichment leveraging AI to contextualize data with semantics and knowledge
• Apply global and automated data governance and data privacy policy enforcement for increased data protection and quality

Data Fabric: Connecting the Dots between Structured and Unstructured Data

Join Jim Handy of Objective Analysis as he moderates a panel featuring Andy Rudoff and Bhushan Chitlur from Intel, David McIntyre from Samsung, and  Sudhir Balasubramanian and Arvind Jagannath from VMware as they discuss Persistent Memory , Compute Express Link™ (CXL™), and Memory Tiering; and how the eco-system is working together to provide solutions for memory tiering using CXL for customer use cases.

Persistent Memory , CXL, and Memory Tiering - Past , Present & Future

Explore the exciting and rapidly evolving world of decentralized storage and how it is bridging the gap between Web 2.0 and Web 3.0

In this webinar, we will provide an overview of the importance of enterprise decentralized storage and why it is more relevant now than ever before. We will delve into the benefits and demand of decentralized storage and discuss the evolution of on-premises to cloud to decentralized storage (cloud 2.0).

We will also explore the various use cases of decentralized storage, including its role in data privacy and security, and the potential for decentralized applications (dApps) and blockchain technology.

As part of this webinar, we will introduce you to the Decentralized Storage Alliance, a group of like-minded individuals and organizations committed to advancing the adoption of decentralized storage. We will provide insights into the members of the Alliance and the Working Groups that are driving innovation and progress in this exciting field and answer questions such as: 

● Why is enterprise decentralized storage important?
● What are the benefits, the demand, and why now?
● How will on-premises to cloud to decentralized storage evolve?
● What are the use cases of decentralized storage?
● Who are the members and Working Groups of the Decentralized Storage Alliance?

Join us to gain valuable insights into the future of decentralized storage and discover how you can be part of this game-changing technology.

Why Web 3.0 is Important to Enterprise Storage

Composable disaggregated infrastructures provide a promising solution to addressing the provisioning and computational efficiency limitations, as well as hardware and operating costs, of integrated, siloed systems. But how do we solve these problems in an open, standards-based way?

DMTF, SNIA, the OFA, and the CXL™ Consortium are working together to provide elements of the overall solution, with Redfish® and SNIA Swordfish™ manageability providing the standards-based interface. OFA is developing an OpenFabrics Management Framework (OFMF) designed for configuring fabric interconnects and managing composable disaggregated resources in dynamic High Performance Computing (HPC) infrastructures using client-friendly abstractions.

In this webinar, attendees will understand how use cases for scaling management of storage and fabrics and beyond are addressed in these architectures, and the applicability to additional technologies.

Casting the Net: Scaling Management of Storage and Fabrics

Data centers continue to expand their environmental footprints, currently consuming 2% of the developed world’s electricity. Experts predict this number could rise to 13% by 2030. This webinar will cover energy-efficiency in data centers and ways to rein in costs and improve sustainability. This includes delivering more power efficiency per capacity, revolutionizing cooling to reduce heat, increasing system processing to enhance performance, and infrastructure consolidation to reduce the physical and carbon footprint.
 
Join this webcast and you’ll hear our panel of experts discuss:

• Defining Sustainability  
• Why Does Sustainability Matter for IT? 
• Sustainability for Storage & Networking 
• The Importance of Measurement and KPIs
• Sustainability vs. Efficiency 
• Best practices - Now vs. Future  
• Bringing your IT, Facilities and Sustainability Organizations Together

Sustainability in the Data Center Ecosystem

The SODA Foundation, in partnership with Linux Foundation Research, has recently published its Data and Storage Trends 2022 Report, “Data and Storage Strategies in the Era of the Data-Driven Enterprise.” The findings from this global study provide a comprehensive look at the intersection of cloud computing, data and storage management, the configuration of environments that end-user organizations are gravitating to, and priorities of selected capabilities over the next several years. 

