Name: Role of Artificial Intelligence in Data Democratization
Start: 2022-06-23T15:00:00Z
End: 2022-06-23T15:00:40.000Z
Location: BrightTALK
Rating: 0

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 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 how will those affect networking planning and operations? What are the biggest challenges facing organizations building networks for AI? And, What are the most important features and capabilities of networking for AI? Join Jim Frey as he helps answer these questions in the latest research on How the AI Revolution will Transform AIOps and Observability.

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

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

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

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

Today's business leaders are racing to adopt Generative AI to streamline operations, transform customer experiences and fuel innovation. But achieving these objectives demands more than technology —success requires a comprehensive GenAI strategy that unites business and IT priorities, establishes robust data governance, and defines clear ethical boundaries. 

In this session, Kader Sakkaria (Head of Global Data Technology at Allied Consulting Group) explores how leaders can strategically implement GenAI across functions to create long-term, sustainable value. Join him to explore common challenges – from data readiness to scaling solutions – and get a strategic framework to overcome them.  

By joining this session, you’ll discover: 
- A strategic framework for aligning GenAI initiatives with business objectives while establishing the governance and ethical guidelines needed to ensure responsible implementation. 
- How to assess organizational data readiness and build the foundational capabilities required to scale GenAI solutions across functions. 
- Best practices for navigating common implementation challenges and measuring ROI as you transition from experimental pilots to enterprise-wide GenAI deployment. 
- And more... 

About the Speaker
Kader Sakkaria is a visionary technology leader with extensive experience leveraging cutting-edge technologies to drive business transformation. As the Head of Global Data Technology at Allied Consulting Group, he has spearheaded innovative AI and data strategies for multinational organizations, focusing on operational efficiency and customer experience. Kader is a recognized expert in enterprise AI adoption, with a strong emphasis on ethical and scalable implementations.

Operationalizing GenAI: From Pilot to Enterprise-Wide Value Creation

Artificial intelligence and Machine learning (AI/ML) is a hot topic in every business at the moment, and there is a growing dialog about what constitutes an Open Model, is it the weights? Is it the data?

Those are important questions, but equally important is ensuring that the tooling and frameworks to train, validate, fine-tune, and perform inference are open source. Storage systems are a crucial component of these workflows, how can open-source solutions address the needs for high capacity and high performance?

Data is key to any and all AI/ML workflows, without it there would be no data to use as an input for model training, re-evaluation and refinement of models, or even just securely storing models once training is complete, especially if they have taken weeks to produce!

Open source solutions like Ceph can provide almost limitless scaling capabilities, both for performance and capacity. In this webinar, learn how Ceph can be used as the backing store for AI/ML workloads.
We’ll cover:
• The demands of AI on storage systems
• How open source Ceph storage fits into the picture
• How to approach Ceph cluster scaling to meet AI’s needs
• How to get started with Ceph

Ceph Storage in a World of AI/ML Workloads

New Memories like MRAM, ReRAM, PCM, or FRAM are vying to replace embedded flash and, eventually, even embedded SRAM.  Are there other memory technologies threatened with similar fates?  What will the memory market look like in another 20 years?
Catch up on the latest in new memory technologies in this fast-paced, entertaining panel, as we explain what these new memory technologies are, the applications that have already adopted them in the marketplace, their impact on computer architectures and AI, the outlook for important near-term changes, and how economics dictate success or failure.  Noted analysts Jim Handy of Objective Analysis and Tom Coughlin of Coughlin Associates, moderated by Arthur Sainio, SNIA PM Special Interest Group Co-Chair, will present the findings of their latest report as they discuss where emerging memories complement CXL, Chiplets, Processing In Memory, Endpoint AI, and wearables, and they explain the inevitability of a conversion from established technologies to new memory types.

A Deep Look at New Memories

The SNIA Cloud Storage Technologies Initiative (CSTI) conducted a poll early in 2024 during a live webinar “Navigating Complexities of Object Storage Compatibility,” citing 72% of organizations have encountered incompatibility issues between various object storage implementations. These results resulted in a call to action for SNIA to create an open expert community dedicated to resolving these issues and building best practices for the industry.  

Since then, SNIA CSTI partnered with the SNIA Cloud Storage Technical Working Group (TWG) and successfully organized, hosted, and completed the first SNIA Cloud Object Storage Plugfest (multi-vendor interoperability testing), co-located at SNIA Developer Conference (SDC), September 2024, in Santa Clara, CA. Participating Plugfest companies included engineers from Dell, Google, Hammerspace, IBM, Microsoft, NetApp, VAST Data, and Versity Software. Three days of Plugfest testing discovered and resolved issues and included a Birds of a Feather (BoF) session to gain consensus on next steps for the industry. Plugfest contributors are now planning two 2025 Plugfest events in Denver in April and Santa Clara in September.

