Systems | Development | Analytics | API | Testing

We hosted our first-ever Confluent AI Day on October 23 in San Francisco and virtually. It was sponsored by Confluent, AWS, and MongoDB, with a vibrant gathering of talent and innovation. With 200 attendees, the full-day event brought together AI developers, technology leaders, and startup innovators to explore how data streaming powers generative AI (GenAI) applications.

Thanks to AI, a few people might be starting the new year with bright, shiny smiles. The technology has exploded in popularity and augmented almost everything, including a toothbrush and app combo that uses AI to optimize your dental hygiene habits. Teeth brushers aren’t the only ones grinning due to the AI explosion. Enterprise leaders have been cheerful about the advantages AI can bring to their company’s testing efforts and the software development lifecycle (SDLC).

This expert panel discussed how businesses can prepare for challenges and opportunities with AI, powering smarter models and personalized experiences.

AI has come as a game-changer in plenty of industries, with OpenAI’s GPT series setting the pace in natural language processing and decision-making abilities. The latest edition, GPT-4, promises even more powerful features, like enhanced accuracy, enlarged context window, and improved performance for grape tasks. However, before the adoption of this avant-garde technology, it is necessary to understand how much GPT-4 costs.

Since 5G networks began rolling out commercially in 2019, telecom carriers have faced a wide range of new challenges: managing high-velocity workloads, reducing infrastructure costs, and adopting AI and automation. Because data management is a key variable for overcoming these challenges, carriers are turning to hybrid cloud solutions, which provide the flexibility and scalability needed to adapt to the evolving landscape 5G enables.

In 2024, organizations realized the revolutionizing business potential of gen AI. They accelerated their gen AI operationalization processes: explored new use cases to implement, researched LLMs and AI pipelines and contemplated underlying ethical issues. And with the seeds of the AI revolution now planted, the market is maturing accordingly.

Keboola, in cooperation with Capgemini and Snowflake, is releasing “AI Data Management For Dummies," a book written to be an easy introduction to the world of discriminative and generative AI.

In the last year, we’ve seen the explosion of AI in the enterprise, leaving organizations to consider the infrastructure and processes for AI to successfully—and securely—deploy across an organization. As we head into 2025, it’s clear that next year will be just as exciting as past years. Here, Cloudera experts share their insights on what to expect in data and AI for the enterprise in 2025.

LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.