SNIA is pleased to host SODA members who led this important research for a live discussion and in-depth look at key trends driving data and storage, including:

• The top 12 trends in data and storage
• Data security challenges facing container deployments
• Approaches to public cloud deployment
• Challenges for storage observability
• Focus on hybrid and multi-cloud deployments
• Top use cases for cloud storage services
• The impact of open source and data and storage

Top 12 Trends in Data and Cloud Storage

Cybercriminals have always been about data – stealing data, compromising data, holding data hostage. Businesses continue to respond with malware detection on laptops and networks to protect data and prevent breaches, so why should storage be left out?  Storage houses what the bad actors are targeting - your data.  Is there anything we can do from within the storage layer to further enhance defense in depth?  
 
Enter "Cyberstorage", a term coined by Gartner, which is defined as doing threat detection and response in storage software or hardware. A parallel, related trend in the security industry is eXtended Detection and Response (XDR) which shifts some of the threat detection from centralized security monitoring tools (SIEMs) down into each domain (e.g., endpoint, network) for faster detection and automated response.  Factor in the growing impact of ransomware and all these forces are driving the need to find creative, new ways to detect malware, including from inside the storage domain.   
 
In this session we'll discuss:
• Cyberstorage and XDR – what are these emerging trends?
• Threat detection and response methods through a storage lens
• Possible approaches for detection when used in conjunction with security tooling
• Why silos between security and storage need to be addressed for successful threat detection

Cyberstorage and XDR: Threat Detection through a Storage Lens

Industry analysts predict Deep Learning will account for the majority of cloud workloads. Additionally, training of Deep Learning models will represent the majority of server applications in the next few years. Among Deep Learning workloads, foundation models -- a new class of AI models that are trained on broad data (typically via self-supervision) using billions of parameters – are expected to consume the majority of the infrastructure.
 
This webcast will discuss how Deep Learning models are gaining prominence in various industries, and provide examples of the benefits of AI adoption. We’ll enumerate considerations for selection of Deep Learning infrastructure in on-premises and cloud datacenters. Our presentation will include an assessment of various solution approaches, and identify challenges faced by enterprises in their adoption of AI and Deep Learning technologies. We’ll answer questions like:
·       What benefits are enterprises enjoying from innovations in AI, Machine Learning, and Deep Learning?
·       How should cost, performance, and flexibility be traded off when designing deep learning infrastructure?
·       How are cloud native AI software stacks such as Kubernetes being leveraged by organizations to reduce complexity with rapidly evolving software stacks (TensorFlow, PyTorch, etc.)?
·       What are the challenges in operationalizing Deep Learning infrastructure?
·       How can Deep Learning solutions scale?
·       Besides cost, time-to-train, data storage capacity and data bandwidth, what else should be considered when selecting a Deep Learning infrastructure?

Training Deep Learning Models in the Cloud

Machine learning (ML) is the study and development of algorithms that improves with use of data. As it deals with the training data, the machine algorithm changes and grows. Most ML models begin with “training data” which the machine processes and begins to “understand” statistically. 
Machine learning models are resource intensive. To anticipate, validate, and recalibrate millions of times, they demand a significant amount of processing power. Training an ML model might slow down your machine and hog local resources. 
The proposed solution is to containerize ML with NVMe over Fibre Channel (FC-NVMe) by putting your ML models in a container. In this webcast, we will highlight the benefits of containerizing ML models with FC-NVMe, discussing: 

• Containers are lightweight software packages that run-in isolation on the host computing environment. Containers are predictable, repetitive, and immutable. This ensures that no unexpected issues occur while moving them to a new system or between environments. A cluster of containers can be created with a configuration suited for machine learning requirements. Containers are also easy to coordinate (or “orchestrate”). 

• Artificial Intelligence (AI) at scale sets the standard for storage infrastructure in terms of capacity and performance, making it one of the most crucial factors for containers.

• FC-NVMe is an extension of the NVMe network protocol to Fibre Channel delivering faster and more efficient connectivity between storage and servers and providing the highest throughput and fastest response times. 

• The combination of FC-NVMe with an NVMe SSD solution with containerized ML allows orchestration to scale data-intensive workloads and increase data mining speed.

