Large-scale transformer-based deep learning models trained on large amounts of data have shown great results in recent years in several cognitive tasks and are behind new products and features that augment human capabilities.
The growing adoption of data-driven and machine learning-based solutions is driving the need for businesses to handle growing workloads, exposing them to extra levels of complexities and vulnerabilities.
The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your MLOps model.
Robustness is the ability of a closed-loop system to tolerate perturbations or anomalies while system parameters are varied over a wide range.
Welcome to Azure Hybrid, Multicloud, and Edge Day—please join us for the digital event.
Testing is an important exercise in the life cycle of developing a machine learning system to assure high-quality operations.
Microsoft is committed to the responsible advancement of AI to enable every person and organization to achieve more.
Over the past few years, there has been an increasingly steady drumbeat for the need to diversify and open the telecommunications supply chain.
As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society.
AI is transforming business and the world. However, AI models learn from the data. Biases that exist in society will exist in the models. Human judgment must be the overriding factor, ensuring that AI models benefit and are inclusive of everyone.
A growing number of organizations are taking advantage of machine learning to increase efficiency, enhance customer experiences and drive innovation. Azure Machine Learning is the enterprise-grade service to build and deploy models faster and accelerate the machine learning lifecycle.
Today, AI and machine learning are enabling data-driven organizations to accelerate their journey to insights and decisions.
