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Cloud vendors jostling for share of IoT analytics

#artificialintelligence

ABI Research says cloud vendors are investing in the data and analytics services space as they attempt to get on board the IoT value chain. The researcher forecasts that cloud suppliers will grow their share of IoT data and analytics management revenues from US$6 billion in 2019 to US$56 billion in 2026. Cloud vendor's revenues come primarily from streaming, storage, and the orchestration of data. Analytics services across cloud vendors, on the other hand, are less differentiated, as reflected in pre-built templates such as AWS Sagemaker and Microsoft Azure Notebooks which leverage the open source Jupyter project. Considering that many cloud vendors are in the early stages of analytics investment, cloud vendors are relying on their partners for addressing more specific advanced analytics and vertical market needs.


Cloud vendors seek partnerships to leverage IoT analytics

#artificialintelligence

The new study found that as cloud suppliers continue to expand into the IoT value chain, their investments in data and analytics services are accelerating. The analyst house forecasts that cloud suppliers will grow their share of IoT data and analytics management revenues from $6 billion in 2019 to $56 billion in 2026. ABI said cloud vendors' services now are focused on data management, complemented by a generic analytics toolset, with their revenues coming primarily from the streaming, storage and orchestration of data. In contrast, analytics services across cloud vendors are less differentiated, as reflected in pre-built templates such as AWS Sagemaker and Microsoft Azure Notebooks, both of which use the open-source Jupyter project. As many cloud vendors are in the early stages of investment in analytics, cloud vendors are relying on partners to address more specific, advanced analytics and vertical markets' needs.


What IoT Edge analysis all about

@machinelearnbot

Internet of Things (IoT) analytics refers to the collection and analysis of data stemming from a large number of heterogeneous Internet connected objects. IoT analytics is an integral element of the vast majority of IoT applications, which process data in order to offer data-intensive services or to drive actuation and control decisions. The Velocity of IoT data streams is usually the attribute that differentiates IoT analytics systems from the majority of conventional BigData systems, which handle large volumes of transactional data. Therefore, IoT analytics systems are usually supported by middleware frameworks for streaming data (such as the open source Apache Storm, Spark and Flink frameworks), rather than the popular MapReduce BigData processing framework. Given their Big Data nature, IoT analytics systems are usually integrated with Cloud computing infrastructures, in order to take advantage of the scalability, storage capacity and processing performance of the Cloud.


Machine Learning and BIG Data Analytics on Microsoft AZURE

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This course is all about learning various cloud Analytics and Machine Learning options available on Microsoft AZURE cloud platform. We would be creating resources for Stream Analytics, Spark, HDInsight exploring options. We would be learning all the Analytics services with some use cases. Machine learning and cloud computing are trending domains and also have lot of job opportunities, if you have interest in machine learning as well as cloud computing then this course for you. This course will let you use your machine learning skills deploy in cloud.


Gartner Says the Future of the Database Market Is the Cloud

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By 2022, 75% of all databases will be deployed or migrated to a cloud platform, with only 5% ever considered for repatriation to on-premises, according to Gartner, Inc. This trend will largely be due to databases used for analytics, and the SaaS model. "According to inquiries with Gartner clients, organizations are developing and deploying new applications in the cloud and moving existing assets at an increasing rate, and we believe this will continue to increase," said Donald Feinberg, distinguished research vice president at Gartner. "We also believe this begins with systems for data management solutions for analytics (DMSA) use cases -- such as data warehousing, data lakes and other use cases where data is used for analytics, artificial intelligence (AI) and machine learning (ML). Increasingly, operational systems are also moving to the cloud, especially with conversion to the SaaS application model."