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Artificial Intelligence in practice

CIO

The world has known the term artificial intelligence for decades. Until recently, discussion of this technology was prospective; experts merely developed theories about what AI might be able to do in the future. Today, integrating AI into your workflow isn’t hypothetical, it’s MANDATORY.

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Optical technology enabling the growth of artificial intelligence

CIO

Artificial intelligence (AI) has a pivotal role to play. But in order for AI to expand, we need new networking technology that boosts transmission speeds and improves responsiveness. What are Large Language Models (LLMs)? Learn more about IOWN here. The answer? Innovation

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Exploring the pros and cons of cloud-based large language models

CIO

The paradigm shift towards the cloud has dominated the technology landscape, providing organizations with stronger connectivity, efficiency, and scalability. In light of this, developer teams are beginning to turn to AI-enabled tools like large language models (LLMs) to simplify and automate tasks.

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5 ways to deploy your own large language model

CIO

It’s the fastest-moving new technology in history. A large language model (LLM) is a type of gen AI that focuses on text and code instead of images or audio, although some have begun to integrate different modalities. That question isn’t set to the LLM right away. Instead, it’s processed first.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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3 principles for regulatory-grade large language model application

CIO

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. Moreover, LLMs should strive for transparency in their methodologies, showcasing how they arrived at a given conclusion.

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Run Large Language Models locally with Ollama and Open WebUI

Xebia

Large Language Models (LLMs) like ChatGPT are amazing. But most big companies will always run their LLMs on the cloud. What if you want to run your own LLMs, on your own computer? Ollama is a tool that allows you to run Large Language Models locally. Let’s get started.

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The AI Superhero Approach to Product Management

Speaker: Conrado Morlan

In this engaging and witty talk, industry expert Conrado Morlan will explore how artificial intelligence can transform the daily tasks of product managers into streamlined, efficient processes. The Future of Product Management 🔮 How to continuously integrate AI into your work to stay ahead of emerging trends and technologies.

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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5 Things a Data Scientist Can Do to Stay Current

And more is being asked of data scientists as companies look to implement artificial intelligence (AI) and machine learning technologies into key operations. Fostering collaboration between DevOps and machine learning operations (MLOps) teams. Collecting and accessing data from outside sources.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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A Tale of Two Case Studies: Using LLMs in Production

Speaker: Tony Karrer, Ryan Barker, Grant Wiles, Zach Asman, & Mark Pace

Join our exclusive webinar with top industry visionaries, where we'll explore the latest innovations in Artificial Intelligence and the incredible potential of LLMs. We'll walk through two compelling case studies that showcase how AI is reimagining industries and revolutionizing the way we interact with technology.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.