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Architecture is an ever-living, ever-evolving entity. Architecture is constantly changing. As time passes and technology advances, it is inevitable that the underlying architecture of technology also evolves and mutates. Learn more about ever-evolving architecture below.

Think of a data professional (data scientist/data engineer/business analyst/…), and guess what they do all day. Design big data algorithms? Build state-of-the-art, scalable pipelines? Discover insights that drive business growth? Wrong. Data professionals spend over 40% of their time preparing data before they even start using it for their job. The data preparation process is the most time-consuming task in a data operative’s schedule.

ClearML is now officially integrated into the NVIDIA TAO Toolkit 🎉. For those of you that don’t know yet, the NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training process by abstracting away the AI/deep learning framework complexity.

Data is everywhere–driving the evolution of technology, changing the way we do business, transforming what it means to be a customer. Yet, too many businesses are still operating in a data-aware state and not truly adapting to a data-driven mentality. According to Deloitte Insights, just 1 in 10 executives believe that their employees can actually use data to make decisions.

The five things you need to know about data observability in a data pipeline are: Becoming a data-driven organization is a vital goal for businesses of all sizes and industries—but this is easier said than done. Too many companies fail to attain the fundamental principle of data observability: knowing the existence and status of all the enterprise data at their fingertips.

We’re in the defining decade of data. Data underpins the technologies transforming how we work, communicate, socialize and buy. If you want to take part in the revolution, you need to become—or hire—a data leader. But what does that even mean? What sets data leaders apart from the average data-aware professional? And how can we become data leaders?