7 considerations when building your ML architecture

Organizations face challenges in building reliable ML architecture due to skills gaps. Only 6% invest in AI upskilling, affecting roles beyond data science. Key considerations include extending existing infrastructure, optimizing GPU usage, adopting MLOps platforms, implementing hybrid cloud strategies, prioritizing security, and focusing on observability. Upskilling and partnering with experts can accelerate AI adoption and reduce overhead.