Name: Addressing the Hidden Costs of AI
Start: 2024-02-27T18:00:00Z
End: 2024-02-27T18:00:55.000Z
Location: BrightTALK
Rating: 4.7

SNIA is a not-for-profit global organization made up of corporations, universities, startups, and individuals. The members collaborate to develop and promote vendor-neutral architectures, standards, and education for management, movement, and security for technologies related to handling and optimizing data. SNIA focuses on the transport, storage, acceleration, format, protection, and optimization of infrastructure for data.

Large Language models (LLMs) based on Transformers architecture have demonstrated the state-of-the-art performance in different code generation benchmarks such as MBPP and HumanEval. In this talk, we will demonstrate how we have used open source LLM models to develop a code generation workflow that can be trained internally in an on-prem infrastructure and used for improving developer productivity by aiding in tasks such as unittest generation, code documentation, code refactoring, code translation, search, and code alignment.

Empowering Developers: Exploring LLM Models for Code Generation

The digital landscape is in hyperdrive, demanding an IT metamorphosis that transcends mere tools. Enter AIOps – not just a technological upgrade, but a paradigm shift redefining how we approach IT operations. This presentation delves beyond the nuts and bolts, unveiling AIOps as a revolution that infuses AI's intelligence into the very fabric of IT thinking and processes.
Key Themes:
• From Dev to Production and Reactive to Proactive: Revolutionizing the IT Mindset: We'll move beyond the "fix it when it breaks" mentality, embracing a shift left, a future-proof approach where AI analyzes risk, anticipates issues, prescribes solutions, and learns continuously.
• Beyond Siloed Solutions: Embracing Holistic Collaboration:  AIOps fosters seamless integration across departments, applications, and infrastructure, promoting real-time visibility and unified action.
• Automating the process: From Insights to Intelligent Action: Dive into the world of self-healing IT, where AI-powered workflows and automation resolve issues and optimize performance without human intervention.

AIOps: Reactive to Proactive – Revolutionizing the IT Mindset

Data is one of the most critical resources of our time. Storage for data is always a critical architectural element in any data center. There are considerations for storage: performance, scalability, reliability, etc. A decade ago, the market was aggressively embracing public storage because of its agility and scalability. In the last few years, people are rethinking that approach, moving toward on-premises storage with cloud consumption models. The new cloud native architecture on-premises has the promise of the traditional data center’s security and reliability with cloud agility and scalability. 
Ceph, an enterprise unified SDS, is the perfect solution for this cloud native on-premises architecture. In this webinar, we will describe how Ceph is uniquely qualified to satisfy this architecture and how the technology community is investing to enable the vision of “Ceph, the Linux of Storage Today”.

Ceph: The Linux of Storage Today

What new storage trends are developing in the coming year? What applications and other factors are driving these trends? Learn from this discussion between industry experts Jeff Janukowicz, Research Vice President at IDC; Brian Beeler, Owner and Editor In Chief, StorageReview.com; and Cameron T. Brett, SNIA STA Forum Chair.

This discussion will cover:

·      How are AI and machine learning affecting storage needs?

·      What is the state of the storage industry in 2024?

·      Security concerns being addressed in data storage.

·      EDSFF E1 and E3: should you make the switch?

·      Is SAS dead? What is the role of SAS in the future of storage?

·      How to make data storage sustainable for current and future need.

Hear about applications driving upcoming trends and learn about market data illustrating the assertions. This promises to be a lively session and you don’t want to miss it!

Storage Trends 2024

With the emergence of Conversational AI tools like Chat GPT and Google Bard, the world has been exposed to incredible new possibilities of technologies with the help of Large Language Models (LLM). A large language model is a type of artificial intelligence algorithm that uses deep learning techniques and massively large data sets accompanied with huge computation infrastructure. However, training LLMs is a complex task which requires substantial computational resources and infrastructure. Fine-tuning large language models (LLMs) for domain-specific data has emerged as a crucial technique to enhance their performance in specialized tasks and industries. In this talk we give an overview of the basic concepts of LLMs , their pre-training process, highlighting the transfer learning paradigm that forms the basis of fine-tuning. 

We will look into the preparatory steps required for successful fine-tuning, including dataset acquisition, cleaning, and structuring. Furthermore, we will discuss the workings of the fine-tuning process which involves adapting the pre-trained LLM’s parameters to domain-specific language patterns, contextual nuances, and task requirements. Architectural considerations, such as selecting appropriate model sizes, are explored in relation to the domain’s computational resources and target task complexity. We evaluate different fine-tuning approaches, ranging from traditional fine-tuning to more advanced techniques like adapter-based architectures. It covers techniques to prevent overfitting, including data augmentation, regularization, and transfer learning from related domains. Lastly, we will address the ethical scope of fine-tuning LLMs, highlighting potential challenges related to bias, fairness, and unintended consequences. They audience will gain an overall knowledge about LLM also they can know how to apply it on their specific data domains.

