Infinidat Products – InfiniBox G4 and InfiniBox SSA

Event: AI Data Infrastructure Field Day 1

Appearance: Infinidat Presents at AI Data Infrastructure Field Day 1

Company: INFINIDAT

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Personnel: Bill Basinas

Infinidat’s presentation at AI Data Infrastructure Field Day 1, led by Bill Basinas, Sr. Director of Product Marketing, focused on their latest storage solutions, the InfiniBox G4 and InfiniBox SSA. Basinas highlighted the unique software-defined architecture of Infinidat’s products, which leverage commodity hardware components to deliver high performance and resilience. The core of their technology, InfuseOS, provides triple redundancy and integrates features like neural cache, which uses a large shared pool of DRAM memory to ensure high performance and cache hit rates. This architecture allows Infinidat to guarantee 100% availability and high performance, making their solutions suitable for mission-critical applications and workloads, including AI tasks like Retrieval Augmented Generation (RAG).

The presentation also detailed the hardware advancements in the G4 series, which transitioned from Intel to AMD EPYC CPUs, resulting in a 31% increase in core count and a 20% improvement in power efficiency. This shift to PCIe Gen 5 and DDR5 DRAM further enhanced the system’s performance. Infinidat’s approach to scalability was also discussed, with the introduction of smaller configurations like the 1400T series, designed for remote data centers and edge deployments. These systems maintain the same enterprise-class architecture and performance, allowing for seamless, non-disruptive scaling. Additionally, Infinidat continues to offer hybrid storage solutions, which are particularly popular for large-scale backup environments.

Infinidat’s InfiniVerse platform was another key topic, providing extensive telemetry and predictive analytics to help customers manage their storage environments. InfiniVerse leverages AI and machine learning to offer insights into storage usage, power consumption, and carbon footprint, aligning with modern ESG goals. The platform supports Infinidat’s white-glove customer service, where technical advisors work closely with clients to optimize their storage infrastructure. The presentation concluded with a discussion on Infinidat’s future plans, including the Mobius controller upgrade program, which promises seamless, in-place upgrades without disrupting system availability, further emphasizing Infinidat’s commitment to innovation and customer satisfaction.


Revolutionizing Enterprise Storage with High Performance and Unmatched Customer Support from Infinidat

Event: AI Data Infrastructure Field Day 1

Appearance: Infinidat Presents at AI Data Infrastructure Field Day 1

Company: INFINIDAT

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Personnel: Bill Basinas

Infinidat, a company founded in 2011 with deep roots in enterprise storage, has established itself as a critical player in the IT storage infrastructure for some of the world’s largest companies. The company’s founder, who previously created EMC Symmetrix and sold XIV to IBM, has leveraged this extensive experience to build Infinidat into a provider of high-performance, high-availability storage solutions. These solutions are designed to handle mission-critical applications and workloads, including databases, ERP systems, and virtual infrastructures. Recently, Infinidat has also expanded its focus to support critical AI workloads, such as Retrieval Augmented Generation (RAG), reflecting the evolving needs of modern enterprises.

Infinidat’s approach to the market is somewhat unique, as it traditionally sold full rack-scale systems but is now offering more flexible consumption options. This flexibility allows customers to integrate Infinidat’s solutions seamlessly into their existing infrastructure without significant changes. The company’s customer base primarily consists of large enterprises, with an average customer managing 17 petabytes of storage. Infinidat’s systems are known for their scalability, capable of handling up to 17 petabytes of effective data, and are designed to provide enterprise-caliber performance, 100% availability resilience, and industry-leading cyber resilience through their InfiniSafe technology.

One of the standout features of Infinidat’s offerings is their “white glove” customer service, which includes comprehensive support from the initial purchase through the entire lifecycle of the product at no additional cost. This service model has flipped the traditional support paradigm on its head, providing customers with technical advisors and eliminating the need for additional support tiers or add-ons. Infinidat’s solutions also contribute to data center consolidation, offering greener, more efficient storage options that reduce floor space, power, cooling, and operational costs. The company’s recent product developments, including the AI-driven Infiniverse platform and the cloud editions of their Fuse OS operating system, further demonstrate their commitment to innovation and addressing the evolving needs of enterprise customers, particularly in the realm of cybersecurity and cyber attack prevention.


