Deploying Deep Learning Models on Kubernetes with GPUs

This post is authored by Mathew Salvaris and Fidan Boylu Uz, Senior Data Scientists at Microsoft. One of the major challenges that data scientists often face is closing the gap between training a deep learning model and deploying it at production scale. Training of these models is a resource intensive task that requires a lot… Read more

Demystifying Docker for Data Scientists – A Docker Tutorial for Your Deep Learning Projects

This post is authored by Shaheen Gauher, Data Scientist at Microsoft. Data scientists who have been hearing a lot about Docker must be wondering whether it is, in fact, the best thing ever since sliced bread. If you too are wondering what the fuss is all about, or how to leverage Docker in your data… Read more

How to Train & Serve Deep Learning Models at Scale, Using Cognitive Toolkit with Kubernetes on Azure

This post is authored by Wee Hyong Tok, Principal Data Science Manager at Microsoft. Deep Learning has fueled the emergence of many practical applications and experiences. It has played a central role in making many recent breakthroughs possible, ranging from speech recognition that’s reached human parity in word recognition during conversations, to neural networks that… Read more

Deployment of Pre-Trained Models on Azure Container Services

This post is authored by Mathew Salvaris, Ilia Karmanov and Jaya Mathew. Data scientists and engineers routinely encounter issues when moving their final functional software and code from their development environment (laptop, desktop) to a test environment, or from a staging environment to production. These difficulties primarily stem from differences between the underlying software environments… Read more