A Complete Practical Guide of Deep Learning Platform
Welcome
Deep learning starts with the study of human brain neurons, which involves multiple disciplines, such as probability theory and statistics. The purpose is to enable the machine to “learn” autonomously, to assist people with detection and decisions, and to facilitate people's lives. Deep learning is widely used in domains like data mining, computer vision and voice recognition. In recent years, with the significant improvement of GPU computing power, deep learning has come back to the frontiers of AI technology.
Each technology, from academic research to practical use in production, will certainly undergo great changes and improvements. Nowadays, deep learning technology evidently plays an integral role in the process of intelligent transformation of industries. Therefore, decision makers need consider the issue on how to choose a highly integrated deep learning platform with easy use.
This article will introduce how to choose deep learning platforms that best suit needs in different business scenarios, and demonstrate the great value that deep learning technology can bring about in further applications into all industries.
Regards,
Shiwei Xu CEO of Qiniu Cloud
Qiniu Content
1. Criteria of Selecting a Deep Learning Platform
In order to better satisfy needs in business scenarios and increasingly improve research and development capabilities, corporations with strong technology have built their own deep learning platforms. Leading platforms, in terms of overall capacity, include Google Cloud Platform, AWS Machine Learning, and Microsoft Azure Machine Learning.
2. Challenges and Solutions of Building a Deep Learning Platform
3. Features and Advantages of the AVA Deep Learning Platform
4. Industry Value from Applications Based on the AVA Deep Learning Platform
Market Guide for Machine Learning Compute Infrastructures
24 September 2018
Devising compute strategies for AI applications can be challenging as it involves navigating complex design considerations. I&O leaders can use this research to deliver highly efficient infrastructures for compute-intensive machine learning and deep neural network-based applications.
Key Findings
- The vendor landscape for ML compute infrastructure is fragmented and rapidly changing, making it tough for enterprises to navigate the market and filter the vendor marketing obfuscations.
- The number of accelerators per node varies greatly across vendors, creating challenges for end users to architect ML compute infrastructures that can efficiently scale.
- Integrating diverse system software components including libraries, drivers, and diverse ML and DNN frameworks can be complex and time-consuming, and require additional skills. [...]