Name: Understanding Workload, App, and Data Deployment and Migration Decision Making
Start: 2025-07-22T15:00:00Z
End: 2025-07-22T15:00:31.000Z
Location: BrightTALK
Rating: 4

The Enterprise Architecture channel  presents the next generation of Enterprise IT: recognizing the strategic importance of digital transformation and the data center to make companies more nimble and competitive. These presentations will help demanding IT and BizOps professionals achieve flexibility, scalability and performance with reduced risk and complexity.

In today’s evolving IT landscape, selecting the right storage architecture is critical for optimal performance, scalability, data governance, and cost-efficiency. Furthermore, AI workloads have uniquely influenced how we meet these demands from our storage infrastructure. This webinar provides a technical deep dive into three fundamental storage deployment models – on-premises, cloud, and hybrid – examining their architectures and operational trade-offs through the lens of two key concepts: indirection (accessing data through mapping layers that provide flexibility and abstraction) and redirection (rerouting data requests to enable failover, load balancing, and optimized performance). 

We are going to take some of the key stages of AI lifecycle development as sample use-cases (such as data ingestion, preparation, training, inferencing, and retrieval) and compare how each storage model can serve these use-cases across varying access patterns, data volumes, and performance requirements.

Attendees will gain a practical framework for aligning AI workloads with the most suitable storage architecture, balancing cost, scalability, and latency. Whether you are building AI infrastructure from scratch or optimizing existing deployments, this session will help you make informed decisions for AI-ready storage.

Learning Objectives and Key Takeaways:

• Introduction to the 3 different types of storage deployment models – on-prem, cloud, and hybrid
• Trade-offs for each deployment model
• Importance of indirection and redirection
• Understand how AI-specific data types and access patterns (e.g., embeddings, checkpointing) influence storage performance and design
• Evaluate trade-offs in latency, scalability, security, and cost when choosing storage for different stages of the AI pipeline
• Gain a decision-making framework for selecting the right storage model based on workload characteristics and infrastructure goals

AI Meets Storage: Comparing On-Prem, Cloud, and Hybrid Architectures Across the AI Lifecycle

As AI workloads continue to scale, the physical infrastructure supporting them must evolve to deliver the performance, reliability, and efficiency required by modern data-intensive applications. This webinar will focus on the interconnect foundations of AI infrastructure, including storage. There will be an emphasis on the form factors, connectors, cables, and transceivers standards that enable scalable and interoperable systems.

We’ll explore how SNIA’s SFF Technical Work Group is contributing to the development of physical layer standards that support high-performance interconnects and storage devices used in AI environments. Topics will include the importance of transceiver innovation and the need to evolve; the role that form factors like EDSFF play; and how innovative cabling and connector designs support the adoption of high-speed signaling technologies like PCIe 8.0 and beyond.

This session is ideal for architects, engineers, and technologists who want to understand how physical infrastructure standards are enabling the next generation of AI systems.

Key Concepts:
• Transceiver development for out-of-box signaling 
• Connectors and cables for high-speed inbox signaling
• Copper vs. optical and the challenges of both
• SSD form factor innovations and their impact on density and thermal design

Accelerating AI Infrastructure: The Role of 400G and PCIe 8.0 in Next-Gen Interconnects

Storage security changes and adaptations are a fact of life to deal with the ever-changing threat and regulatory landscapes. A black swan event, a new standard, or a regulation often serves as a catalyst for organizations to review their controls and practices. The recent publication of NIST Special Publication 800-88 Rev. 2, Guidelines for Media Sanitization, is drawing attention to storage security, but it is not the only development worth noting. SNIA, Open Compute Project (OCP), Trusted Computing Group (TCG), and IEEE are developing or have developed standards and specifications that could be important going forward. One central theme underlying several of the activities is in storage sanitization (i.e., controlled eradication of data).

