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DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training
Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn and more. You can also easily add Cloud GPU and Cloud TPU
Oracle Cloud Infrastructure Virtual Machines for Data Science
With the explosion of business data—ranging from customer data to the Internet of Things—data scientists need the flexibility to explore and build models quickly. But purchasing new hardware to meet temporary or peak demand can involve significant capital expense as well as a considerable amount of time.
Oracle Cloud Infrastructure Virtual Machines (VMs) for Data Science are preconfigured environments that enable you to build models and deliver business value faster. Built on Oracle Cloud Infrastructure, these VMs offer exceptional performance, security, and control. You can expand your compute resources as needed using compute autoscaling and keep costs under control by stopping compute instances when they are not needed.
Compute options suitable for this VM image include a virtual machine with an NVIDIA GPU that can be up and running in under 15 minutes with preinstalled common IDEs, notebooks, and frameworks. Oracle Cloud Infrastructure VMs for Data Science include basic sample data and code for you to test and explore.
In response to the surge in popularity of AI and machine learning, Cloud Service Providers (CSPs) have begun providing virtual machines (VMs) specialized for these applications. However, the default offerings usually contain unoptimized machine learning frameworks that do not leverage the Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instruction set for faster vector operations on Intel® Xeon® Scalable processors. To address this, Intel has collaborated with Microsoft to build the Intel Optimized Data Science Virtual Machine (DSVM), an extension of the Ubuntu* version of Azure* DSVMs with CPU-optimized conda environments for TensorFlow* and MXNet*. These optimized environments require no modification to existing TensorFlow or MXNet code, and provide an average of 7.7X speedup over unoptimized environments.
Virtualization has changed so much in IT, and by doing so, it has increased the number of options made available to the backup industry. As companies continue to move from legacy IT towards a modern, software-defined datacenter environment with server virtualization, companies bear the responsibility to modernize their data protection strategies to include more modern backup and recovery approaches. We can assume that machine learning, artificial intelligence and predictive analytics will take over and make backup administrators faster and more productive at their jobs. Systems will become smart enough to know which versions of files and application recovery points to roll back after an attack. AI will leverage predictive learning algorithms and automatically perform proactive recoveries, eliminating outages even before an end user can detect them.
Also, it would be interesting to see how backup data, with an infusion of AI, will not just reside in one place but could also be used for organizational benefits.