In this study, we propose a big data pipeline for cotton bloom detection using a Lambda architecture, which enables real-time and batch processing of data. Our proposed approach leverages Azure resources such as Data Factory, Event Grids, Rest APIs, and Databricks. This work is the first to develop and demonstrate the implementation of such a pipeline for plant phenotyping through Azure's cloud computing service. The proposed pipeline consists of data preprocessing, object detection using a YOLOv5 neural network model trained through Azure AutoML, and visualization of object detection bounding boxes on output images. The trained model achieves a mean Average Precision (mAP) score of 0.96, demonstrating its high performance for cotton bloom classification. We evaluate our Lambda architecture pipeline using 9000 images yielding an optimized runtime of 34 minutes. The results illustrate the scalability of the proposed pipeline as a solution for deep learning object detection, with the potential for further expansion through additional Azure processing cores. This work advances the scientific research field by providing a new method for cotton bloom detection on a large dataset and demonstrates the potential of utilizing cloud computing resources, specifically Azure, for efficient and accurate big data processing in precision agriculture.

Organizations create more data than ever before. Newer, faster technologies and storage systems have risen to the challenge, but the storage elements of artificial intelligence can be complex. AI data often requires high-performance, scalable storage and long retention periods. Organizations must find cost-effective storage systems to protect, manage and analyze large amounts of data; to ensure short and long-term success, it's crucial for organizations to assess their storage and data management needs throughout an AI project. Chinmay Arankalle, author and data engineer, has spent years working on big data systems.

The impression I had for implementing Machine Learning up to 3 years back was that of building a model in Python and deploying the project to an automated CI/CD pipeline. While it solved the basic criteria of performing predictions, it could never be called an end-to-end workflow because data storage and reporting were two significant components missing in this workflow and had to be dealt with separately. In this article, I will walk through an entire Machine Learning Operation Cycle and show how to establish every step of the way using Azure Data Factory (ADF). Yes, it is possible, easy, and extremely reliable. As a bonus, it also automatically sets you up to receive alerts for any sort of data anomalies occurring throughout the process, so you do not have to worry about monitoring the workflow manually.

Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines. This enables you to run your machine learning models with data from multiple sources (85 data connectors supported in ADF). This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.

After releasing Microsoft Azure Data Factory v2 (ADF) in public preview in September, Microsoft has recently followed up with the announcement of a public preview of new visual tooling for the fully managed cloud-based data integration and ETL service. However, for the September release of the service Visual tooling was not available, making it a manual process to create ADF v2 components and pipelines. The recent release of the visual tooling brings the service more in line with the previous version. The tooling is web-based and is launched in the Azure portal from within the deployed Azure Data Factory. To support copy activities and offloading of computing tasks, there is an additional type of Integration Runtime component that is either Azure-based or Self-Hosted.

In this post we will discuss reference architecture for Big Data and Advanced Analytics using Cortana Intelligence Suite. The architecture can be relevant for organizations looking to fully manage big data and advanced analytics to transform all enterprise information into intelligent action. This will allow to take action ahead of your competitors by going beyond looking in the rearview mirror to predicting what's next. In general, in such solutions you use relational and semi-structured data from business and custom applications, and also semi-structured or unstructured data from sensors, devices, web sites, social networks and other sources. Big Data Reference architecture represents most important components and data flows, allowing to do following.

Beginning in 2016, Microsoft rolled out a preview of Microsoft R Server (MRS) for Azure HDInsight clusters. Recent blog posts (by Max Kaznady and David Smith) have highlighted how to use and tune this service for large scale machine learning tasks. In this post, we push the envelope and show how to build an end-to-end fully operationalized analytics pipeline using Azure Data Factory (ADF) and MRS with HDInsight (specifically Apache Spark). By integrating Azure Data Factory with Microsoft R Server and Spark, we show how to configure a scalable training and testing pipeline that operates on large volumes of data.

In this post we will discuss reference architecture for Big Data and Advanced Analytics using Cortana Intelligence Suite. The architecture can be relevant for organizations looking to fully manage big data and advanced analytics to transform all enterprise information into intelligent action. This will allow to take action ahead of your competitors by going beyond looking in the rearview mirror to predicting what's next. In general, in such solutions you use relational and semi-structured data from business and custom applications, and also semi-structured or unstructured data from sensors, devices, web sites, social networks and other sources. Big Data Reference architecture represents most important components and data flows, allowing to do following.

In this post we will discuss reference architecture for Big Data and Advanced Analytics using Cortana Intelligence Suite. The architecture can be relevant for organizations looking to fully manage big data and advanced analytics to transform all enterprise information into intelligent action. This will allow to take action ahead of your competitors by going beyond looking in the rearview mirror to predicting what's next. In general, in such solutions you use relational and semi-structured data from business and custom applications, and also semi-structured or unstructured data from sensors, devices, web sites, social networks and other sources. Big Data Reference architecture represents most important components and data flows, allowing to do following.

For orchestrating workloads on Azure ML (Machine Learning) batch execution web services, Azure Data Factory supports a built-in activity, namely Azure ML Batch Execution activity. Customers can leverage this activity to operationalize their ML models at scale. Little while ago, Azure ML added support to allow multiple Web Service Inputs for a given experiment. Consequently, customers have been looking to leverage this capability through Azure Data Factory. Data Factory now supports configuring the ML Batch Execution Activity to pass multiple Web Service Inputs to the ML web service.