This session brings together a unique panel of experts who have served as editors of some of the most important storage sanitization standards. These experts also have broad knowledge of storage security. As a panel, they will provide insight into these new developments as well as observations on what organizations are experiencing. A moderator will engage the panel with thought-provoking questions, but participants are also encouraged to bring their own questions.

Join us to understand: 
• Changes to storage sanitization and what that means to organizations
• Primary drivers for storage security
• Difference between Storage Sanitization and Data Sanitization
• Interplay and interdependencies for storage security standards/specifications

The Storage Security Shake-Up: Adapt Now or Get Left Behind

Magnetic RAM, Resistive RAM, Ferroelectric RAM, and other new memory technologies have been pushed to the forefront by issues with Moore’s Law scaling, but the ancillary benefits they bring to computing will provide significantly more important longer-term benefits.  In this session, analysts Jim Handy and Tom Coughlin will explain how these new nonvolatile technologies will help accelerate and cost-reduce inference through compute-in-memory engines while standard computing will also see lower costs and higher performance by bringing inexpensive persistence closer to, and even within the processor chip itself.  The session will also address ways that new frameworks like CXL will help provide persistence-friendly structures at all levels of the computing hierarchy.

How New Memories Will Accelerate Both Inference and General-Purpose Compute

Smarter data management is the key to unlocking the full potential of next-generation data infrastructure for AI. This webinar explores how the SNIA Cloud Data Management Interface (CDMI™) standard enables government labs, HPC centers, and organizations leveraging the power of AI/ML to reimagine and innovate their infrastructure to meet next-generation data management demands. CDMI is a mature ISO standard (ISO/IEC: 17826:2022) with over 15 years of development and implementation, and is purpose-built for cloud data management. CDMI's extensive capabilities enhance key aspects of AI operations by transforming data management challenges into streamlined, intelligent workflows. 

CDMI 3.0 (in development now) will further extend the standard with:
• Automated resource discovery that accelerates AI workload efficiency
• Portable data movement that enables preservation of metadata fidelity during migrations, including graph relationships and knowledge graph
• MCP-based data management that enhances AI accuracy while improving access speed

Building on a proven, standards-based foundation, CDMI enables true machine-readable data management. CDMI 3.0 will further improve how teams discover resources, orchestrate AI agents, and protect data across complex cloud environments while establishing the infrastructure for automated, self-describing data ecosystems with full auditability that drive future innovation. 

Read the SNIA CDMI 3.0 white paper https://www.snia.org/educational-library/introduction-cloud-data-management-interface-cdmitm-30-2025

Intelligent Data Management: Shaping the Future for AI Workloads

Join us for an insightful webinar and demonstration as we explore the foundational process of training machine learning models, with a special focus on computer vision applications. This session will guide participants through the essential stages of model development—from data preparation to algorithm selection—highlighting how these steps influence performance and accuracy.
Through practical demonstrations and conceptual discussions, attendees will gain a deeper understanding of how vision models are built and validated, and how these techniques can be extended to broader predictive analytics. We’ll also examine common challenges and share strategies to overcome them, emphasizing the value of simplicity, modular design, and thoughtful evaluation.

Key Takeaways:
• Core steps in the model training lifecycle
• Why data quality is critical to success
• Understanding inferencing and metrics for model validation
• Shifting from engineering mindset to modeling intuition
• Avoiding common pitfalls in training workflows
• Embracing parsimony and modularity in design
• Using checkpoints to monitor and refine progress

From Data to Decisions: Understanding How AI Models Learn

Storage networking consumers are fully aware of the classic benefits of Fibre Channel as a dedicated storage networking solution that provides reliable and lossless data transfer as well as high availability and redundancy.  What is less known is that Fibre Channel supports several Upper Layer Protocols (ULPs) including SCSI, FICON, NVMe, Avionics and RDMA. In this webinar, we explore the FC-4 layer of the Fibre Channel architecture that provides the services used by the various ULPs deployed in Fibre Channel products. Join us for an exploration into:

• The history of Fibre Channel’s versatility 
• Fibre Channel architectural layers
• Overview of the current FC-4 transports: FCP, FC-SB, FC-NVMe, FC-RDMA

Attendees of the webinar will become well versed in the scope and capabilities of Fibre Channel and develop a well-rounded understanding of the technology.