Fine-Tuning Large Language Models: Empowering AI for Specialized Applications

Any discussion about storage systems is incomplete without the mention of Throughput, IOPs, and Latency. But what exactly do these terms mean and why are they important?
Collectively, these three terms are often referred to as storage performance metrics. Performance can be defined as the effectiveness of a storage system to address I/O needs of an application or workload. Different application workloads have different I/O patterns, and with that arises different bottlenecks, so there is no “one-size fits all” in storage systems. These storage performance metrics help with storage solution design and selection based on application/workload demands
 In this webinar, we’ll cover:
• What storage performance metrics mean – understanding key terminology nuances
• Why users/storage administrators should care about them
• How these metrics impact application performance 
• Real-world use cases

Everything You Wanted to Know About Throughput, IOPs, and Latency

Keeping Data Secure is one of the prime concerns of any organisation today, given that data is the new oil and attacks on data are on the rise. Therefore addressing security concerns is a key prerogative for any enterprise class block storage. We will discuss various aspects of keeping data secure on an Enterprise Class Block storage controllers and some latest trends in that space.:
• Storage architects get a view on what is involved in building a secure and robust block storage controller
• Developers will get a view on the latest trends in this space
• Solution and ecosystem partners will get a view on what security aspects need to be considered for data security for a block storage controller

Building Robust Data-Centric Security with Advanced Block Storage Systems

Workloads using generative artificial intelligence trained on large language models are frequently throttled by insufficient resources (e.g., memory, storage, compute, or network dataflow bottlenecks). If not identified and addressed, these dataflow bottlenecks can constrain Gen AI application performance well below optimal levels. 

Given the compelling uses across natural language processing (NLP), video analytics, document resource development, image processing, image generation, and text generation, being able to run these workloads efficiently has become critical to many IT and industry segments. The resources that contribute to generative AI performance and efficiency include CPUs, DPUs, GPUs, FPGAs, plus memory and storage controllers.  

This webinar, with a broad cross-section of industry veterans, provides insight into the following:

• Defining the Gen AI dataflow bottlenecks
• Tools and methods for identifying acceleration options
• Matchmaking the right xPU solution to the target Gen AI workload(s)
• Optimizing the network to support acceleration options
• Moving data closer to processing, or processing closer to data
• The role of the software stack in determining Gen AI performance

Accelerating Generative AI – Options for Conquering the Dataflow Bottlenecks

Emerging memories are now found in multiple applications both as stand-alone chips and embedded into systems on chips (SoCs) as they replace established technologies, including SRAM, NOR flash, and DRAM. In this webinar, SNIA CMSI members and leading experts Tom Coughlin (Coughlin Associates/IEEE President)  and Jim Handy (Objective Analysis) will discuss the latest developments in MRAM, ReRAM, FRAM, PCM, and other new memory technologies to explain why, how, and when these technologies will grow, and how their success will impact both the semiconductor and the capital equipment markets.

Emerging Memories Branch Out

Object Storage has firmly established itself as a cornerstone of modern data centers and cloud infrastructure. Ensuring API compatibility has become crucial for object storage developers who want to benefit from the wide ecosystem of existing applications. However, achieving compatibility can be challenging due to the complexity and variety of the APIs, access control mechanisms, and performance and scalability requirements.

In this webinar, we'll highlight real-world incompatibilities found in various object storage implementations. We'll discuss specific examples of existing discrepancies, such as missing or incorrect response headers, unsupported API calls, and unexpected behavior. We’ll also describe the implications these have on actual client applications.
 
This analysis is based on years of experience with implementation, deployment and evaluation of a wide range of object storage systems on the market. Attendees will leave with a deeper understanding of the challenges around compatibility and how to address them in their own applications.
 
During this webinar, we'll call for participation in a Cloud Object Storage Plugfest, facilitated by SNIA and co-located at Storage Developer Conference (SDC) 2024, aimed at improving cross-implementation compatibility for client and/or server implementations of private and public cloud object storage solutions. This endeavor is designed to be an independent, vendor-neutral effort with broad industry support, focused on a variety of solutions, including on-premises and in the cloud.

Navigating the Complexities of Object Storage Compatibility

OK, I'll be honest, the title is a little dramatic, but ask yourself this question, how much data do you have today, and have you ever considered what impact you are having on the environment? The reality is, data centers have been shown to account for approximately 1 – 1.5% of global electricity use. In this session, we will explore what sustainability is, and I will give you a hint, it’s not just environmental. We will look at the impact digitization can have on energy consumption, and therefore the environment, and some practical applications that you can do to help address data challenges to address environmental sustainability.

How is Data Harming Your Health?

The enterprise storage market is rapidly expanding to include NVMe and NVMe-oF products pervasively. This presents the challenge: how do you manage these as part of your enterprise data center?

As the NVM Express family of specifications continue to develop, the corresponding Swordfish management capabilities are also evolving. The SNIA Swordfish management bundle (including the specification, schema, documentation, and more) has expanded to include full NVMe and NVMe-oF technology enablement and alignment across DMTF, NVMe and SNIA for NVMe and NVMe-oF technology use cases.

In conjunction with Redfish®, Swordfish's capabilities to manage NVMe and NVMe-oF devices in the enterprise provide a seamless management ecosystem. 

