Organizations can struggle to maximize value from Machine Learning (ML), and Artificial Intelligence (AI), but often experience inefficient data preparation and a lack of flexibility. Only 54% of AI/ML projects make it to production. Despite these numbers, AI/ML has the potential to deliver massive value when deployed correctly across your organization.

For decades, organizations have sought to improve leadership and decision-making through data-driven capabilities that synthesized information and delivered insights. Now, generative AI has unlocked new opportunities for organizations to increase productivity, innovate faster, and outpace the competition.

To better understand how organizations can lead in the era of generative AI, Teradata and IDC surveyed 900 organizations across the globe, focusing on CDOs, CIOs, and CFOs. We found that there are leaders and laggards when it came to AI-driven decision-making—and that had a massive impact on market share, profitability, customer satisfaction, and employee satisfaction.
 
In this special event, join Teradata and guest speakers from IDC to discover:
• What separates AI leaders and laggards and how it impacts business outcomes
• How organizational culture and structure can accelerate AI value
• The technology that unleashes innovation for AI leaders

Speakers include:
• Dan Vesset – Group Vice President, Analytics and Information Management at IDC
• Chandana Gopal – Research Director, Future of Intelligence at IDC
• Chris Twogood – Senior Vice President of Product Marketing at Teradata
• Vedat Akgun, Ph.D. – Vice President of Data Science & AI at Teradata

Leadership and Decision-Making in the Era of Generative AI

Knowledge is power. If you want to get more from modern data, Pure’s Tech Talk series can supercharge your storage savvy. Led by Pure solution experts and industry guests, these lively discussions explore features, live demos, and best practices to address the use cases you care most about. You’ll come away informed, inspired—and ready to unleash the full power of modern data for your business. Uncomplicated Data Storage, Forever.

While commercial AI offerings have existed for over 50 years, 2023 is shaping up to be a breakout year as enterprises and businesses of all sizes invest in AI to advance their goals. AI advances including creative and generative AI, AI-human collaboration, low or no-code AI are just the tip of the iceberg.

Join Dr. Kirk Borne, an acclaimed data scientist and AI expert, and a panel of Pure Storage AI experts in this exciting discussion on what’s HOT in AI today, opportunities with AI in the future, and lessons learned from our expert panel on small to massive AI initiatives that they’ve each had hands-on experience with.

Additional topics include:
* Optimizing outcomes with infra/storage architected for AI
* Aligning goals between data/AI/analytics teams with IT/Infra teams for optimal AI (AI Centers of Excellence)
* Meeting ESG goals associated with AI projects
* Top Impacts to AI with the right storage platform
* Consolidation of app data and the benefits of that

Future of AI & Lessons Learned from a Former Space Scientist, now Data Scientist

Enterprise Strategy Group Distinguished Analyst Jon Oltsik Presents: 

From skills shortages to nuanced cyber-attacks, cybersecurity leaders have well-documented challenges that threaten the successful protection of their business. Generative artificial intelligence (GenAI) has burst onto the scene promising a long list of potential applications, with cybersecurity improvement touted among them. This session will explore cybersecurity use cases, including those employed by Security Operations Analyst for threat detection and response purposes.

Cybersecurity GenAI Use Cases

In the era of digital transformation, the ability to access and harness the vast sea of information within an enterprise is crucial. Join us for a concise yet impactful 20-minute webinar where we'll explore a game-changing solution: bringing a large language model behind your firewall to revolutionize enterprise content search. 

Key Highlights: 

The Age of Large Language Models: Discover why large language models have become a cornerstone of modern AI and how they can transform content search within your organization. 

The Security Advantage: Explore the benefits of hosting a language model within your own infrastructure, ensuring the utmost data security and compliance. 

Tailored to Your Needs: Learn how to customize and fine-tune the language model to cater specifically to your enterprise's unique content search requirements. 

Enhancing Efficiency: Uncover how leveraging a large language model can significantly boost your content search efficiency, allowing you to extract insights and make data-driven decisions faster than ever. 

Join us for this enlightening session and equip yourself with the knowledge and strategies to bring a large language model behind your firewall, empowering your organization to navigate the digital landscape with confidence and precision.

Maximizing Enterprise Content Search with Generative AI

Join us for an insightful fireside chat as we peer into the future of artificial intelligence (AI) in 2024. In this dynamic session, industry experts will share their predictions and unveil the vast opportunities ahead in the rapidly evolving landscape of AI. Our speakers will delve into key areas shaping the AI ecosystem, exploring recent advancements in machine learning, large language models (LLMs), and beyond. Studying transformative breakthroughs to emerging trends, we will analyze the forces propelling AI innovation and their potential impact on industries ranging from healthcare and finance to manufacturing and entertainment.