Versatility of Fibre Channel: Multi-Protocol Magic

Edge computing is transforming AI deployment by processing data locally rather than in distant cloud data centers. This session explores how distributed intelligence enables both sustainability and capabilities that centralized architectures cannot provide.

Through two contrasting deployments—precision agriculture in resource-constrained environments and real-time manufacturing optimization in advanced facilities—we demonstrate how edge computing addresses fundamentally different challenges using the same architectural principles. Both achieve 50-70% energy reduction with compelling ROI, while solving problems where cloud computing fails due to latency, connectivity, bandwidth, or data sovereignty requirements.

Beyond the case studies, attendees learn decision frameworks for edge versus cloud architecture, strategies for deploying AI in environments with limited infrastructure, and how to build business cases that balance sustainability goals with operational performance. The session provides actionable guidance for organizations seeking to scale AI deployments while reducing their environmental footprint and enabling new capabilities.

Edge AI from Farm to Factory: Building Sustainable Intelligence across the world

On-premises enterprise data centers have been in decline since the creation of co-lo’s and the cloud, but a new age is emerging with the advent of AI. While most AI is served by the mega data centers mushrooming in “data corridors”, some applications require the low latency response times that edge computing was designed to provide. The industry readily builds large numbers of small processing centers at the edge to serve things like 5G cell service. But constructing AI data centers close to every high demand user is financially impractical and subject to local restrictions due to their enormous demands on power and water.

Tune into this session presented by industry thought leader Bob McFarlane to learn how reviving enterprise data centers as edge facilities may be the answer to acquiring virtually instant response times. Despite significantly greater power and cooling demands, they are unlikely to reach the levels that concern local citizens, enabling them to be renovated or built where necessary without pushback. Companies with significant AI demands may need to start planning and budgeting for high capacity, high reliability infrastructures with costs and staffing beyond anything they have previously experienced.

Learning Objectives:
- Understand the key elements of Edge Computing, and how they relate to AI applications.
- Learn the limitations AI services providers confront in constructing edge facilities.
- Learn to identify AI applications that require the fast response times of the “edge”.
- Prepare for the challenges your organization may face in utilizing AI in critical applications.

Will Enterprise Data Centers Become ‘The Edge’ for AI?”

This webinar will explain the process of model inferencing and deployment options. We will discuss how trained models are used to make predictions and the considerations for deploying models. Attendees will understand the challenges and strategies for optimizing model performance, covering:
 
• What is model inferencing  
• Model inferencing process 
• How inferencing deployment differs in Gen AI and visual inspection
         - Inferencing deployment software options 
          - Inferencing hardware considerations (e.g., on the edge, on-prem, etc.)
• Deploying AI models for production best practices and lessons learned 
• Strategies for maintaining model performance post-deployment

AI Model Inferencing and Deployment Options

As breakthrough technologies like AI mature, they inevitably evolve from centralized to decentralized architectures. With global edge computing spending projected to reach $378 billion by 2028, organizations that continue relying solely on centralized AI risk falling behind in both efficiency and performance. The pervasive waste and expense of centralized generative AI models have underscored the urgent need for intelligence at the edge.

While edge computing concepts have existed since the 1990s with content delivery networks, today's businesses face the complex challenge of transforming their architectures to process data closer to its source. This session explores how leading organizations are navigating this transition, reducing latency and costs while competing with decades of established centralized cloud infrastructure.

Join Alan Morrison (Advanced Data Technologies Consultant and Writer) to discover how decentralized AI and edge computing are revolutionizing data processing across industries.

