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Data Annotation
Our data specialists use cutting-edge tools to transform raw data into finely tuned training datasets while maintaining ethical best practices. The result: AI models with exceptional accuracy, rapid development cycles, and high performance in real-world scenarios.
Data Acquisition
Aya Data can acquire top-quality data to fuel your AI models. Our diverse industry network enables us to gather valuable, hard-to-find information through field collection, web scraping, surveys, and other targeted methods.
AI Consulting
From generative AI to computer vision, 3D modelling, speech recognition, and predictive analytics, Aya Data delivers bespoke AI solutions to propel your projects forward. We offer end-to-end support, guiding you from initial concept to deployment and ongoing model optimisation.
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- Proven track record of success
From Our Blog
6 Reasons Why an eCommerce Business Cannot Function Without Data Labeling
eCommerce is one of the world’s largest industries, forecast to account for 22% of all global retail sales by the end of 2023. The infrastructure required for smooth and efficient eCommerce has evolved to support this growth.Online shops are now extremely intelligent, with recommendation systems, personalization, and marketing automation that targets customers at precisely the right time to drive sales.
How to Find Training Data for Machine Learning
Supervised machine learning projects require training data. By learning from training data, a supervised algorithm aims to be able to accurately predict outcomes when exposed to real data.Training data is required for all types of supervised machine learning projects Images, video, LiDAR, and other visual media are annotated for the purposes of computer vision (CV). Audio is annotated and labeled for the purposes of training conversational AIs and technologies with audio sensors.
The Different Types of Data Annotation Explained
Data annotation is a basic step,the foundation of all artificial intelligence and machine learning projects. You simply can’t have a functioning ML model that was created without processing data. And there are many types of data annotation, depending on the needs of given projects.That is what will be discussed in this article - the different types of data annotation, from image annotation to text annotation, exploring their processes and significance.
Definitive Guide to Computer Vision in AI
Artificial intelligence (AI) has risen from a fringe concept in sci-fi to one of the most influential technologies conceived. Building systems that can understand visual information has been a cornerstone of AI research and development. This allows machines to ‘see’ and respond to the world around them. The domain of AI related to vision and visual data is called computer vision (CV). Computer vision equips computers with the ability to process, interpret, analyze, and understand visual data.
What Is Audio Transcription and How Does It Relate to Data Labeling?
Audio transcription is the process of converting unstructured audio data, such as recordings of human speech, into structured data. Artificial intelligence (AI) and machine learning (ML) algorithms require structured data to perform various tasks involving human speech, including speech recognition, sentiment analysis, and speaker identification. In short, audio transcription is fundamental for teaching computers to understand spoken language.
What Is Polygon Annotation and How Does it Work?
Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict and classify objects when exposed to new, unseen data. The physical environment primarily consists of complex shapes with non-linear edges – polygon annotation is considerably more effective at labeling them when compared to bounding boxes, which include a lot of useless information.
Image Annotation: Everything You Need to Know
As technology continues to advance, the demand for image recognition and object detection has skyrocketed. From self-driving cars to medical imaging, accurate and reliable data annotation is crucial for these AI systems to function effectively. This comprehensive guide covers everything you'll need to understand this component of machine learning.
Transforming Tree Counting for Large-Scale Farming
The large-scale oil palm plantation and the orderly rows of banana trees might look serene, but their management presents complex challenges. From overseeing the hectares of plantation to keeping an eye on the dying plants and restoring the health of wilted ones to manually keeping a count of the trees, numerous problems can arise. Manual labor and traditional methods not only waste resources but also require more time. Plus, there is always room for inaccuracy.
How Drones and Computer Vision are Used to Enhance Crop Yield
Drones or UAVs (Unmanned Aerial Vehicles) are propelling a revolution in farming and agriculture. In 2019, the FAO published E-Agriculture in Action: Drones for Agriculture, a detailed guide and roadmap of how drones are being used in agriculture, and the best practices for the future. The agricultural drone market is growing and is expected to surpass $4.8 billion by 2024.
Predicting Crime With AI: Navigating Ethical Issues
In June 2022, a cross-discipline team of researchers at the University of Chicago created an AI model that could predict the location and rate of crime in the city with 90% accuracy. The team utilized citywide crime open data between the years of 2014 and 2016, dividing the city into squares of around 1000ft (300m). The resulting model could predict the square where crime was most likely to occur 1-week in advance.
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