In this webinar, you will learn:

What could be next, after this year’s explosive growth of large language models (LLMs)
Strategic considerations for organizations and data professionals navigating the AI landscape in 2024
How open-source AI and closed-source AI will compete in the coming year and the potential outcomes of the race to build marketable AI systems

AI in 2024: Unveiling Predictions and Seizing Opportunities

Data is the new oil, but raw data is not valuable. It needs to be cleaned and augmented to be useful. 
While everyone wants to get the most out of their data with AI, it can be slow and painful to prepare the data, train your models, and create checkpoints at scale. 

This requires new technologies and collaboration between teams to go from raw data to business outcomes. 

Join Markus Grau, JB Thomas, and Michael Kaspers to hear about their first-hand experience tackling these challenges.
You might be surprised where storage can enable your AI journey.

Boost Your AI Initiatives from Ingest to Inference in a Flash

Enterprise Strategy Group Principal Analyst Mike Leone Presents: 

Application developers are challenged with efficiently creating innovative solutions while managing time constraints, which can be mitigated by the transformative impacts of generative artificial intelligence (AI) streamlining code generation and accelerating development processes. Organizations have integrated generative AI (GenAI) into their operational setup to accelerate code creation, refine code structures, elevate code quality, and deliver personalized customer experiences. By harnessing GenAI, application developers tackle issues by capitalizing on the technology’s ability to automate tasks, drive creativity, and deliver innovative solutions.

The Role of GenAI in Application Development

Enterprise Strategy Group Senior Analyst Jonathan Brown Presents: 

A common criticism of AI is its high potential to increase carbon footprints due to its heavy compute requirements. As a result, sustainability has risen to become a critical consideration for nearly all organizations. This session will explore how organizes balance deploying AI-ready infrastructure with well-defined sustainability requirements and the metrics designed to monitor energy-intensive AI environments.

Deploying Sustainable AI Infrastructure

With so many people – like your CIO – hyped about artificial intelligence, machine learning, and generative AI products like ChatGPT, you’re likely under a lot of pressure to develop applications with those technologies for your company.

Like, yesterday.

But how do you turn terabytes of unstructured data – ranging from text to images, audio, and video – into useful information for the AI application?

The answer is a vector database. Vectorizing converts unstructured data into numerical representations known as vector embeddings, and Redis stores, indexes, and retrieves them. Vectors make searching for similar images or text or documents – vector similarity search (VSS) – faster and easier. Moreover, an external vector database with domain-specific, proprietary, or more recent data – used with public general purpose large language models – improves AI application responses.

Not to mention, keeping it from making things up.  
In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. We show the elements of building generative AI applications – using Redis technology you already know.

Watch to find out more about it.
* What’s a vector database
* Examples
* Why LLMs need real-time response
* Redis Enterprise VSS features and differentiators

Driving AI Innovation: Making It Easy to Build Generative AI Apps with Redis

Enterprise Strategy Group Principal Analyst Mike Leone Presents:

The larger and more accurate the data set, the more effective the AI model. However, the road leading to that seemingly simple goal is strewn with roadblocks that pose substantial challenges for many organizations. Data experts and data quality tools and techniques to ensure the quality and accuracy of collected data while also assuring data is compliant with industry regulations is critical. This session will explore these considerations as well as how moving huge data sets to AI is also subject to service level agreements.

AI Data Quality, Compliance, and Infrastructure Requirements

Enterprise Strategy Group: GenAI Summit​

Generative AI has the potential to revolutionize businesses by adding over $4 trillion to the global economy. This figure could even double if the impact of embedding generative AI into software is considered. To maximize this opportunity, businesses need to move from proofs of concept (POCs) to production quickly and cost-effectively. This requires integrating generative AI into decision-making processes and establishing a trusted AI ecosystem. By doing so, businesses can gain accurate and actionable insights that deliver value both now and in the future.

Join Vedat Akgun, Ph.D., VP of Data Science & AI at Teradata and Wayne Eckerson, President, Eckerson Group as they discuss how organizations can deliver trusted AI innovation at scale.

Three Steps to Creating Enterprise Value with Generative AI

Generative AI

Trusted AI

Data Science

AI Solutions

AIOps

Data Governance

Cloud Analytics

Ethical AI

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Data and Analytics

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Three Steps to Creating Enterprise Value with Generative AI

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Teradata