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About Vimana MENA
Background
Vimana MENA specializes in developing AI solutions for industrial and commercial applications. With seven years of experience in the field, we have successfully deployed cutting-edge and unique AI-driven solutions across various industries.
We are committed to transforming even the most complex requirements into AI-powered solutions, ensuring no challenge is too difficult to tackle. Our focus is on delivering top-quality AI solutions at the most competitive price, combining innovation with cost effectiveness.
The technology we utilize is based on AI components, enabling real-time data processing across industries. We deploy cost-effective hardware, including industrial cameras and edge computing devices, connected via Ethernet, wireless, or USB. Processing is handled on-premises for low-latency needs or in the cloud (Azure) when network speed allows.
Our expertise spans multiple AI domains: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Predictive Analytics, Generative AI, Speech Recognition, Recommendation Systems, Supply Chain Optimization, Fraud Prevention, Education, Retail, Manufacturing, and Personalized Medicine.
Underlying Technology
Neural Network
We employ various types of neural networks based on the specific needs of each solution. This approach gives us exceptional flexibility, enabling a rapid and cost-effective development cycle—setting us apart from other players in the industry.
Our current models include ResNet50, ResNet100, YOLO, and others. Additionally, we can combine different models within the same infrastructure to deliver tailored solutions.
Once a solution is deployed, two key components run in parallel to ensure seamless AI delivery:
EXECUTION PROCESS:
This component runs the neural network, allowing real-time analysis of incoming images and comparison with the active solution
DATA PREPARATION:
This stage processes and refines data for machine learning, leveraging real-world accumulated data to enhance model performance.
Execution process
Convolutional Neural Networks (CNNs) on Azure:
KEY TECHNOLOGY:
●Azure Machine Learning: Scalable environment for training and deploying CNN models.
●Azure Cognitive Services: Pre-built APIs for image recognition, object detection, and facial recognition powered by CNNs.
APPLICATIONS:
●Custom Vision: Build and deploy image classification models.
●Azure Video Indexer: Analyze and extract insights from video content using CNNs.
AZURE COMPUTE RESOURCES:
●Azure Virtual Machines (VMs): Use GPU-based VMs for accelerated model training.
●Azure Kubernetes Service (AKS): Deploy CNN models at scale using containerized services.
Process of model creation
Use Azure Data Factory for data cleaning and transformation
Store datasets in Azure Blob Storage.
Build models with Azure Machine Learning Studio or Jupyter Notebooks.
Support for TensorFlow, PyTorch, and Keras.
Scale with Azure ML Compute (GPU VMs) for efficient training.
Leverage Azure ML HyperDrive for hyperparameter tuning.
Use built-in evaluation tools and TensorBoard for performance tracking.
Monitor metrics with Azure ML Metrics.
Use automatic delivery system to deploy model on device