You plan to perform analytics of the medical records of patients located around the world. You need to recommend a solution that avoids storing and processing data in the cloud. What should you include in the recommendation?
A.
Azure Machine Learning Studio
B.
the Text Analytics API that has container support
Suggested Answer:D🗳️
The Microsoft Machine Learning Library for Apache Spark (MMLSpark) assists in provisioning scalable machine learning models for large datasets, especially for building deep learning problems. MMLSpark works with SparkML pipelines, including Microsoft CNTK and the OpenCV library, which provide end-to-end support for the ingress and processing of image input data, categorization of images, and text analytics using pre-trained deep learning algorithms. References: https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789131956/10/ch10lvl1sec61/an-overview-of-the-microsoft-machine-learning- library-for-apache-spark-mmlspark
Spark cluster will be deployed using HDInsight or used with Databricks and clusters cant be deployed locally therefore all processing and storing will be on cloud. D can't be right answer.
Correct. Anytime you get a question that suggests wording like "should not be uploaded to the cloud" usually suggests deploying a container service. Since text analytics can help perform analytics on medical documents, B is the right answer.
D is correct.
B is not correct as the text analytics API can't make analytics for medical records it's used in sentiment analysis, key phrase extraction, and language detection.
https://github.com/Azure/mmlspark
The question gives you two key pieces of information regarding the solution:
1 - Analytics on medical records (vague statement) scattered all over the world;
2 - The data must not be stored nor processed in the cloud;
The only relevant example in the link you referenced is example 8, which deals with....**drum roll** key phrase extraction, which you also state that Text Analytics API performs. Therefore, Text Analytics API seems like the right choice for me, as it addresses all the requirements of the question.
MMLSpark can be deployed locally via Docker container and it uses Microsoft Cognitive Services to "tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation". If you want to make analytics on data you need an environment (while Text Analytics may represent only a piece of the solution), and Spark would be a good choice. So, in my opinion D is correct. Just check out here:
https://mmlspark.blob.core.windows.net/website/index.html
the most suitable recommendation for avoiding storing and processing data in the cloud is to use the Text Analytics API with container support (Option B).
"Medical Records" most likely contain images. Text Analytics is "text" analytics.
Azure Machine Learning works on an Edge Node... more seamlessly too, and is native to Azure. But I think bulk of the data would still need to get stored on the Cloud. Eliminating this option.
MML Spark does run on Edge. But can it store all the date locally?
I think AML is a better option here because it can work off of local data.
https://docs.microsoft.com/en-us/azure/architecture/hybrid/deploy-ai-ml-azure-stack-edge
That said, I still don't have a strong opinion about the answer unfortunately.
Though Text Analytics for health is in preview, but the use case on this link is an answer for this question:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health
You never use something in Preview in production environment.
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