Benefits of FC-NVMe for Containerized ML Models

A new class of cloud and datacenter infrastructure is emerging into the marketplace. This new infrastructure element, often referred to as Data Processing Unit (DPU), Infrastructure Processing Unit (IPU) or xPU as a general term, takes the form of a server hosted PCIe add-in card or on-board chip(s), containing one or more ASIC’s or FPGA's, usually anchored around a single powerful SoC device. 

The OPI project has been created to foster the emergence of an open and creative software ecosystem for DPU/IPU based cloud infrastructure. The project intends to delineate what a DPU/IPU is, to loosely define a framework(s) and architecture for a DPU/IPU-based software stack(s) applicable to any vendors hardware solution, to allow the creation of a rich open- source application ecosystem, to integrate with existing open-source projects aligned to the same vision such as the Linux kernel, IPDK.io, DPDK and SPDK to create new APIs for interaction with and between the elements of the DPU/IPU ecosystem:
• the DPU/IPU hardware 
• DPU/IPU hosted applications 
• the host node 
• remote provisioning software 
• remote orchestration software

Learning Objectives: 
• OPI introduction 
• OPI workstream definitions and status 
• How you too can contribute to OPI

An Introduction to the OPI (Open Programmable Infrastructure) Project

Have you ever wondered how intelligent Industry 4.0 factories or smart cities of the future will process massive amounts of sensor and machine data? What you may not expect is a digital twin will most likely play a role. A digital twin is a virtual representation of an object, system or process that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. Digital twins can be used to help answer what-if AI-analytics questions, yield insights on business objectives and make recommendations on how to control or improve outcomes.

This webinar will introduce digital twin usage in edge IoT applications, highlighting what is available today, what to expect in the next couple of years, and what the future holds.  We will provide examples of how digital twin methods help capture virtual representation of real-world entities and processes, discussing: 

• What is driving the edge IoT need 
• Data analytics problems solved by digital twins
• How digital twins are being used today, tomorrow and beyond
• Use cases: adaptive agile factories, massive data generation across industries, and system of systems processes
• Why this is a technology and trend that is here to stay

Journey to the Center of Massive Data: Digital Twins

Using software to perform memory copies has been the gold standard for applications performing memory-to-memory data movement or system memory operations. With new accelerators and memory types enriching the system architecture, accelerator-assisted memory data movement and transformation need standardization.

SNIA's Smart Data Accelerator Interface (SDXI) Technical Work Group (TWG) is at the forefront of standardizing this. The SDXI TWG is designing an industry-open standard for a memory-to-memory data movement and acceleration interface that is – Extensible, Forward-compatible, and Independent of I/O interconnect technology. A candidate for the v1.0 SNIA SDXI standard is now in review.

Adjacently, Compute Express Link™ (CXL™) is an industry-supported Cache-Coherent Interconnect for Processors, Memory Expansion, and Accelerators. CXL is designed to be an industry-open standard interface for high-speed communications, as accelerators are increasingly used to complement CPUs in support of emerging applications such as Artificial Intelligence and Machine Learning.

In this webcast, we will: 
• Introduce SDXI and CXL 
• Discuss data movement needs in a CXL ecosystem  
• Cover SDXI advantages in a CXL interconnect

What’s in a Name? Memory Semantics and Data Movement with CXL™ and SDXI

The race to the cloud over the past two decades has been fast and furious. The ease and flexibility of turning on services when you want them, turning them off when you don’t, is an enticing prospect for developers as well as application deployment teams, but it has not been without its challenges. Many cite expanding costs, data sovereignty, or security as reasons for repatriation of workloads, but what about other factors, such as data gravity, latency, and extending workloads to the edge?

Kubernetes platforms offer a unique cloud-like experience — all the flexibility, elasticity, and ease of use — with the added benefit of a common user experience regardless of where it is deployed — on premises, in a private or public cloud, even at the edge. It also allows organizations to run different workloads on different underlying platforms by abstracting the system layer, creating a seamless hybrid and multicloud strategy. We even see more and more service providers offering Containers-as-a-Service regardless of where the physical infrastructure resides.