This webinar will share insights into industry best practices, explain the benefits your implementation may gain with improved compatibility, and welcome your client and server cloud object storage team to join us in building momentum. Join us on November 21st where we’ll discuss:  
• Implications on client applications
• Complexity and variety of APIs 
• Access control mechanisms 
• Performance and scalability requirements
• Real-world incompatibilities found in various object storage implementations
• Missing or incorrect response headers
• Unsupported API calls and unexpected behavior

More information:
• SNIA CSTI https://www.snia.org/groups/csti
• SDC'24 https://www.sniadeveloper.org/
• Cloud Object Storage Plugfest (Sept. 2024)  https://www.sniadeveloper.org/cloud-object-storage-plugfest

Building Community to Tackle Cloud Object Storage Incompatibilities

Join us for an insightful webinar on the transformative impact of AI on networking. This session will delve into the various use cases of AI, the nature of traffic for different workloads, and the network impact of these workloads. We will explore the multiple networking challenges posed by AI and how Ethernet is evolving to meet these demands. Special focus will be given to congestion issues during model training, the role of Ultra Ethernet Consortium (UEC), and the specific requirements related to training large language models (LLMs) and other use cases.

Learning Objectives:
- Understand the different types of Network Topologies typically used with AI workloads.
- Identify the nature of traffic for various AI workloads and their impact on networks.
- Learn about the challenges Ethernet faces with AI workloads and the solutions being implemented.
- Explore a specific use case to see how Ethernet addresses bandwidth and congestion issues.

Don’t miss this opportunity to stay ahead in the rapidly evolving field of AI and networking. Register now to secure your spot and gain valuable insights from industry experts!

Ethernet in the Age of AI: Adapting to New Networking Challenges

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

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

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 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

This presentation examines the critical role of storage solutions in optimizing AI workloads, with a primary focus on storage-intensive AI training workloads. We will highlight how AI models interact with storage systems during training, focusing on data loading and checkpointing mechanisms. We will explore how AI frameworks like PyTorch utilize different storage connectors to access various storage solutions. Finally, the presentation will delve into the use of file-based storage and object storage in the context of AI training: 
 
Attendees will:
- Gain a clear understanding of the critical role of storage in AI model training workloads
 - Understand how AI models interact with storage systems during training, focusing on data loading and checkpointing mechanisms
- Learn how AI frameworks like PyTorch use different storage connectors to access various storage solutions.
- Explore how file-based storage and object storage is used in AI training

The Critical Role of Storage in Optimizing AI Training Workloads

As generative AI copilots/ virtual assistants become widely available, companies are often struggling with justifying licensing and training costs, and in measuring ROI. In this session, Metrigy's Irwin Lazar, president and principal analyst, will share insights gathered from hundreds of end-user organizations. 

Attendees will learn: 
- How to measure ROI for generative AI collaboration tools. 
- How best to train users. 
- How generative AI apps are changing collaboration application deployments - How to translate time savings into measurable business value.

Realizing ROI for Generative AI in Collaboration

The “If you deploy it, they will use it” approach doesn’t work when it comes to UCC. Too many IT professionals focus exclusively on the architecture and design for UCC solutions, without taking into account the role of end user usage, adoption, and training to drive the overall success of a UCC. solution. This session we will focus on why end user adoption and training are so important, key findings from a usage and adoption study, and steps to drive adoption in order to get the most out of your UCC solution.

Key Takeaways 
- Why usage and adoption are even more important than the UCC technology itself
- How to develop a UCC end user adoption strategy
- Best practices for UCC training and adoption
- How to overcome some of the roadblocks to driving user adoption

Rethinking Unified Comms User Adoption

How To Choose a Data Warehousing Solution in An Ever-Changing Data Landscape

Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps

Managing Resiliencies for edge data-centre pods (& Clusters)

From Excessive Data Center Energy to Modular, Generative & Sustainable Designs

Reimagining Analytics With an Open Lakehouse

Full Cloud Native Stack

Pragmatic way to accelerate cloud native transformation

Data Architecture & Engineering in 2022

Data Democratization is the final frontier of employee empowerment. Citizen developers are a widely discussed topic, but what about citizen analysts? Analytics can be an indispensable asset from the C-suite all the way to the frontline worker. But there are several challenges executives and non-technical users face in leveraging data for decision-making. How can AI help in democratizing data and transform your organization into a data-driven organization?

Join AI industry analyst Kashyap Kompella in this fast-paced webinar on the current state of data democratization, its challenges, and the best practices of leveraging AI.

Key Takeaways

• Executive overview of the what, why, and how of data democratization.
• Rationale and role of AI for data democratization, including:
o How AI can drive adoption of self-service analytics;
o How AI can enhance the user experience for analytics applications;
o How AI can improve data protection and privacy practices
• Use cases of examples of AI for data democratization.

Role of Artificial Intelligence in Data Democratization

Data Analysis

Data Architecture

Data Analytics

Data Protection

data democratization

Artificial Intelligence

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Role of Artificial Intelligence in Data Democratization

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