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.

Our team runs dozens of production machine learning models on a daily, weekly, and monthly basis. We recently went through a redesign of our ML infrastructure to increase its abilities to enable self-serve, scale to match computing needs, reduce impacts among models running on the same VM, and remove differences between dev and production environments. In this post, I will describe the challenges we faced with the previous infrastructure and how we addressed them with our Version 2 architecture. Our machine learning engineers use Python and R to implement models. Our Version 1 infrastructure used a custom XML format from which we generated Azure Data Factory (ADF) v1 pipelines to copy the model input data to blob storage.

By Li Yuan Some of the most critical work in advancing China's technology goals takes place in a former cement factory in the middle of the country's heartland, far from the aspiring Silicon Valleys of Beijing and Shenzhen. An idled concrete mixer still stands in the middle of the courtyard. Boxes of melamine dinnerware are stacked in a warehouse next door. Inside, Hou Xiameng runs a company that helps artificial intelligence make sense of the world. Two dozen young people go through photos and videos, labeling just about everything they see.

Some of the most critical work in advancing China's technology goals takes place in a former cement factory in the middle of the country's heartland, far from the aspiring Silicon Valleys of Beijing and Shenzhen. An idled concrete mixer still stands in the middle of the courtyard. Boxes of melamine dinnerware are stacked in a warehouse next door. Inside, Hou Xiameng runs a company that helps artificial intelligence make sense of the world. Two dozen young people go through photos and videos, labeling just about everything they see.

In recent years, deep learning has completely revolutionized the fields of computer vision, speech recognition and natural language processing. Despite breakthroughs in all three fields, one common barrier for training neural networks to solve real-world problems remains the amount of labeled training data that is required to train a model. In some domains, like video understanding, gathering real world data can be prohibitively expensive and time consuming in the absence of innovative solutions. At TwentyBN, we solved this problem by building an in-house data factory for generating high-quality videos for neural networks to learn about the real world. We instruct crowd workers to record short video clips based on carefully predefined and highly specific descriptions.

In the fall of 2016, a pop song was released in Japan. "Daddy's Car," derivative of a Beatles tune, had a soothing beat and vaguely uplifting lyrics: "Good day sunshine in the backseat car / I wish that road could never stop." The ditty was distinctive for its authorship. Sony's Computer Science Laboratories in Paris produced the song, which was written by an artificial intelligence (AI) system called Flow Machines. The melody and harmony were composed by AI, and a human musician mixed the sound and wrote lyrics for the track. AI -- the new set of technologies that perform tasks that require human intelligence, such as speech recognition, decision making, and learning -- is rapidly working its way into business operations within many global industries. Some members of the entertainment and media (E&M) industry have downplayed its potential. After all, these are creative industries in which both the germ of the business and the value added to it stem from the contribution of human ingenuity and people exchanging ideas. The most successful E&M products and services rely on connecting creative content, brands, and experiences with audiences.

In the fall of 2016, a pop song was released in Japan. "Daddy's Car," derivative of a Beatles tune, had a soothing beat and vaguely uplifting lyrics: "Good day sunshine in the backseat car / I wish that road could never stop." The ditty was distinctive for its authorship. Sony's Computer Science Laboratories in Paris produced the song, which was written by an artificial intelligence (AI) system called Flow Machines. The melody and harmony were composed by AI, and a human musician mixed the sound and wrote lyrics for the track. AI -- the new set of technologies that perform tasks that require human intelligence, such as speech recognition, decision making, and learning -- is rapidly working its way into business operations within many global industries. Some members of the entertainment and media (E&M) industry have downplayed its potential. After all, these are creative industries in which both the germ of the business and the value added to it stem from the contribution of human ingenuity and people exchanging ideas. The most successful E&M products and services rely on connecting creative content, brands, and experiences with audiences.

Steel production is an area that has been studied for decades, and as such the industry has remained very conservative. Despite the big data revolution beginning in the early 2000s, "old-school" industries like steel-making have largely shunned any form of data-driven applications. Fortunately, things change, and here's an example of how data analytics technologies, born within the internet industry, can be applied to an offline practice like turning pig iron into steel. When we began work with Magnitogorsk Iron and Steel Works (MMK), one of the world's largest steel producers and a leading steel company in Russia, a lot of time was spent looking for a challenge that if solved, could (a) positively impact business revenues, and (b) be completed in reasonable time.The challenge that was eventually uncovered and able to meet these criteria, is one well-known to all metallurgists: how much of each ferroalloy to add during steel-making process in order to ensure the required chemistry of the steel at the lowest possible cost. This chemistry is dictated by the international standards for steel – a list of required ranges for the amounts of each element in the final mix.