What are three compute contexts that you can use for Machine Learning Server?

You deploy an infrastructure for a big data workload.

You need to run Azure HDInsight and Microsoft Machine Learning Server. You plan to set the RevoScaleR compute contexts to run rx function calls in parallel.

What are three compute contexts that you can use for Machine Learning Server? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.
A . SQL
B . Spark
C . local parallel
D . HBase
E . local sequential

View Answer

Answer: ABC

Explanation:

Remote computing is available for specific data sources on selected platforms. The following tables document the supported combinations.

– RxInSqlServer, sqlserver: Remote compute context. Target server is a single database node (SQL Server 2016 R Services or SQL Server 2017 Machine Learning Services). Computation is parallel, but not distributed.

– RxSpark, spark: Remote compute context. Target is a Spark cluster on Hadoop.

– RxLocalParallel, localpar: Compute context is often used to enable controlled, distributed computations relying on instructions you provide rather than a built-in scheduler on Hadoop. You can use compute context for manual distributed computing.

References:

https://docs.microsoft.com/en-us/machine-learning-server/r/concept-what-is-compute-context