According to Gartner, by 2025, over 95% of new digital workloads will be deployed on cloud-native platforms, up from 30% in 2021. This change highlights the growing importance of cloud-native environments in meeting the increasing reliance on these platforms to drive scalability, flexibility, and resilience in their AI-driven applications.
Inadequate analysis of large amounts of data can cause serious problems for users and organizations, often leading to frustration and inefficiency. For example, a data scientist searching for the “best machine learning library in Python” might encounter irrelevant results focusing on basic Python classes rather than advanced libraries like TensorFlow or Scikit-learn.
According to Forbes, 2025 will be the year AI transforms into fraud. AI fraudsters are becoming more sophisticated and widespread, targeting vulnerable people worldwide.
This blog explores how to secure and optimize AI development in the cloud, ensuring that your AI initiatives remain both robust and resilient in a rapidly changing environment.
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