Name: Bridging AI/HPC with Immersion: Transforming Storage and Data Backup Efficiency
Start: 2025-04-23T12:00:00Z
End: 2025-04-23T12:00:35.000Z
Location: BrightTALK
Rating: 5

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

GenAI promises to fundamentally change IT operations and help organizations increasingly leverage their data estate. AI can theoretically make IT more efficient, effective, scalable, correct and possibly cheaper even as our IT environments get more complex, distributed and dynamic. Most significantly, AI should be able to address challenges and solve IT problems that legacy solutions simply can’t figure out! Yet today’s GenAI is also nearing the peak of it’s broader business hype cycle. Many are starting to realize that the truly effective application of AI models (like LLM's) needs to be properly scoped, fenced and integrated.

Come to our practical review and discussion about the state of the art in AI for IT. We’ll examine how GenAI is already being applied productively within IT solutions for Storage and Data Protection. We’ll dive deeper into AI in IT storage and data protection operations:

- Machine Learning (ML) v GenAI vs agentic AI vs AGI - where is there real value?
- Good vs. Bad practical uses of GenAI for IT and their organizations - more than a chatbot, less than a human?
- How to leverage GenAI specifically within and for Storage and Data Protection?
- What is RAG and how might we best take advantage of it? 
- What to watch out for when deploying AI in data-centric operations - does AI know about IP, Privacy, Security and all the governance we care about?
- Is agentic AI going to someday replace IT?

Can You Trust the Machine? AI Best Practices for Storage & Security

Storage is one of the highest costs in IT environments today. Ironically as the unit cost of storage has decreased, the overall cost has increased due to the sheer volume of data companies create, process and analyse. If companies don’t manage the underlying storage of their data, they run the risk of data storage becoming the expensive answer to everything.

This session from industry thought leader Simon Ratcliffe will explore how we can choose and apply AI solutions to help optimise and secure underlying storage platforms. With so many AI options available, how do companies make the right choice? How do they avoid the solution becoming yet another disconnected part of our infrastructure ecosystem? Tune in to learn how to tackle these issues and much more.

Picking the Right Tool for Storage Management

In today's high-stakes digital landscape, organizations face unprecedented risks to their data assets, from sophisticated cyber-attacks to system failures and natural disasters. While most enterprises focus on backup systems and recovery protocols, the true foundation of effective disaster recovery lies in robust data governance – the systematic approach to managing data as a valuable business asset.

Many organizations struggle to implement effective data governance frameworks, grappling with data quality issues, inconsistent standards, and complex regulatory requirements. The ISO 8000 standard offers a comprehensive solution, providing internationally recognized data quality management principles that strengthen everyday operations and disaster recovery capabilities. This proven framework helps organizations maintain data accuracy, completeness, and consistency – critical elements that determine the success of any recovery effort.

In this session, Obukohwo Akpede shares key insights on leveraging ISO 8000 principles to build a bulletproof disaster recovery strategy that protects your critical data assets and ensures business continuity across your enterprise.

Key takeaways:
- Decode the essential components of a data governance framework that enhances recovery capabilities while meeting international quality standards.
- Learn practical techniques for implementing ISO 8000 principles to improve data quality and recovery readiness.
- Discover how to align your governance strategy with your organization's specific recovery objectives and risk tolerance.
- Gain insights into measuring and maintaining governance effectiveness through metrics and continuous improvement.
- Understand how to transform compliance requirements into operational advantages that strengthen your recovery position.

Data Governance as a Foundation for Disaster Recovery: An ISO 8000 Perspective

Tired of vendors ambulance-chasing when it comes to cyber resilience and ransomware? Seeing alarming cybersecurity news and wondering how it got so bad? Not sure what general defense principles you can use? Join us for Coffee Break with Andrew Miller, featuring returning guests Andy Stone, Pure Storage CTO and former white hat hacker and Jason Walker, Pure Storage Technical Strategy Director for Cyber Resilience. Together, they’ll tackle the latest in cyber resilience, cutting through the noise to share actionable insights and practical defense strategies. Andy will break down key cyber trends from 2024 and what to expect in 2025. Jason will showcase the latest Pure Storage innovations, partner updates, and new collaboration with Rubrik.

The Wide World of Cyber Resilience: A CISO’s Insights & 2025 Advances

Organizations everywhere are navigating an unprecedented surge in data complexity and volume that touches every aspect of their operations. As cyber threats evolve and regulatory landscapes shift, the integrity and availability of this data has become fundamental to business continuity. Organizations need storage and backup solutions that don't just protect data—they need strategies that transform data management into a competitive advantage.