Key Takeaways:

- Understand why centralized AI approaches are becoming unsustainable and how edge computing addresses these limitations
- Learn practical strategies for implementing edge intelligence from organizations already succeeding with this approach
- Discover the economic benefits of processing data at the edge versus transmitting everything to centralized systems
- Gain insights into market growth projections and how to position your infrastructure investments accordingly
- Identify the architectural transformation challenges of edge computing and how to overcome them

Decentralized AI: The Future of Edge Computing

Today’s enterprise workflows don’t live in one place — they move across edge devices, cloud infrastructure, and on-prem systems in milliseconds. Some start at the edge — like sensor data or customer interactions — while others finish there, after running through cloud analytics or AI inference. The result is a constantly shifting architectural boundary where speed, security, and compliance collide.

This session looks at how to design cross-architectural workflows that maintain consistency, security, and data integrity from end to end. We’ll unpack how different architecture types — edge-native, hybrid, and multi-cloud — impact data handling, governance, and latency. And we’ll dive into the new layer of complexity AI introduces: model hallucinations, prompt injections, and evolving regulatory constraints when sensitive customer data moves from edge to cloud.

Join Eugina Jordan, CEO of YOUnifiedAI, as she breaks down how to architect distributed systems that are both intelligent and compliant — not in theory, but in practice.

Key takeaways:

- Understand how modern workflows traverse edge, cloud, and on-prem environments — and where control points break down
- Design architectures that protect customer data and maintain compliance even when processing happens across geographies
- Implement AI workflows that minimize hallucination risks and prevent prompt-injection attacks at the edge
- Balance performance with governance — using architecture, not just policy, to enforce trust
- Build an operational model that lets security, data, and AI teams collaborate on a single distributed workflow

Building Secure AI Workflows Across Edge, Cloud & AI Without Breaking Trust

Information systems vary in their primary roles and objectives. Real-time information systems, in particular, impose strict deadlines for generating control responses, as delays in data generation, processing, or transfer may lead to critical failures. For such systems, ensuring proper transactions depends not only on their logical correctness but also on their ability to operate within the required time constraints—as excessive latency renders them ineffective.

Timely data processing is essential for data intelligence, influencing the methods and tools used for classifying and integrating real-time information. Achieving low-latency solutions requires leveraging local resources instead of relying solely on centralized servers or cloud infrastructure. This shift brings computing closer to the network's physical endpoints, introducing the edge computing paradigm. Expanding the network edge extends computational and data processing capabilities to the point of data generation, reducing cloud dependency while enhancing real-time analytics, security, and efficiency.

Discover the key technologies driving edge expansion, including edge computing, cloud-edge advanced communication protocols, and AI-driven edge networks, and gain expert insights into the future of edge computing.

Discover Technologies Driving the New Network Edge

In today’s data-driven world, ensuring the resilience of your storage systems is critical for long-term success. This vendor-neutral webinar, hosted by the STA, will bring together industry experts to explore proven methodologies for testing the robustness of Serial Attached SCSI (SAS) and other data storage infrastructures. Craig Foster (Teledyne LeCroy) and Paul Coddington (Amphenol) will discuss practical approaches and the latest standards that enable secure, reliable, and high-performance storage systems. Rick Kutcipal (Broadcom) will moderate the Q&A and offer other perspectives. Join us to gain insights on ensuring your storage solutions meet the demands of modern workloads and prevent costly data loss or downtime.

Key Takeaways:

- Understand the key factors impacting the robustness of Serial Attached SCSI (SAS) and other data storage systems.
- Learn about the latest testing methodologies and standards in SAS and storage infrastructures.
- Discover how vendor-neutral best practices can enhance the security and reliability of SAS-based storage.

Reduce Your Risk of Data Loss! "Critical Testing Insights for Developers"

In the midst of rapidly advancing technological progress, the rise of digitization and remote work has become a prevailing trend. As a result, critical infrastructures are increasingly susceptible to new risks, continually expanding the realm of cybersecurity threats and attack methods. 

