Name: Strategic Edge Computing: Elevating Security and Performance for Enterprises
Start: 2024-11-06T22:00:00Z
End: 2024-11-06T22:00:29.000Z
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

As organizations embrace the cloud-native edge, the convergence of digital and physical infrastructures has created both unprecedented opportunities and challenges. Security, traditionally discussed in fragmented pieces, now requires a holistic, value-driven approach. This session explores how concepts of "accelerated readiness" in cloud-native edge environments can comprehensively address risk management while streamlining compliance.

We will delve into the critical role that infrastructure modernization plays in closing the gap between regulatory requirements and operational efficiency, turning the security challenges into realized business value. Oftentimes, the edge serves as the nexus where digital command and control meet physical assets.   In this evolving area, achieving operational excellence demands a robust governance, risk, and compliance (GRC) strategy.

By focusing on edge opportunities, this session will explore a high-level and cohesive approach that addresses risk management framework requirements, accelerates compliance, and ensures that the edge platform - along with the business operations it supports - has the best possible security posture.

Accelerated Readiness: Securing the Cloud Native Edge

As the digital landscape expands, edge computing emerges as a pivotal technology, pushing data processing to the frontier of the network to drive real-time insights and actions. However, the integration of edge computing with cloud infrastructure presents unique challenges and opportunities, especially in terms of security and seamless operations. This presentation aims to explore edge computing innovations, focusing on the strategies and technologies that ensure secure and fluid integration with cloud services.

This session will start by examining the current state of edge computing and its integral role in supporting IoT, AI, and real-time analytics applications across various industries. We will navigate through the architectural considerations for integrating edge computing with cloud platforms, highlighting the importance of creating a cohesive environment that leverages the strengths of both technologies.

We will then address security in edge-to-cloud integration. We will dissect the unique security challenges posed by distributed computing models and discuss strategies to safeguard data integrity, confidentiality, and availability. This includes the adoption of advanced encryption methods, secure access controls, and robust identity management systems, as well as the implementation of consistent security policies across the edge and cloud.

Join us in exploring how your organization can navigate the complexities of edge-to-cloud integration, harnessing the power of edge computing innovations to drive competitive advantage in a connected world.

Bridging the Gap: Enhancing Security in Edge-to-Cloud Integration for Next-Generation Computing

The rapid expansion of edge computing has introduced new cybersecurity challenges, particularly in decentralized and distributed environments. This presentation explores the emerging role of Generative AI (GenAI) in addressing these challenges and potentially reshaping the landscape of edge security.

We'll examine the current state of threat detection and response in edge computing, discussing its unique vulnerabilities and the limitations of traditional security approaches. The session will then delve into how GenAI technologies are being applied to enhance real-time threat detection, enable predictive analytics, and facilitate automated responses.

Through a series of case studies and theoretical models, we'll explore the potential benefits and limitations of GenAI in edge security. We'll discuss the broader implications of this technology for the future of cybersecurity, including ethical considerations and potential areas for further research and development.

Key Takeaways:
- Understand the specific security challenges unique to edge computing environments.
- Gain insights into the current and potential applications of GenAI in cybersecurity.
- Evaluate the effectiveness of GenAI-powered solutions in real-time threat detection and response.
- Hear real-world case studies of GenAI in action across various industries.

About the Speaker
Inna Ushakova is a pioneering tech entrepreneur who has built two successful and recognized companies, Scalarr and AI EdgeLabs, in the fields of AI-driven fraud prevention and cybersecurity.

Leveraging GenAI for Advanced Threat Detection and Response in Edge Computing

According to IDC, worldwide spending on cloud computing is projected to surpass $1.35 trillion by 2027. As these costs continue to rise, corporate CIOs and CFOs are collaborating to assess the use of cloud technologies in their business growth strategies. As Akamai discovered through Project Cirrus, it is possible to realize savings by undertaking a comprehensive cloud services reassessment and applying core principles to optimize cloud services. Join us for an upcoming webinar where we will discuss how Akamai leveraged distributed cloud computing to:

• Lower cloud costs by 40% in one year
• Improve performance by over 40% faster queries
• Facilitate transition from cloud-friendly to cloud-native

During the discussion, Akamai leaders will help attendees better understand their cloud utilization and start to gain control over cloud spending, while improving performance and agility.

