Availability Groups in Microsoft Azure

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.

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Create Prediction Model using Microsoft Azure Machine Learning Studio

Machine Learning ◽

10.1142/9789811228155_0006 ◽

2021 ◽

pp. 73-106

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Microsoft Azure

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Moving your containers to the Cloud with Microsoft Azure

Windows Containers for IT Pros ◽

10.1007/978-1-4842-6686-1_6 ◽

2021 ◽

pp. 167-210

Author(s):

Vinicius Ramos Apolinario

Keyword(s):

Microsoft Azure

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Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2020040102 ◽

2020 ◽

Vol 10 (2) ◽

pp. 21-39

Author(s):

Archana Yashodip Chaudhari ◽

Preeti Mulay

Keyword(s):

Smart Grids ◽

Clustering Algorithm ◽

Incremental Clustering ◽

Clustering Method ◽

Data Types ◽

Dynamic Data ◽

Real Dataset ◽

Processing Power ◽

Microsoft Azure ◽

Socioeconomic Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

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