Consequently, organizations are more inclined to entertain the notion of paying ransoms to threat actors. It is imperative for organizations to remain vigilant against these emerging threats to safeguard their systems. The recent proliferation of artificial intelligence (AI) technology has given rise to AI-powered cyber-attacks, which are experiencing exponential growth. This development has facilitated cybercriminals in creating sophisticated and innovative malware embedded with new zero-day vulnerabilities. 

AI-powered cyber-attacks leverage machine learning to adapt to evolving defenses and devise novel methods to circumvent them, all while remaining undetected. This enables them to establish an illicit, enduring presence within networks for the purpose of extracting highly sensitive data. 

In order to effectively counter advanced cyber-attacks, organizations must adopt a comprehensive approach and incorporate zero trust principles into their architecture. This involves: 
- Treating every device, node, or entity as a potential threat point. 
- Implementing explicit identification, authentication, and authorization measures. 
- Embracing a default deny model as opposed to a default access model. 
- Developing proactive incident response plans. 
- Providing cybersecurity best practices training for employees. 
- Deploying AI-powered cybersecurity solutions. 

We invite you to engage in thoughtful discourse on these topics, cultivating a clear understanding and exploring strategies for integrating zero trust into your scalable enterprise architecture.

Integrating Zero Trust in a Scalable Enterprise Architecture

This webinar will provide a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML). We will explore the basic concepts, history, and applications of AI/ML, setting the stage for more advanced topics in subsequent webinars. Attendees will gain insights into how AI/ML technologies are transforming industries and everyday life and have the opportunity to see one of the earliest AI experiments: Training a neural network to recognize written letters.
 
Key Concepts:
• Definition and History of AI and ML
• Differences Between AI, ML, and Deep Learning
• Real-World Applications of AI/ML
• Basic Terminology and Concepts

Introduction to AI and Machine Learning

In this webinar, we’ll introduce the PCI Express (PCIe) interface which is an essential component of AI systems due to its ability to provide high bandwidth and low latency over short distances. We’ll introduce the basics, then discuss the evolution, requirements and benefits. We’ll cover features in PCIe Gen6 including bandwidth, AER (Advanced Error Reporting), DPC (Downstream Port Containment), different modes and signaling techniques. We will also cover PCIe switching and its applications for AI. Join us for an insightful discussion on:
- What is PCIe and why it matters? 
- Discovery/Enumeration and Hot-plugging 
- NTB (Non-Transparent Bridging) applications and uses
- Shared credit/received buffer
- Selective NAK vs. Standard NAK for Flits replay
- PCIe Gen6 Enhancements
- PCIe Switch and applications for AI

Everything You Wanted to Know About PCIe But Were Too Proud to Ask

In an era where application environments are distributed, the network often directly determines the cost, the efficiency and the experience of nearly every application. With the era of AI on the immediate horizon, how will organizations evolve their network and infrastructure planning and operations to adapt? Which deployment strategies are being considered for AI workloads, and what role must the network play in the various scenarios? What are the biggest challenges facing organizations building networks for AI? And, what are the most important features and capabilities required to ensure the network can be an enabler, and not a barrier, to AI deployments? Join Jim Frey as he helps answer these questions in the latest research on Networking for AI.

Architecting the Network for an AI-Powered World: Customer Challenges, Plans, and Priorities

Scaling AI requires flexibility to get started but also completeness when deploying to production. 

Learn how to build high-performance AI pipelines with Pure Storage—integrating FlashBlade®, FlashArray™, and Portworx®. Join Field Solutions Architect Emir Biser and Principal Field Solutions Architect Ian Saunders as they share insights to guide your AI success. 

● How to design and build high-performance, end-to-end AI data pipelines using Pure Storage solutions like FlashBlade, FlashArray, and Portworx.

● Best practices for scaling AI workloads from experimentation to production, ensuring both flexibility during development and robustness during deployment.

● Strategies for helping organizations manage data as AI systems become production ready, managing exponential growth while maintaining simplicity and efficiency.