The Cloud Spend Conundrum: Gain Control of Ballooning Cloud Costs

In today’s rapidly evolving digital landscape, organisations are increasingly harnessing the power of edge computing to drive innovation and maintain a competitive advantage. By bringing computational resources closer to the source of data generation, edge computing minimises latency and reduces bandwidth use, leading to more efficient operations.

As AI-driven applications proliferate, the adoption of edge computing is set to accelerate due to its ability to synthesise and analyse data at unprecedented speeds. This trend underscores the importance of understanding and embracing edge strategies to stay at the forefront of technological advancement.

About this webinar, our experts will cover:
• Business drivers and technical requirements for edge computing
• Traditional solutions vs. novel approaches
• Deploying, managing, and protecting applications at the edge
• Practical approaches to Edge AI and federated learning
 
Join us to gain actionable insights and solutions for effectively managing the edge ecosystem.

AI on the Edge

Edge computing plays a crucial role in industrial automation, particularly in enhancing communications. Here’s how:

1. REDUCED LATENCY:  By processing data closer to the source, edge computing minimizes the need to send data to a centralized cloud or data center. This significantly reduces latency, allowing for near-real-time decision-making and control.

2. IMPROVED RELIABILITY: Industrial environments often have connectivity challenges, such as network interruptions or bandwidth limitations. Edge computing ensures that essential operations can continue even if the connection to the central cloud is disrupted, enhancing system reliability and uptime.

3. EFFICIENT BANDWIDTH USAGE: Edge computing reduces the amount of data that needs to be transmitted to the cloud by filtering, aggregating, and analyzing data locally. Only the most relevant or summarized data is sent, which optimizes bandwidth usage and reduces communication costs.

4. ENHANCED SECURITY: Sensitive data can be processed and stored locally at the edge, minimizing the risk of exposure during transmission to the cloud. This decentralized approach can improve data security and compliance with regulations, especially in industries with stringent data protection requirements.

5. SCALABILITY: As industrial operations grow and more devices are connected (through IoT expansion), edge computing can help manage the increased data load without overwhelming central systems. It allows for scalable deployment of resources where they are needed most, at the edge of the network.

6. SUPPORT FOR REAL-TIME ANALYTICS AND AI: Edge computing enables the deployment of AI and machine learning algorithms directly at the production site. This capability supports real-time analytics and decision-making, improving process efficiency and productivity without relying on distant cloud resources.

Tune into this session to learn more.

The Value of Industrial Communications in Edge Computing

As the edge-cloud continuum reshapes distributed computing, the ability to dynamically shift computation and data processing between the edge and the cloud is crucial. This flexibility optimizes performance and efficiency for real-time applications, AI orchestration, and mission-critical workloads. However, as edge-to-cloud deployments grow, so do the demands for customizable solutions that meet security, scalability, and performance requirements.

Join Wind River Linux Product Management experts Lexi Schroeder and Derek Straka in a deep dive into the evolving landscape of edge-to-cloud systems. Discover a new, adaptable enterprise-ready Linux derivative with modern tools to create a unified tech stack, streamline maintenance and reduce operational overhead. 

Key Takeaways:
• Get introduced to a new, freely available open-source Linux solution designed for consistency across various devices and environments.
• Learn about the latest requirements and optimizations for Linux platforms tailored for enterprise-wide use cases.
• Explore how to accelerate innovation in distributed networks to meet the changing needs of edge to cloud systems. 

Don’t miss this opportunity to gain actionable insights and strategies to optimize your edge-to-cloud deployments.

Unlocking the Full Potential of Cloud-to-Edge with a New Linux Approach

As organizations grow more reliant on real-time decision-making and data processing, edge AI has emerged as a critical technology to drive business value by enhancing speed, automation, and intelligence at the edge. Yet, the road to successfully deploying AI at the edge is complex,
 
This session will take you through a comprehensive exploration of edge AI, focusing on practical business outcomes and real-world use cases across industries like healthcare, manufacturing, and smart cities. 
 
