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.

Observability refers to understanding a system’s internal state by examining its external outputs, like logs, metrics, and traces. Borrowed from control theory, it’s the ability to infer what’s happening inside a system without direct access. To visualize observability, consider the human body. We don’t need to open it up to understand its internal state. By measuring external signals like body temperature, heart rate, or blood pressure, we can infer what’s happening inside.

The demand for food delivery services is skyrocketing, driven by our busy lifestyles and the need for convenience and variety. Apps like Uber Eats, Zomato, and DoorDash have changed how we order food, giving us access to countless restaurant options and smooth delivery experiences. For entrepreneurs, developing an app similar to DoorDash is a great opportunity to tap into this booming market.

From humble beginnings, Apache Kafka steadily rose to prominence and now sits as the backbone of data streaming for thousands of organizations worldwide. From its robust API, cloud-native implementations like Confluent Cloud, and synergy with other technologies like Apache Flink, Kafka has grown to cover many use cases across a broad range of industries.

In the dynamic world of mobile app development, the speed and simplicity with which you can share builds with testers and stakeholders can be the difference between a successful, on-time, bug-free release – or watching that opportunity fly by. To help your team maximize all of these opportunities, there’s a new feature within Release Management: Build distribution for testing.

In this article, I will introduce the steps to release a Flutter app to the App Store using the Codemagic CI/CD. If you have been uploading your app to App Store Connect manually using Xcode and want to simplify that process, I hope you find this article helpful.

Since the use of mobile technology is set to advance further, testing these real devices is crucial and requires effective and efficient methods. The true concept of mobile device testing guarantees that applications perform optimally on various devices. According to Statista data, the current global number of smartphone users is 4.2 billion and is expected to rise by 1.7 billion between 2024 and 2029.