- We expect to see some new elements of technology such as machine learning and deep learning make their way into our thinking about IT architectures.
- Data Protection Overview Data Backup and Protection Storage Data Backup and Protection Software Data Protection Suites Cloud Backup and Protection Copy Data Management
- We expect 2017 to be the year when enterprises realize that not all clouds are equal.
- In 2017, as multi-cloud enterprise IT designs become more commonplace, the need to protect that data in SaaS systems or across multiple on and off-premises clouds will become critical.
- One of the most significant gaps is how to protect data in the cloud era.
As we approached the end of 2016, I reflected on the storage trend predictions we identified – Containers, 2-Tier Storage, Cloud Portfolio Management, New Media technologies and IT Skills focused on Cloud-native development.
Continue reading “The New Media Evolution, Memory-Centric Architectures and Deep Learning: 2017 Predictions from the CTO”
- The virtualization cost savings were increasingly taken for granted and the operational benefits drove the spread of virtualization.
- Cloud computing software is eating the world, and each day is bringing new developments in this world.
- Virtualization was the seed that sparked the cloud revolution, although cloud has now taken its concepts significantly further.
- General Session | Trends, Challenges, and Solutions to Protect the Cloud
- The cloud, just like virtualization before it, first gained popularity because of the most basic business driver there is: cost savings.
As today’s businesses increasingly turn to the cloud to run their operations, seasoned enterprise technology professionals may recognize a familiar pattern. Years ago, virtualization emerged as a transformational technology with a similar pattern of business drivers; initially the promise of significant cost savings justified deployments. Just as virtualization evolved from a tactical cost-saving technology into a corporate strategy enabling enterprises to be responsive to changing business demands, cloud computing is following a similar path.
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- Figure 4: Generating training data in parallel using Microsoft R Server.
- We present the final tagged test image in Figure 8 where cars and boats are labeled with red and green bounding boxes respectively; you can also download the image .
- Each worker node returns a labelled list of moving window tile coordinates, which is then used to label the final test image in MRS running on HDInsight Spark edge node.
- We compress 2.3 million training images from 8.9GB of raw PNG images to 5.1GB with im2rec binary in 10 minutes for optimal training performance.
- MXNet DNN model training using NVIDIA Tesla K80 GPU using Microsoft R Server (MRS).
This post is by Max Kaznady, Data Scientist, Miguel Fierro, Data Scientist, Richin Jain, Solution Architect, T. J. Hazen, Principal Data Scientist Manager, and Tao Wu, Principal Data Scientist Manager, all at Microsoft.
Continue reading “Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake”