With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular type of stream analytics is the recurrent neural network (RNN) deep learning model based time series or sequence data prediction and forecasting. Different from traditional analytics that assumes data are available ahead of time and will not change, stream analytics deals with data that are being generated continuously and data trend/distribution could change (a.k.a. concept drift), which will cause prediction/forecasting accuracy to drop over time. One other challenge is to find the best resource provisioning for stream analytics to achieve good overall latency. In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM). We propose a novel edge-cloud integrated framework for hybrid stream analytics that supports low latency inference on the edge and high capacity training on the cloud. To achieve flexible deployment, we study different approaches of deploying our hybrid learning framework including edge-centric, cloud-centric and edge-cloud integrated. Further, our hybrid learning framework can dynamically combine inference results from an LSTM model pre-trained based on historical data and another LSTM model re-trained periodically based on the most recent data. Using real-world and simulated stream datasets, our experiments show the proposed edge-cloud deployment is the best among all three deployment types in terms of latency. For accuracy, the experiments show our dynamic learning approach performs the best among all learning approaches for all three concept drift scenarios.

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.

Azure Stream Analytics is a fully managed Platform as a Service (PaaS) that supports thousands of mission-critical customer applications powered by real-time insights. Out-of-the-box integration with numerous other Azure services enables developers and data engineers to build high-performance, hot-path data pipelines within minutes. The key tenets of Stream Analytics include Ease of use, Developer productivity, and Enterprise readiness. Today, we're announcing several new features that further enhance these key tenets. Let's take a closer look at these features: Rollout of these preview features begins November 4th, 2019.

Azure Stream Analytics is a fully managed Platform as a Service (PaaS) that supports thousands of mission-critical customer applications powered by real-time insights. Out-of-the-box integration with numerous other Azure services enables developers and data engineers to build high-performance, hot-path data pipelines within minutes. The key tenets of Stream Analytics include Ease of use, Developer productivity, and Enterprise readiness. Today, we're announcing several new features that further enhance these key tenets. Let's take a closer look at these features: Rollout of these preview features begins November 4th, 2019.

In previous posts (part 1 and part 2), I have explained the process of data live streaming. In the first post, I have explained the process of setting up the Raspberry PI 3 and Weather station. In the second one, I have explained the process of setting up the Event Hub that helps us to. In this post, I will explain the process of creating and setting up the Stream Analytics. Stream Analytics is an event processing engine, which helps to send data from Event Hub to Power BI and Azure ML.