The limitations of traditional storage and backup solutions are becoming increasingly apparent as companies face rising costs, operational inefficiencies, and heightened security risks. With ransomware threats escalating, compliance requirements tightening, and sustainability goals taking center stage, organizations must move beyond conventional approaches to data protection. Modern challenges demand transformative solutions that embrace hybrid cloud models, intelligent data tiering, and modernized backup architectures.

This session provides a practical roadmap for modernizing your storage environment and building a robust data protection framework that addresses today's challenges while preparing for tomorrow's demands.

Key Takeaways:
- Evaluate the impact of exponential data growth on your storage infrastructure and learn how to optimize for both efficiency and protection.
- Master hybrid cloud and intelligent data management strategies that reduce costs while enhancing data accessibility and security.
- Implement modern backup solutions that protect against ransomware through immutable storage and automated recovery workflows.
- Discover how DRaaS and advanced resilience strategies can minimize downtime while meeting compliance requirements.
- Build a strategic framework for assessing your current storage environment and prioritizing investments for future scalability.

Transformative Storage and Backup: Building Resilience in a Data-First World

There is so much to consider in setting your cyber resilience strategy that it can be overwhelming even at a single point in time, much less how things change every day.

The key to getting on the right track is keeping things simple and planning for before, during, and after an attack. Get on board and walk through what to consider when building your cyber resilience strategy in all three phases.

Building Cyber Resilience for Today and Tomorrow: Before, During, and After an Attack

Like the Prime Directive in Star Trek - the most important rule or law that must be followed above all others - organizations today require a core set of principles for managing and protecting their most valuable asset: data. Insofar as data is concerned, the prime directive means to find, clean, classify, protect, and contextualize it to mitigate data risks. Oh, and ensure it's backed up properly. Failure to do this could spell disaster.   

Historically, the absence of this prime directive has had catastrophic consequences. The 2008 financial crisis, for example, was significantly amplified by the widespread reliance on flawed data for risk assessment. Today, with the unprecedented volumes of data generated daily, the potential for similar or even greater failures looms large if proactive, data-centric strategies are not implemented.  

In this session, Michele Thomas (Managing Director and Co-Founder of TrustedTec) explores practical, actionable strategies for establishing and enforcing your data's prime directive.  

Join the session to learn:  
- The critical components of a modern data prime directive for your organization.  
- How to build strategies for data storage and backup to combat evolving cybersecurity threats.  
- The cybersecurity and AI challenges that can arise from the exponential growth of data.  
- How to transform raw data into actionable insights for strategic business advantage.  
And more...  
 

Meet the speaker 
Michele Thomas is a visionary leader, data evangelist, digital transformation trailblazer, disruptive thinker, savvy strategist, cybersecurity and identity and access management expert, and in the last decade has been a CISO, CIO, and CTO. She has 30+ years of experience in both the public and private sectors. Michele possesses a BA in Public Administration from George Mason University and a Masters in Public Administration from the Key Executive Leadership Program at American University. Michele and her family live in Maryland.

Data's Prime Directive: Navigating Storage, Backup, and Strategic Activation

Transformative Strategies for Storage Management and Data Backup

The explosion of data and pursuit of high-performance computing are creating a shift in data storage and backup strategies, with immersion cooling at the forefront. This technology promises enhanced efficiency and a total reframing of data center thermodynamics. With immersion cooling, organizations can move past the traditional limits of air cooling - minimizing operational expenses and drastically reducing energy and water use, all while safeguarding the integrity and availability of critical data assets.

The shift to immersion-cooled storage and backup can seem challenging. But with the right support, undergoing this necessary transformation doesn’t need to be all that complex.

In this session, Stephen Zhao (Thermal Management Director – EMEA, BP Castrol) will share tried-and-tested strategies and best practices for designing resilient storage infrastructure with immersion cooling.

Join to explore: 
- Gain a comprehensive understanding of the core principles and transformative advantages of immersion cooling for high-density storage and backup architectures. 
- Identify and strategically mitigate the critical challenges associated with integrating immersion cooling into existing data center infrastructures. 
- Discover the impact of immersion cooling on energy efficiency, operational expenditures, and responsible use of resources. 
- Get insights and best practices from BP Castrol's immersion cooling deployments.  
And more... 
  
Meet the Speaker 
Stephen Zhao is the Thermal Management Director for EMEA in Castrol, where he leads the development of the liquid cooling business in Europe, the Middle East, and Africa. He has many years of experience in the energy industry and expertise in solutions for the data centre industry. He has also held roles in academia, government, and the non-profit sector. Stephen holds degrees from the University of Oxford and University of Toronto.

Bridging AI/HPC with Immersion: Transforming Storage and Data Backup Efficiency

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Bridging AI/HPC with Immersion: Transforming Storage and Data Backup Efficiency

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