A geometry-aware approach to optimization for neural architecture search.
See an enterprise deep learning platform in action that comprises Pachyderm for data management, Determined for model development and training, and Seldon Core for deployment.
How you structure your machine learning codebase has a big impact on how easy it is to scale, including adding support for distributed training, hyperparameter tuning, and experiment tracking.
How to build a deep learning platform with open source components to handle tasks such as training data management and versioning, scalable model training, and deployment.
There is an incredibly common story playing out in countless enterprises in 2020.
With Determined you can cloud burst your deep learning training workloads on GCP’s cost-effective preemptible GPUs, in a way that is friendly to infrastructure teams and model developers alike.
Lack of software infrastructure is a fundamental bottleneck in achieving AI’s immense potential – a fact not lost on tech giants like Google, Facebook, and Microsoft. These elite firms have invested massive resources and expertise to build proprietary, AI-native internal infrastructure, and are already reaping the benefits in the form of transformative AI-powered applications and productive Deep Learning Engineers. For everyone else who doesn’t have access to this infrastructure, building practical applications powered by AI remains prohibitively expensive, time-consuming, and difficult.
Determined AI and AWS SageMaker are both platforms that accelerate these Machine Learning Engineering workflows, but with key differences that we compare, in detail.
A Response To Andreessen Horowitz’s “The New Business Of AI”
In this post, we explore the reasons behind it and suggest paths towards scalable training that have the potential to reliably work out of the box.