Streamline AI Projects with Infrastructure Abstraction from HPE

Event: AI Data Infrastructure Field Day 1

Appearance: HPE Presents at AI Data Infrastructure Field Day 1

Company: HPE

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Personnel: Alexander Ollman

In this presentation, Alex Ollman from Hewlett Packard Enterprise (HPE) discusses the transformative potential of infrastructure abstraction in accelerating AI projects. The focus is on HPE’s Private Cloud AI, a solution designed to simplify the management of complex systems, thereby allowing data engineers, scientists, and machine learning engineers to concentrate on developing and refining AI applications. By leveraging HPE Ezmeral Software, the Private Cloud AI aims to provide a unified experience that maintains control over both the infrastructure and the associated data, ultimately fostering innovation and productivity in AI-driven projects.

Ollman emphasizes the importance of abstracting the underlying infrastructure, including GPU accelerator compute, storage for models, and high-speed networking, into a virtualized software layer. This abstraction reduces the time and effort required to manage these components directly, enabling users to focus on higher-level tasks. HPE’s GreenLake Cloud Platform plays a crucial role in this process by automating the configuration of entire racks, which can be set up with just three clicks. This ease of use is further enhanced by HPE AI Essentials, which allows for the creation and deployment of automations tailored to the unique data structures of different organizations.

The presentation also highlights HPE’s collaboration with NVIDIA to scale the development and deployment of large language models and other generative models. This partnership aims to make these advanced AI components more accessible and scalable for enterprises. HPE’s solution accelerators, part of the Private Cloud AI offering, promise to streamline the deployment of data, models, and applications with a single click. This capability is expected to be formally released by the end of the year, providing a powerful tool for enterprises to manage and scale their AI projects efficiently.


Building a Generative AI Foundation with HPE

Event: AI Data Infrastructure Field Day 1

Appearance: HPE Presents at AI Data Infrastructure Field Day 1

Company: HPE

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Personnel: Alexander Ollman, Edward Holden

Join Hewlett Packard Enterprise’s product team for a deep dive into the AI architecture and infrastructure needed to deploy generative AI at enterprise scale. We’ll explore the essential components—from high-performance compute and storage to orchestration—that power these models. Using real-world case studies, we’ll uncover the intricacies of balancing computational resources, networking, and optimization. Discover how Hewlett Packard Enterprise simplifies this process with integrated solutions.

In the presentation, Alex Ollman and Edward Holden from HPE discuss the comprehensive infrastructure required to support generative AI at an enterprise level, focusing on both hardware and software components. They emphasize the importance of a holistic approach that integrates high-performance computing, storage, and orchestration to manage the complex workflows involved in machine learning operations. The HPE Ezmeral platform is highlighted as a key solution that abstracts the underlying infrastructure, making it easier for data scientists, engineers, and developers to focus on their specific tasks without worrying about the technical complexities of setting up and managing the infrastructure.

The presentation also delves into the roles of different personas within an organization, such as cloud administrators, AI administrators, and AI developers. Each role has specific needs and responsibilities, and HPE’s Private Cloud AI offering is designed to cater to these needs by providing a unified platform that simplifies user management, data access, and resource allocation. The platform allows for seamless integration of various tools and frameworks, such as Apache Airflow for data engineering and Jupyter Notebooks for development, all pre-configured and ready to use. This approach not only accelerates the deployment of AI models but also ensures that the infrastructure can scale efficiently to meet the demands of enterprise applications.

Furthermore, the presentation touches on the collaboration between HPE and NVIDIA to enhance the capabilities of the Private Cloud AI platform. This partnership aims to deliver scalable, enterprise-grade AI solutions that can handle large language models and other complex AI workloads. The integration of NVIDIA’s AI Enterprise stack with HPE’s infrastructure ensures that users can deploy and manage AI models at scale, leveraging the best of both companies’ technologies. The session concludes with a discussion on the support and diagnostic capabilities of the platform, ensuring that organizations can maintain and troubleshoot their AI infrastructure effectively.


