Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

Hiring helps, but hiring without a system to scale expertise is how organizations accidentally scale inconsistency.

We are in the midst of an AI talent race. In 2023, just 33% of organizations used generative AI. In July 2024, it was 71%, according to McKinsey researchers. Large enterprises—those over $500 million ...

Countries that moved early now see the full cost of those choices. What began as a digital bet has steadily changed grids, ...

Enables AI infrastructure providers to emulate and optimize all aspects of the data center, from the physical layer through the application layer Validates and optimizes system-level performance, ...

Alberta’s energy industry has always been an economic engine. Now, it’s taking on a new challenge: powering the AI revolution. Alberta’s energy industry has always been an economic engine. Now, it’s ...

The future is not about asking if AI will get better, but when and at what cost. The leaders who internalize these curves will use AI to redefine productivity, innovation, and national competitiveness ...

What if your workflows could process tens of thousands of files in parallel, never missing a beat? For many, scaling n8n workflows to handle such massive workloads ...

Analysis Whether or not OpenAI's new open weights models are any good is still up for debate, but their use of a relatively new data type called MXFP4 is arguably more important, especially if it ...