By the end of this session, you will not only gain the technical knowledge to optimize edge AI for real-time data processing and decision-making but also understand how to drive tangible business value—from improving operational efficiency to reducing latency and delivering measurable ROI on your AI investments.
 
3 Key Takeaways:
1. Drive Business Outcomes with Edge AI: Learn how to harness the power of edge AI to deliver measurable business value, such as improved operational efficiency, cost reduction, and faster decision-making.
2. Overcome Industry-Specific Challenges: Explore solutions to key industry challenges, from regulatory compliance in healthcare to cost optimization in manufacturing, ensuring your edge AI strategy aligns with business goals.
3. Implement Scalable Edge Infrastructure: Gain actionable insights into building a scalable edge-to-cloud architecture, managing data flows securely, and deploying AI models in real-time with a future-proof infrastructure.
 
Who Should Attend: This session is ideal for CIOs, CTOs, and IT leaders focused on leveraging edge AI to drive business outcomes, enhance real-time decision-making, and optimize infrastructure. It also caters to IT architects, engineers, AI/ML practitioners, and industry professionals in sectors like healthcare, manufacturing, and IoT, who are looking for practical strategies to overcome specific industry challenges while deploying scalable, secure edge AI solutions.

Maximizing Business Impact and ROI with AI at the Edge: Harnessing Analytics, CI/CD, and Scalable Infrastructure

In today's rapidly evolving technological landscape, the convergence of DevOps and Edge Computing presents a powerful opportunity for innovation, particularly in distributed environments. This session explores how organizations can leverage the combined strengths of DevOps and edge computing to drive faster, more reliable, and secure deployments at the edge. Edge computing enables data processing closer to where it is generated, and when combined with DevOps practices such as automation and continuous integration, it opens the door to transformative possibilities across industries.

Key aspects covered include the low latency and real-time processing capabilities of edge computing and how DevOps can streamline infrastructure management at the edge. The session also addresses the challenges of deploying applications in resource-constrained, distributed environments and offers best practices to overcome these obstacles. Real-world case studies in smart cities, industrial IoT, and retail will demonstrate the practical benefits of DevOps at the edge.

Key Takeaways:
- Synergy of DevOps and Edge Computing.
- Overcoming challenges in edge environments.
- Real-world applications and future trends.

DevOps Meets the Edge: Accelerating Innovation in Distributed Environments

Discover the keys to maximizing reliability and resiliency in edge computing environments. As organizations increasingly rely on edge computing for critical operations, ensuring the robustness of these systems becomes imperative.

This webinar offers deep insights into the latest techniques and technologies for enhancing edge computing reliability and resilience. Craig Theriac, Scale Computing's VP of Product Management will share practical approaches to mitigate risks, optimize performance, and fortify infrastructure against potential disruptions.

Join us to gain invaluable knowledge and actionable strategies to bolster your edge computing capabilities.

Maximizing Edge Computing Reliability and Resiliency

Innovations in Edge Computing

Traditional application hosting strategies are increasingly redundant. As user expectations for instant access and seamless experiences grow, businesses are facing challenges like increased latency and security risks that hinder performance and harm customer satisfaction. 

Edge computing emerges as a transformative strategy that addresses these challenges by bringing computing resources closer to the end-user. This decentralization reduces latency and opens new avenues for enhanced security and scalability, allowing enterprises to host their applications more effectively alongside existing on-premises and cloud infrastructures.

In this session, Harri Gollipalli (Senior Software Development Manager at PayPal) will dive into the organizational approach to adopting edge computing. Join the session to get practical strategies for deploying edge solutions, enhancing security frameworks, and significantly improving application performance—all critical elements for staying competitive in a rapidly changing market.

Key Takeaways
- Enhanced Security: Learn how edge computing can reinforce your enterprise's security posture.
- Efficient Deployment: Gain insights into deploying applications at the edge for optimal performance.
- Improved Performance: Explore techniques to reduce latency and elevate the overall customer experience.
- And more...

Strategic Edge Computing: Elevating Security and Performance for Enterprises

edge computing

Cyber Security

latency

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Strategic Edge Computing: Elevating Security and Performance for Enterprises

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