According to Statista, the microservices architecture market will grow at a robust CAGR of 19.6%, reaching $10.86 billion by 2027, up from $4.44 billion in 2022. The shift to microservices architecture has revolutionized software design and development, significantly enhancing agility, scalability, resilience, delivery times, cost-effectiveness, and more. Since Dr.

If you’re one of the cool kids building AI-based products you’ve probably heard of — or are already doing — RAG. If you’re not, let me tell you what RAG is before telling you one weird fact about it. “Retrieval-Augmented Generation” is nothing more than a fancy way of saying “including helpful information in your LLM prompt.” Still, there are many ways to do it and many questions to answer when building a RAG pipeline.

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.

As automation capabilities grow, so do the related use cases. It’s not surprising that 80% of executives think automation can be applied to any business decision, according to a Gartner survey. But as implementation becomes easier and automation adoption more prevalent, your organization can quickly lose control, with too many automation projects happening in silos.