A Step-by-Step Guide to Build Robust AI with Hewlett Packard Enterprise

Event: AI Data Infrastructure Field Day 1

Appearance: HPE Presents at AI Data Infrastructure Field Day 1

Company: HPE

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Personnel: Alexander Ollman

Generative AI holds the promise of transformative advancements, but its development requires careful planning and execution. Hewlett Packard Enterprise (HPE) leverages its extensive experience to navigate the intricacies of building enterprise-grade generative AI, covering aspects from infrastructure and data management to model deployment. Alexander Ollman, a product manager at HPE, emphasizes the importance of integrating the needs of those who will use the AI infrastructure into the decision-making process, highlighting the rapid and essential demand for robust AI solutions in the enterprise sector.

Ollman provides a detailed explanation of the evolution and significance of generative AI, particularly focusing on the development of transformer models by Google in 2017, which revolutionized the field by enabling real-time generation of responses. He distinguishes between traditional AI models, which are often specific and smaller, and generative models, which are large, computationally intensive, and designed for general applications. This distinction is crucial for understanding the different infrastructure requirements for each type of AI, as generative models necessitate more substantial computational resources and sophisticated data management strategies.

The presentation underscores the complexity of deploying generative AI applications, outlining a multi-step process that includes data gathering, preparation, selection, model training, and validation. Ollman stresses the importance of automating and abstracting these steps to streamline the process and make it accessible to various personas involved in AI development, from data engineers to application developers. He also highlights the necessity of high-performance infrastructure, such as GPU-accelerated compute and fast networking, to support the large-scale models used in generative AI. By abstracting technical complexities, HPE aims to empower organizations to harness the full potential of generative AI while ensuring reliability and efficiency in their AI deployments.


Managing Google Cloud Storage at Scale with Gemini

Event: AI Data Infrastructure Field Day 1

Appearance: Google Cloud Presents at AI Data Infrastructure Field Day 1

Company: Google Cloud

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Personnel: Manjul Sahay

In the presentation, Manjul Sahay from Google Cloud discusses the challenges and solutions for managing vast amounts of data in Google Cloud Storage, particularly for enterprises involved in data-intensive activities like autonomous driving and drug discovery. He highlights that traditional methods of data management become ineffective when dealing with billions of objects and petabytes of data. The complexity is compounded by the need for security, cost management, and operational insights, which are difficult to achieve at such a large scale. To address these challenges, Google Cloud has developed new capabilities to streamline the process, making it easier for customers to manage their data efficiently.

One of the key solutions introduced is the Insights Data Set, which aggregates metadata from billions of objects and thousands of buckets into BigQuery for analysis. This daily snapshot of metadata includes custom tags and other relevant information, allowing users to gain insights without the need for extensive manual querying and scripting. This capability is designed to be user-friendly, enabling even non-experts to perform complex data analysis with just a few clicks. By leveraging BigQuery’s powerful tools, users can generate actionable insights quickly, which is crucial for maintaining security and compliance, as well as optimizing storage usage and costs.

Additionally, Google Cloud has integrated AI capabilities through Gemini, a natural language interface that allows users to query metadata in real-time without needing specialized knowledge. This feature democratizes data management by shifting some responsibilities from storage admins to end-users, making the process more efficient and less error-prone. Gemini also provides verified answers to common questions, ensuring accuracy and reliability. The overall goal of these innovations is to help enterprises manage their data at scale, keeping it secure, compliant, and ready for AI applications, thereby enabling them to focus on their core business objectives.


Google Cloud Vertex Al & Google Cloud NetApp Volumes

Event: AI Data Infrastructure Field Day 1

Appearance: Google Cloud Presents at AI Data Infrastructure Field Day 1

Company: Google Cloud

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Personnel: Raj Hosamani

Rajendraprasad Hosamani from Google Cloud Storage presented on the integration of Google Cloud Vertex AI with Google Cloud NetApp Volumes, emphasizing the importance of grounding AI agents in bespoke, enterprise-specific data. He highlighted that AI workloads are diverse and that agents can significantly enhance user experiences by providing interactive, personalized, and efficient data sharing. For agents to be effective, they must be grounded in the specific truths of an organization, which requires seamless data integration from various sources, whether on-premises or in the cloud. This integration also necessitates robust governance to ensure data is shared appropriately within the enterprise.