Our panel of experts will debate these issues, discussing:
• So how are all these trends coming together?
• Is cloud repatriation really a thing? 
• How are traditional hardware vendors reinventing themselves to compete? 
• Where does the data live? 
• How is the data accessed? 
• What workloads are emerging?

Kubernetes Trials & Tribulations: Cloud, Data Center, Edge

At the 2022 Open Compute Global Summit, OEMs, cloud service providers, hyperscale data center, and SSD vendors showcased products and their vision for how the family of EDSFF form factors solves real data challenges. In this webcast, SNIA SSD SIG co-chairs Cameron Brett of KIOXIA and Jonmichael Hands of Chia Network explain how having a flexible and scalable family of form factors allows for optimization for different use cases, different media types on SSDs, scalable performance, and improved data center TCO. They'll highlight the latest SNIA specifications that support these form factors, provide an overview of platforms that are EDSFF-enabled, and discuss the future for new product and application introductions.

EDSFF - Taking Center Stage in the Data Center

With the emergence of GPUs, xPUs (DPU, IPU, FAC, NAPU, etc) and computational storage devices for host offload and accelerated processing, a panoramic wild west of frameworks are emerging, consolidating and vying for the preferred programming software stack that best integrates the application layer with these underlying processing units. 
 
This webcast will provide an overview of programming frameworks that support (1) GPUs (CUDA, SYCL, OpenCL, oneAPI), (2) xPUs (DASH, DOCA, OPI, IPDK), and (3) Computational Storage (SNIA computational storage API, NVMe TP4091 and FPGA programming shells). 

We will discuss strengths and challenges and market adoption across these programming frameworks as we untangle the alphabet soup of new frameworks that include:
• AI/ML: OpenCL, CUDA, SYCL, oneAPI 
• xPU: DOCA, OPI, DASH, IPDK 
• Core data path frameworks: SPDK, DPDK 
• Computational Storage: SNIA Standard 0.8 (in public review), TP4091

You’ve Been Framed! xPU, GPU & Computational Storage Programming Frameworks

The Critical Role of IoT in COVID-19 Recovery

Laying the Foundation for Smart Cities

Panel: IoT Technologies Driving Digital Transformation

Transform your Enterprise with an Intelligent Edge and IoT

IoT at the Edge

Uncover strategies related to IoT at the Edge for Industry 4.0 and role of 5G

Getting “Edge-y” with the Internet of Things

Panel: Driving Enterprise Digital Transformation with IIoT

IoT Connectivity: The Move From 3G to LPWAN to 5G and Beyond

IoT Potpourri

Panel: Driving Business Opportunities with IoT at the Edge

Overnight Digital Transformation: A Guide to a Successful Remote Workplace

Panel: The Road to 5G for Industry 4.0

Driving Digital Transformation 2020

To compete in today's digital economy, it's essential that businesses make branch offices an integral part of the network. Cloud-based applications - including global collaboration ones whose use has skyrocketed due to COVID-19 - require bandwidth that is robust and highly scalable, and with data centers becoming more virtualized and distributed than ever, MPLS can no longer cut it.
 
With SD-WAN a key part of the puzzle piece of successful Digital Transformation, it’s crucial that business looking to adopt SD-WAN as part of their DX strategy have all bases covered, namely:
 
- Ensuring they have a dynamic SD-WAN that recognizes and supports business-critical applications
- Making sure the have the ability to modify WAN policies to cater for application criticality, performance needs and security requirements
- Establishing an SD-WAN strategy in the age of the Edge that understands the need for centralized visibility spans the distributed network 
 
But how do businesses ensure that their SD-WAN strategy and toolkit address the above? Join this panel of experts as they explore the key points of a winning SD network, and help drive your business to Digital Transformation success.

[Panel] Digital Transformation and the Need for SD-WAN

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[Panel] Digital Transformation and the Need for SD-WAN

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