Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

An increasing number of companies are using data analytics to improve their products,
services, and business processes. However, learning knowledge effectively from massive …

When training a deep learning (DL) model, input data are pre-processed on CPUs and
transformed into tensors, which are then fed into GPUs for gradient computations of model …

The increasing demand for learning from massive datasets is restructuring our economy.
Effective learning, however, involves nontrivial computing resources. Most businesses utilize …

Training deep learning (DL) models on petascale datasets is essential for achieving
competitive and state-of-the-art performance in applications such as speech, video analytics …

In recent years, the integration of artificial intelligence (AI) and cloud computing has
emerged as a promising avenue for addressing the growing computational demands of AI …

With the widespread adoption of Internet of Thing (IoT) and the exponential growth in the
volumes of generated data, cloud providers tend to receive massive waves of demands on …

Lowering costs by driving high utilization across deep learning workloads is a crucial lever
for cloud providers. We present Singularity, Microsoft's globally distributed scheduling …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …

Cloud computing is undeniably becoming the main computing and storage platform for
today's major workloads. From Internet of things and Industry 4.0 workloads to big data …