Vertex AI, Google’s flagship platform for AI app builders, offers a comprehensive suite of tools categorized into model garden, model builder, and agent builder. The model garden allows users to select from first-party, third-party, or open-source models, while the model builder focuses on creating custom models tailored to specific business needs. The agent builder facilitates the responsible and reliable creation of AI agents, incorporating capabilities like orchestration, grounding, and data extraction. This platform supports no-code, low-code, and full-code development experiences, making it accessible to a wide range of users within an organization.

The integration of NetApp Volumes with Vertex AI enables the use of NetApp’s proven OnTap storage stack as a data store within Vertex AI. This allows for the seamless incorporation of enterprise data into AI development workflows, facilitating the creation, testing, and fine-tuning of AI agents. Raj demonstrated how this integration can elevate user experiences through various implementations, such as chat agents, search agents, and recommendation agents, all of which can be developed with minimal coding. This integration empowers organizations to leverage their accumulated data to create rich, natural language-based interactions for their end users, thereby enhancing the overall value derived from their AI investments.


Google Cloud Storage for AI ML Workloads

Event: AI Data Infrastructure Field Day 1

Appearance: Google Cloud Presents at AI Data Infrastructure Field Day 1

Company: Google Cloud

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Personnel: Dave Stiver

In his presentation on Google Cloud Storage for AI ML workloads, Dave Stiver, Group Product Manager at Google Cloud, discussed the critical role of cloud storage in the AI data pipeline, particularly focusing on training, checkpoints, and inference. He emphasized the importance of time to serve for machine learning developers, highlighting that while scalability and performance are essential, the ability to interact with object storage through a file interface is crucial for developers who are accustomed to file systems. Stiver introduced two key features, GCS FUSE and Anywhere Cache, which enhance the performance of cloud storage for AI workloads. GCS FUSE allows users to mount cloud storage buckets as local file systems, while Anywhere Cache provides a local zonal cache that significantly boosts data access speeds by caching data close to the accelerators.

Stiver shared a use case involving Woven, the autonomous driving division of Toyota, which transitioned from using Lustre to GCS FUSE for their training jobs. This shift resulted in a 50% reduction in training costs and a 14% decrease in training time, demonstrating the effectiveness of the local cache feature in GCS FUSE. He also explained the functionality of Anywhere Cache, which allows users to cache data in the same zone as their accelerators, providing high bandwidth and efficient data access. The presentation highlighted the importance of understanding the consistency model of the cache and how it interacts with the underlying storage, ensuring that users can effectively manage their data across different regions and zones.

The discussion then shifted to the introduction of Parallel Store, a fully managed parallel file system designed for high-throughput AI workloads. Stiver explained that Parallel Store is built on DAOS technology and targets users who require extremely high performance for their AI training jobs. He emphasized the importance of integrating storage solutions with cloud storage to optimize costs and performance, particularly for organizations that need to manage large datasets across hybrid environments. The presentation concluded with a focus on the evolving landscape of AI workloads and the need for tailored storage solutions that can adapt to the diverse requirements of different applications and user personas within organizations.


Workload and AI-Optimized Infrastructure from Google Cloud

Event: AI Data Infrastructure Field Day 1

Appearance: Google Cloud Presents at AI Data Infrastructure Field Day 1

Company: Google Cloud

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Personnel: Sean Derrington

Sean Derrington from Google Cloud’s storage group presented on the company’s efforts to optimize AI and workload infrastructure, focusing on the needs of large-scale customers. Google Cloud has been working on a comprehensive system, referred to as the AI hypercomputer, which integrates hardware and software to help customers efficiently manage their AI tasks. The hardware layer includes a broad portfolio of accelerators like GPUs and TPUs, tailored for different workloads. The network capabilities of Google Cloud ensure predictable and consistent performance globally. Additionally, Google Cloud offers various framework packages and managed services like Vertex AI, which supports different AI activities, from building and training models to serving them.

Derrington highlighted the recent release of Parallel Store, Google Cloud’s first managed parallel file system, and Hyperdisk ML, a read-only block storage service. These new storage solutions are designed to handle the specific demands of AI workloads, such as training, checkpointing, and serving. Parallel Store, for instance, is built on local SSDs and is suitable for scratch storage, while Hyperdisk ML allows multiple hosts to access the same data, making it ideal for AI applications. The presentation also touched on the importance of selecting the right storage solution based on the size and nature of the training data set, checkpointing needs, and serving requirements. Google Cloud’s open ecosystem, including partnerships with companies like SciCom, offers additional storage options like GPFS-based solutions.