From Data to Insight: Building End-to-end AI Pipelines with Pure Storage

Consistent excellence in application experience must be a priority for any digital business, whether that experience is for its customers or its employees. Observability and AIOps tools play a critical role in runtime post-deployment (Day 2) operations across distributed cloud environments. What are the current pain points, best practices, and investment priorities for developers and IT operators related to observability, visibility, and monitoring? How much time do application development teams spend on troubleshooting production environments, maintaining or modernizing existing application code, and productively writing new business code? How can modern AI-enabled tools and technologies help? And what barriers persist? Join Torsten Volk as he helps answer these questions in the latest research on How the AI Revolution will Transform AIOps and Observability.

How the AI Revolution Will Transform AIOps and Observability

• Review the current state of virtualization
• Data Infrastructure Insights anomaly detection
• Autonomous Ransomware Protection for NAS and coming soon for SAN workloads
• How NetApp leverages AI within our storage
• What does the future look like

Virtualization and AI: Building smarter and more efficient systems

Enterprise Strategy Group’s (ESG) latest Generative AI: State of the Market Study found that 68% of polled organizations plan to increase their AI budgets this year, applying AI and GenAI in an average 3 business areas. As these AI initiatives evolve from experimental to mission-critical, IT leaders face greater infrastructure challenges that extend beyond traditional high-performance computing requirements. 

Watch this fireside chat to glean our best practices for building scalable AI infrastructure and the crucial data pipelines that underpin both training and inference workloads.  You’ll learn how NetApp successfully scales production AI environments for customers across multi-cloud ecosystems, discover battle-tested frameworks for building resilient data pipelines, and prepare for the next generation of AI models.

Scale Your Enterprise AI: Balancing Governance, Performance, and Cost

With AI and data-intensive workloads transform IT priorities, data storage infrastructure plays a crucial role in the success of digital operations. Data drives modern business success, yet data environments are more distributed and silo’d than ever before. What are the top challenges of distributed data environments? How is AI shifting storage investments and considerations? What is fueling a resurgence of on-premises storage investment? What role does the hypervisor and hyperconverged infrastructure play in the modernization of hybrid and multi-cloud environments? Answering these questions is the key to optimizing data storage and access in the modern digital age of business. Join Simon Robinson as he discusses recent research on the State of Enterprise Storage and HCI.

Navigating the Cloud and AI Revolution: The State of Enterprise Storage and HCI

Want to accelerate your ability to predict and prevent incidents before they occur? Learn how Splunk AI can be the catalyst. Join us to learn how to get started using Splunk AI for Predictive Maintenance, Detecting Service Performance Issues, Alert Noise Reduction, and User Experience Monitoring. We'll introduce the AI impact assessment framework to assess objectives, risk, and execution capacity, demonstrate how to use custom Anomaly Detection with the Splunk Platform and discuss the embedded AI capabilities in Splunk Observability Cloud with a special focus on Splunk ITSI.

Splunk AI for Observability: Accelerate Detection, Investigation and Response

Enterprise Strategy Group AI for Infrastructure, Cloud, and App Dev

Businesses enjoy a wealth of application deployment choices between cloud providers, data centers, colocation, as well as edge locations. As cloud adoption matures organizations become more distributed with investments increasing both on and off premises. How do businesses make application deployment and migration decisions between locations? Why do “cloud-first “organizations keep workload on premises? When do organizations switch primary cloud providers? What rationale, considerations, and processes matter? How have the adoption of cost-analysis tools impacted organizations investment decisions. Answering these questions is the key to understanding the future of hybrid, and multi-cloud investment. Join Scott Sinclair as he discusses recent research on Understanding Workload, App, and Data Deployment and Migration Decision Making across Distributed cloud environments.

Understanding Workload, App, and Data Deployment and Migration Decision Making

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Understanding Workload, App, and Data Deployment and Migration Decision Making

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