This presentation will introduce management of NVMe and NVMe-oF technology with SNIA Swordfish. Using an example of the SNIA Swordfish functionality, the presenters will introduce how to manage the complexity of discovery controllers with the simplified model presented to Swordfish clients.

Catch the Wave – Managing NVMe-oF™ in the Enterprise

The days of simple, static, self-contained file systems have long passed. Today, we have complex, dynamic namespaces, mixing block, file, object, key-value, queue, and graph-based resources, accessed via multiple protocols, and distributed across multiple systems and geographic sites. These complexities result in new challenges for simplifying management. 

The good news is, that the SNIA Cloud Data Management Interface (CDMI™), an open ISO standard (ISO/IEC 17826:2022) for managing data objects and containers, already includes extensive capabilities for simplifying the management of complex namespaces. In this webinar, attendees will learn how to simplify namespace management – the open standards way, including namespace discovery, introspection, exports, imports and more, discussing:

• Challenges and limitations with proprietary namespace management
• Overview of namespaces and industry evolution
• Lack of portability between platforms
• Using CDMI for simplified and consistent namespace management
• Use cases for namespace management

Simplified Namespace Management – The Open Standards Way

AI is disrupting so many domains and industries and by doing so, AI models and algorithms are becoming increasingly large and complex. This complexity is driven by the proliferation in size and diversity of localized data everywhere, which creates the need for a unified data fabric and/or federated learning. It could be argued that whoever wins the data race will win the AI race, which is inherently built on two premises: 1) Data is available in a central location for AI to have full access to it, 2) Compute is centralized and abundant. 

Edge AI though, defies these assumptions. If centralized (or in the cloud) AI is a superpower and super expert, edge AI is a community of many smart wizards. As humans, we can appreciate the power of cumulative knowledge over a central superpower. In this webinar, we will touch on: 
• The value and use cases of distributed edge AI 
• How data fabric on the edge differs from the cloud and its impact on AI
• Edge device data privacy trade-offs and distributed agency trends
• Privacy mechanisms for federated learning, inference, and analytics
• How interoperability between cloud and edge AI can happen

Why Distributed Edge Data is the Future of AI

With cloud data privacy regulations evolving worldwide and accelerated adoption of AI technologies such as ChatGPT, Large Language Models (LLMs) and more, companies must ensure data and AI models are compliant. Confidential AI is a new collaborative platform for data and AI teams to work with sensitive data sets and run AI models in a confidential environment. It includes infrastructure, software, and workflow orchestration to create a secure, on-demand work environment that meets organizations' privacy requirements and complies with regulatory mandates. In this session, you’ll learn:

• What confidential AI is and why it is important
• Changing privacy and compliance landscape for AI use and models
• Biggest data privacy threats and opportunities 
• Pragmatic examples and next steps

The Rise of Confidential AI

Since its ratification in late 2018, NVMe/TCP has gained a lot of attention due to its great performance characteristics and relatively low cost. Since then, the NVMe/TCP protocol has been enhanced to add features such as Discovery Automation, Authentication and Secure Channels that make it more suitable for use in enterprise environments. More recently, as customers evaluate their options and consider adopting NVMe/TCP for use in their environment, many find they need a bit more information before deciding how to move forward and are asking questions such as:
• How does NVMe/TCP stack up against my existing block storage protocol of choice in terms of performance?
• Should I use a dedicated storage network when deploying NVMe/TCP or is a converged network ok?
• How can I automate interaction with my IP-Based SAN?

Join us for an open discussion regarding these questions and more.

NVMe/TCP: Performance, Deployment and Automation

Data Fabric is an architecture, set of services and platform that standardizes and integrates data across the enterprise regardless of data location (On-Prem, Cloud, Multi Cloud, Hybrid Cloud), enabling self-service data access to support various applications, analytics, and use cases.  The data fabric leaves data where it lives and applies intelligent automation to govern, secure and bring AI to your data. This session will discuss:
• Different types of data sources for data fabric integration
• Unification of structured and unstructured data sources into the data fabric
• How to simplify data access by virtually connecting data end points across a hybrid cloud landscape
• Providing automatic enrichment leveraging AI to contextualize data with semantics and knowledge
• Apply global and automated data governance and data privacy policy enforcement for increased data protection and quality

Data Fabric: Connecting the Dots between Structured and Unstructured Data

The latest buzz around generative artificial intelligence (AI) ignores the massive costs to run and power the technology. Without any guard rails in place, what are the impacts of AI on sustainability and costs across our technology resources? This webinar will offer insights on the potentially hidden technical and infrastructure costs associated with generative AI, best practices and potential solutions to be considered, discussing:   

• Scalability considerations for generative AI in enterprises 
• Significant computational requirements and cost for Large Language Model inferencing 
• Fabric requirements and costs 
• Sustainability impacts due to increased power consumption, heat dissipation, and cooling implications 
• AI infrastructure savings - On-prem vs. Cloud
• Practical steps to reduce impact, leveraging existing pre-trained models for specific market domains

Addressing the Hidden Costs of AI

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Addressing the Hidden Costs of AI

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