The presentation emphasized the need for customers to carefully consider their storage requirements, especially as they scale their AI operations. Different storage solutions are suitable for different scales of operations, from small-scale jobs requiring low latency to large-scale, high-throughput needs. Google Cloud aims to provide consistent and flexible storage solutions that can seamlessly transition from on-premises to cloud environments. The goal is to simplify the decision-making process for customers and ensure they have access to the necessary resources, such as H100s, which might not be available on-premises. The session concluded with a promise to delve deeper into the specifics of Parallel Store and other storage solutions, highlighting their unique capabilities and use cases.


A Demonstration of the MinIO Enterprise Object Store – The AI-Centric Feature Set

Event: AI Data Infrastructure Field Day 1

Appearance: MinIO Presents at AI Data Infrastructure Field Day 1

Company: MinIO

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Personnel: Dil Radhakrishnan, Jonathan Symonds

MinIO’s AI feature set is expansive, but there are core features that allow enterprises to operate at exascale. Those include observability, security, performance, search and manageability. In this segment, MinIO goes from bucket creation to RAG-deployment, emphasizing each core AI feature and why it matters to enterprises with data scale challenges that run from PBs to EBs and beyond.

MinIO’s presentation at the AI Data Infrastructure Field Day 1 focused on demonstrating the capabilities of their enterprise object store, particularly its AI-centric features. The core features highlighted include observability, security, performance, search, and manageability, which are essential for enterprises operating at exascale. The presentation began with an overview of the global console, which allows for the management of multiple sites across different cloud environments, both public and private. This console integrates key management systems for object-level encryption, providing granular security that is crucial for large-scale data operations.

The demonstration showcased how MinIO handles various AI and ML workloads, emphasizing the importance of data preprocessing and transformation in data lakes. The observability feature was particularly highlighted, showing how MinIO’s system can monitor and manage the health of the cluster, including drive metrics, CPU usage, and network health. This observability is crucial for maintaining performance and preemptively addressing potential issues. The presentation also covered the built-in load balancer, which ensures even distribution of workloads across nodes, and the in-memory caching system that significantly boosts performance by reducing data retrieval times.

Additionally, the presentation touched on the catalog feature, which allows for efficient searching and managing of metadata within massive namespaces. This feature is particularly useful for identifying and addressing issues such as excessive requests from buggy code. The session concluded with a discussion on the integration of MinIO with AI/ML workflows, including the use of Hugging Face for model training and the implementation of RAG (Retrieval-Augmented Generation) systems. This integration ensures that enterprises can seamlessly manage and scale their AI/ML operations, leveraging MinIO’s robust and scalable object storage solutions.


Why MinIO is Winning the Private Cloud AI Battle

Event: AI Data Infrastructure Field Day 1

Appearance: MinIO Presents at AI Data Infrastructure Field Day 1

Company: MinIO

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Personnel: Rakshith Venkatesh

Many of the largest private cloud AI deployments run on MinIO and most of the AI ecosystem is integrated or built on MinIO, from Anyscale to Zilliz. In this segment, MinIO explains the features and capabilities that make it the leader in high-performance storage for AI. Those include customer case studies, the DataPod reference architecture and the features that AI-centric enterprises deem requirements.

MinIO has established itself as a leader in high-performance storage for AI, particularly in private cloud environments. The company’s software-defined, cloud-native object store is designed to handle exabyte-scale deployments, making it a preferred choice for large AI ecosystems. MinIO’s S3-compatible object store is highly integrated with various AI tools and platforms, from Anyscale to Zilliz, which has contributed to its widespread adoption. The company emphasizes its ease of integration, flexibility with hardware, and robust performance, which are critical for AI-centric enterprises. MinIO’s architecture allows customers to bring their own hardware, supporting a range of chipsets and networking configurations, and is optimized for NVMe drives to ensure high throughput and performance.

A notable case study highlighted in the presentation involved a customer needing to deploy a 100-petabyte cluster over a weekend. MinIO’s solution, which does not require a separate metadata database and offers a complete object store solution rather than a gateway, was able to meet the customer’s needs efficiently. The deployment showcased MinIO’s ability to scale quickly and handle large volumes of data with high performance, achieving 2.2 terabytes per second throughput in benchmarking tests. This performance was achieved using commodity off-the-shelf hardware, demonstrating MinIO’s capability to deliver enterprise-grade storage solutions without the need for specialized equipment.

