Systems | Development | Analytics | API | Testing

The software testing landscape is experiencing an unprecedented transformation as we venture into 2024. This transformation isn't merely an incremental change—it represents a fundamental shift in how organizations approach quality assurance and testing methodologies. At the heart of this revolution lies artificial intelligence (AI), which is reshaping traditional testing paradigms and creating new possibilities for quality assurance professionals and developers alike.

We are entering a new era for marketing and advertising agencies. From evolving consumer expectations and increasingly stringent privacy regulations to the rise of AI, the landscape is shifting rapidly. To remain competitive, agencies need to reimagine how they operate. The winners will be those that adopt forward-thinking data strategies, build trust with partners and clients, and leverage AI to deliver real-time insights and personalized campaigns.

AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that telecommunications enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

The promise of AI-driven development is undeniable – faster code, quicker releases, and unprecedented innovation. But here’s the catch: AI isn’t perfect, and the code it generates could be riddled with hidden flaws. In fact, within three years, over a third of all code will be AI-generated, and much of it may introduce more bugs into production than ever before.

It’s a truism that global supply chains are complex. The process of sourcing raw materials, transforming them into finished products, and distributing them to customers encompasses numerous systems (e.g., ERPs, WMSs, and TMSs). All systems within “the supply chain” are trending in the same direction; they’re aiming to be more efficient, resilient, and agile. Various technological developments have facilitated this directional trend.

By Erez Schnaider, Technical Product Marketing Manager, ClearML In a previous blog post, we demonstrated how easy it is to leverage Arm Neoverse-based Graviton instances on AWS to run training workloads. In this post, we’ll explore how ClearML simplifies the management and deployment of LLM inference using llama.cpp on Arm-based instances and helps deliver up to 4x performance compared to x86 alternatives on AWS. (Want to run llama.cpp directly?

Large language models (LLMs) are at the heart of generative AI transformations, driving solutions across industries — from efficient customer support to simplified data analysis. Enterprises need performant, cost-effective and low-latency inference to scale their gen AI solutions. Yet, the complexity and computational demands of LLM inference present a challenge. Inference costs remain prohibitive for many workloads. That’s where SwiftKV and Snowflake Cortex AI come in.

AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that manufacturing enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

As we head into 2025, Qlik is taking a significant step forward in the evolution of our go-to-market approach by placing an even greater emphasis on our partnerships. This move is aimed at capturing the growing market opportunity in data integration, data quality, analytics and AI.