Deep learning is typically a long and costly endeavour, especially when it comes to training models. There are many factors that impact the process, but processing power, in particular, can make or break your pipeline. Today, many developers leverage graphics processing units (GPUs). Learn how you can scale up deep learning in the cloud.
In this blog post we’ll explore where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.
The emergence of cloud native development and containers has redefined how software is developed. But not all organizations have the resources or expertise to set up the required infrastructure to support a containerized application. Luckily, cloud vendors offer Containers-as-a-Service to help developers to capitalize on the benefits of cloud native development.
How are desktop and cloud development evolving? We’ve prepared an infographic with some key insights that can help you better understand the cloud and desktop developer landscape, based on our recent report focusing on the topic.