MinIO also addresses operational challenges through features like erasure coding, Bitrot protection, and a Kubernetes-native operator for seamless integration with cloud-native environments. The company provides observability tools to monitor the health and performance of the storage infrastructure, ensuring data integrity and efficient resource utilization. MinIO’s reference architecture, DataPod, offers a blueprint for deploying large-scale AI data infrastructure, guiding customers on hardware selection, networking configurations, and scalability. This comprehensive approach, combined with MinIO’s strong performance and ease of use, positions it as a leading choice for enterprises looking to build robust AI data infrastructures.


Why AI is All About Object Storage with MinIO

Event: AI Data Infrastructure Field Day 1

Appearance: MinIO Presents at AI Data Infrastructure Field Day 1

Company: MinIO

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Personnel: Jonathan Symonds

Almost every major LLM is trained on an object store. Why is that? The answer lies in the unique properties of a modern object store – performance (throughput and IOPS), scale and simplicity. In this segment, MinIO details how AI scale is stressing traditional technologies and why object storage is the de facto storage standard for modern AI architectures.

Jonathan Symonds kicks off the presentation by MinIO at AI Data Infrastructure Field Day 1, describing the critical role of object storage in the realm of artificial intelligence (AI). Symonds begins by highlighting the unprecedented scale of data involved in AI, where petabytes have become the new terabytes, and the industry is rapidly approaching exabyte-scale challenges. Traditional storage technologies like NFS are struggling to keep up with this scale, leading to a shift towards object storage, which offers the necessary performance, scalability, and simplicity. Symonds emphasizes that the distributed nature of data creation, encompassing various formats such as video, audio, and log files, further necessitates the adoption of object storage to handle the massive and diverse data volumes efficiently.

Symonds also addresses the economic and operational considerations driving the adoption of object storage in AI. Enterprises are increasingly repatriating data from public clouds to private clouds to achieve better cost control and economic viability. This shift is facilitated by the cloud operating model, which includes containerization, orchestration, and APIs, making it easier to manage large-scale data infrastructures. The presentation underscores the importance of control over data, with Symonds citing industry leaders who advocate for keeping data within the organization’s control to maintain competitive advantage. This control is crucial for enterprises to maximize the value of their data and protect it from external threats.

The presentation concludes by discussing the unique features of object storage that make it ideal for AI workloads. These include the simplicity of the S3 API, fine-grained security controls, immutability, continuous data protection, and active-active replication for high availability. Symonds highlights that these features are essential for managing the performance and scale required by modern AI applications. He also notes that the simplicity of object storage scales operationally, technically, and economically, making it a robust solution for the growing demands of AI. The presentation reinforces the idea that object storage is not just a viable option but a necessary one for enterprises looking to harness the full potential of AI at scale.


Rethinking Data Center Infrastructure Automation with Nokia – Operations Made Easy

Event: Networking Field Day Exclusive with Nokia

Appearance: Rethinking Data Center Infrastructure Automation

Company: Nokia

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Personnel: Bruce Wallis

Bruce Wallis, Senior PLM on the Event Driven Automation (EDA) solution shifts gears to explain the operational capabilities of the data center fabric with a focus on the state of the network and the way that EDA abstractions reduce the complexity of the day to day tasks for data center operations teams.


Rethinking Data Center Infrastructure Automation with Nokia – Fabric Creation

Event: Networking Field Day Exclusive with Nokia

Appearance: Rethinking Data Center Infrastructure Automation

Company: Nokia

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Personnel: Bruce Wallis

Bruce Wallis, Senior PLM on the Event Driven Automation (EDA) solution delves deeper into how EDA automates the key deployments steps of setting up a new data center fabric, including the creation of an underlay network (the fabric) and overlay services (EVPN) via the power of the EDA app-store.


Rethinking Data Center Infrastructure Automation with Nokia – EDA Overview

Event: Networking Field Day Exclusive with Nokia

Appearance: Rethinking Data Center Infrastructure Automation

Company: Nokia

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Personnel: Bruce Wallis

Bruce Wallis, Senior PLM on the Event Driven Automation (EDA) solution explains the thinking behind the EDA infrastructure automation engine and the core foundation of the solution for data center networks.


