The SageMaker support is helpful for users who want to develop machine learning models without the overhead of infrastructure management. Self-managed Kubernetes clusters using EC2 instances.ĭeep Learning Containers support TensorFlow version 1.14.0, MXNet version 1.4.1 and the NVIDIA CUDA toolkit version 10.0.NGC has a registry of ready-to-use containers that include all the elements required to develop deep learning applications for NVIDIA environments.ĪWS took a page from NVIDIA's strategy and added Deep Learning Containers, a set of Docker images for the most popular deep learning development frameworks. NVIDIA, whose GPU hardware accelerates deep learning workloads, was among the first companies to address this problem with NVIDIA GPU Cloud (NGC). Data scientists had to navigate several source code repositories and dealt with many dependencies and configuration nuances because it was a DIY effort. Historically, creating a programming and testing environment for deep learning models has been complicated and time-consuming. In this breakdown, we'll highlight the recent updates to the AWS machine learning platform that users need to know. In contrast, the managed services, which join similar products for image recognition, speech transcription and interactive chatbots, are designed for data scientists and non-specialists who want to analyze large and complicated data sets using techniques that are more advanced than standard statistical analysis. The Deep Learning AMIs and Containers target developers who build sophisticated custom models with the AWS machine learning platform and DevOps teams charged with deploying them on cloud infrastructure.
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