R Programming Training in Chandigarh

As a workstation

• If you want to get started with R in the cloud quickly and easily, this is your best bet.
• The environment will be familiar to anyone who has worked with R on a local workstation.
• However, instead of using local resources, the R environment runs on a VM in the cloud.
• If your data is already stored in Azure, this has the added benefit of allowing your R scripts to run "closer to the data." Instead of transferring the data across the Internet, the data can be accessed over Azure's internal network, which provides much faster access times.
• The DSVM can be particularly useful to small teams of R developers.
• Instead of investing in powerful workstations for each developer and requiring team members to synchronize on which versions of the various software packages they will use, each developer can spin up an instance of the DSVM whenever needed.

As a compute platform

• In addition to being used as a workstation, the DSVM is also used as an elastically scalable compute platform for R projects. Using the AzureDSVM R package, you can programmatically control the creation and deletion of DSVM instances.
• You can form the instances into a cluster and deploy a distributed analysis to be performed in the cloud. This entire process can be controlled by R code running on your local workstation.

ML Services on HDInsight

• Microsoft ML Services provide data scientists, statisticians, and R programmers with on-demand access to scalable, distributed methods of analytics on HDInsight. This solution provides the latest capabilities for R-based analytics on datasets of virtually any size, loaded to either Azure Blob or Data Lake storage.
• This is an enterprise-grade solution that allows you to scale your R code across a cluster. By using functions in Microsoft's RevoScaleR package, your R scripts on HDInsight can run data processing functions in parallel across many nodes in a cluster. This allows R to crunch data on a much larger scale than is possible with single-threaded R running on a workstation.
• This ability to scale makes ML Services on HDInsight a great option for R developers with massive data sets. It provides a flexible and scalable platform for running your R scripts in the cloud.

Azure Databricks

• Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform.
• Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
• The collaboration in Databricks is enabled by the platform's notebook system. Users can create, share, and edit notebooks with other users of the systems.
• These notebooks allow users to write code that executes against Spark clusters managed in the Databricks environment.
• These notebooks fully support R and give users access to Spark through both the SparkR and sparklyr packages.
• Since Databricks is built on Spark and has a strong focus on collaboration, the platform is often used by teams of data scientists that work together on complex analyses of large data sets. Because the notebooks in Databricks support other languages in addition to R, it is especially useful for teams where analysts use different languages for their primary work.

Azure Machine Learning

• Azure Machine Learning can be used for any kind of machine learning, from classical machine learning to deep learning, supervised and unsupervised learning.
• Whether you prefer to write Python or R code or zero-code/low-code options such as the designer, you can build, train and track highly accurate machine learning and deep-learning models in an Azure Machine Learning Workspace.

Azure Machine Learning Studio (classic)

• Machine Learning Studio (classic) is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions in the cloud. It enables emerging data scientists to create and deploy machine learning models without the need to write much code.
• Azure Machine Learning Studio (classic) supports both R and Python.
• Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try Azure Machine Learning designer (preview), which provides drag-n-drop ML modules plus scalability, version control, and enterprise security.