Networking Field Day Exclusive with Nokia Delegate Roundtable

Event: Networking Field Day Exclusive with Nokia

Appearance: Networking Field Day Exclusive with Nokia Delegate Roundtable

Company: Nokia

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Personnel: Tom Hollingsworth

The state of data center automation is fragmented and too bespoke. The next generation of IT specialists aren’t going to be building their strategy from scratch. They’ll be using familiar tools and working with comfortable platforms. In this roundtable discussion, Tom Hollingsworth is joined by the Networking Field Day Exclusive delegates as well as employees from Nokia to discuss Nokia EDA and how it is transforming the landscape of data center networking automation. They also discuss the role that management has to play in the process and how we can better prepare our replacements for the future.


The Path Towards Consumable Network Automation with Nokia

Event: Networking Field Day Exclusive with Nokia

Appearance: Nokia for Data Center Networking

Company: Nokia

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Personnel: Wim Henderickx

Network automation has been talked about for over 10 years, yet the adoption of automation tools within the enterprise network is sporadic, with many only deploying islands of automation. Nokia IP Head of Technology and Architecture and Bell Labs Fellow, Wim Henderickx will discuss what needs to change to make the network more consumable. Through open and extensible frameworks, the aspirations of network automation are within reach, Wim will tell you how.


Nokia for IP Networking with Vach Kompella

Event: Networking Field Day Exclusive with Nokia

Appearance: Nokia for Data Center Networking

Company: Nokia

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Personnel: Vach Kompella

In a rapidly evolving tech landscape, Nokia has emerged as a formidable player in global IP networking, captivating the attention of technical engineers worldwide. Nokia is not just keeping pace with the demands of modern networking but is also setting new standards. Hear from Vach Kompella, the leader of the Nokia IP team on how they achieve best-in-industry software quality, hardware capacities and operational toolsets that allow IP networkers to build at speed and with confidence.


Nokia for the Data Center and Beyond

Event: Networking Field Day Exclusive with Nokia

Appearance: Nokia for Data Center Networking

Company: Nokia

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Personnel: Michael Bushong

Industry veteran Michael Bushong has recently joined the Nokia team to lead business development for IP solutions into the data center. What piqued Mikes interest in what Nokia was building and ultimately drove him to join? Mike will cover his view from the outside and provide insights into Nokia and the key differentiations that sets the solution set apart from the competition.


The AI Development Lifecycles with Guy Currier of The Futurum Group

Event: Edge Field Day 3

Appearance: Ignite Talks at Edge Field Day 3

Company: Ignite, The Futurum Group

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Personnel: Guy Currier

Guy Currier from The Futurum Group discusses the AI development lifecycle, emphasizing that AI is not an application in itself but rather a service integrated into applications. He begins by clarifying a common misconception: AI is often thought of as a standalone application, but in reality, it is a service that enhances applications by providing intelligent responses or actions. For example, a chat application may use AI to generate responses, but the AI is just one component of the broader application. This distinction is crucial for organizations looking to adopt AI, as they need to understand that AI development involves creating services that can be integrated into various applications, rather than building a single AI “application.”

Currier outlines three distinct life cycles in AI development. The first involves foundational models, such as GPT or DALL-E, which are typically developed by large organizations or cloud providers. While companies can create their own foundational models, it is often more practical to adopt existing models and tune them using proprietary data. This tuning process is where organizations begin to develop their own intellectual property, as they adapt the foundational models to their specific needs. For instance, companies like Thomson Reuters have incorporated AI into their legal services, using proprietary legal data to enhance the AI’s capabilities. This tuning process follows a development cycle similar to traditional software development, involving infrastructure, tooling, and iterative testing.

The third life cycle Currier discusses is Retrieval Augmented Generation (RAG), which adds contextual data to user prompts. RAG involves searching for relevant information, either from internal or external sources, to enhance the AI’s responses. This process requires its own development and monitoring, as the quality and relevance of the contextual data are critical to the AI’s performance. Currier emphasizes that these three life cycles—foundational model development, model tuning, and RAG—each have distinct infrastructure and development strategies. Organizations must address all three to create AI services that are responsive, contextual, and tailored to specific business scenarios.


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