The DSVM is currently missing a proper environment to develop enterprise applications for Machine Learning and Deep Learning with Java. Despite of the fact that all the Python frameworks are the best tools for modeling and research, to get ready for production the backend in most case is more suitable to be C++, C# or Java, to address performances, multi-threading and backward interoperability with enterprise systems (distributed or not).
So I would suggest to add Java frameworks for Machine and Deep Learning like

• DeepLearning4J (see benchmarks here: https://github.com/deeplearning4j/dl4j-benchmark/blob/master/README.md)


• Weka: this is a powerful research tool, well known and used in the Research world, that provides powerful tools for clustering and predictive modeling.


• ELKI: another well known application for data clustering that covers the totality of known classic unsupervises clustering techniques most of them used today for data pre-processing and visualization;


• other libraries are Apache Spark Mlib and H2O;

Java BLAS libraries like
- ND4J

I will suggest also to provide GPU support for ND4J. A possibile approach would be to use isolated docker containers with nvidia-docker, that works pretty much fast and can handle multiple and distributed gpus as well.

Currently, on the Windows DSVM the image provides SQL Server Management Studio (SSMS) which is the classic SQL DB management and development tool.

Microsoft released a new experience way back in July 2018 called Azure Data Studio (ADS) which provides a variety of new features and some overlapping features with SSMS and runs on Linux and Windows.

https://docs.microsoft.com/en-us/sql/azure-data-studio/download-azure-data-studio?view=sql-server-ver15

The new tool is complementary to SSMS and is seeing significant investment related to the Azure Synapse experience. You’ll see many of the features of ADS align with data science activities including support for Notebooks, HDFS integration, flat file import, CSV/JSON export, extensions, source code control and Azure Resource Explorer. You can review the full Feature Comparison of SSMS and ADS here:

https://docs.microsoft.com/en-us/sql/azure-data-studio/what-is?view=sql-server-ver15#feature-comparison-with-sql-server-management-studio-ssms

Please include this tool in both the Linux and Windows VM images

The DSVM is currently missing a proper environment to develop enterprise applications for Machine Learning and Deep Learning with Java. Despite of the fact that all the Python frameworks are the best tools for modeling and research, to get ready for production the backend in most case is more suitable to be C++, C# or Java, to address performances, multi-threading and backward interoperability with enterprise systems (distributed or not).
So I would suggest to add Java frameworks for Machine and Deep Learning like

• DeepLearning4J (see benchmarks here: https://github.com/deeplearning4j/dl4j-benchmark/blob/master/README.md)


• Weka: this is a powerful research tool, well known and used in the Research world, that provides powerful tools for clustering and predictive modeling.


• ELKI: another well known application for data clustering that covers the totality of known classic unsupervises clustering techniques most of them used today for data pre-processing and visualization;


• other libraries are Apache Spark Mlib and H2O;

Java BLAS libraries like
- ND4J

I will suggest also to provide GPU support for ND4J. A possibile approach would be to use isolated docker containers with nvidia-docker, that works pretty much fast and can handle multiple and distributed gpus as well.