The development process of an AI (specifically, machine learning) model involves several essential steps.
Advancing Industries with Computer Vision Technologies
Computer vision is an area within artificial intelligence (AI) focused on training computers and systems to interpret and analyze information from images, videos, and other visual data. By leveraging machine learning and neural networks, computer vision enables systems to recognize patterns, detect flaws, and respond to visual cues by making recommendations or taking appropriate actions.
What is Computer Vision?
Computer vision is an area within artificial intelligence (AI) focused on training computers and systems to interpret and analyze information from images, videos, and other visual data. By leveraging machine learning and neural networks, computer vision enables systems to recognize patterns, detect flaws, and respond to visual cues by making recommendations or taking appropriate actions.
CV General Process Flow
- Cameras:
– Mobile Devices
– Stationary
– Body-camera
– Drones
– Satellites
– Microscopes
– Telescopes, etc. - Sensors:
– LiDARs
– Kinect
– Infrared Sensors
– Ultrasonic
– Stereo Cameras, etc. - Document Scans
- Images & Videos
- X-rays
- MRIs
- CT Scans
- Depth and 3D Data
- Sensor Data: Infrared, Thermal,
Ultrasonic images - Annotated and Contextual Data
- Synthetic and Augmented Data
- Azure AI Vision
- Google Cloud Vision AI
- TensorFlow
- Adobe Sensei
- NVIDIA Clara
- Straico
- Amazon Rekognition
- IBM Watson Visual Recognition
- Object Detection and Recognition
- Image Segmentation and Mapping
- Tracking and Monitoring
- Measurement and Analysis
- Text and Information Extraction
- Action Triggers and Automation
- Visualization and Interpretability
Transforming Sectors with Visual Intelligence
AI/ML Workflow
1. Business Analysis
This is the most important step toward understanding the business context. Many things will be analyzed, including internal and external factors that drive the decision, challenges to be addressed, desired outcomes, and metrics for implementing an AI-based solution.
2. Data Understanding
To build an effective AI model, it’s mission-critical to understand whether the available data set is sufficient for the identified tasks and challenges. Our team will analyze it in detail, consult with you, and share insights about the available data’s quality.
Should the data prove to be not fully available or inconsistent, Intetics’ team will suggest proceeding with additional data acquisition.
2.1. Data Collection*
This sub-step presumes the process aims to collect the required data from available sources, both internal and external, to feed the AI model.
*Optional step if the dataset isn’t of the required amount and quality level.
2.2. Data Processing/Mining
Data processing is a technical process, sometimes automated or semi-automated, designed specifically to collect, transform, and load the data (ETL). Usually, an automated data processing pipeline will be set up in the future.
The data processing stage often includes data labeling, which is the process of identifying raw data (images, text files, videos, etc.) to provide the necessary context for the AI model to learn.
3. Model Selection and Training
Based on the data parameters and business goals, AI experts will select a few AI/ML approaches to confirm the best methodology with the highest predictable accuracy. It’s very similar to a contest between AI models to prove its efficiency and scalability.
4. AI Model Assessment
Once the model is selected and trained, it will be tested against the validation data set to prove its effectiveness in the real world.
5. Production Deployment
When the model demonstrates the required KPIs according to the declared business needs, the team deploys it to the customer’s production environment, e.g., a web/mobile application.
6. Fine-Tuning & Maintenance
The final stage is to continuously improve the AI model’s performance. This includes eliminating “noise” and adding additional parameters to increase the accuracy and computational speed when working with new data.
It’s worth noting that while the above provides a general framework, the development of AI models can be complex, and additional considerations might arise based on the specific context, domain, and use case. Collaboration with domain experts, constant validation, and careful consideration of ethical implications are also essential throughout the process.
Computer Vision: Case Studies
- Case Study
Switching from 3d-party car parts database provider to a proprietary CV-based model enabled accurate measuring and cost-cutting.
- Case Study
The implemented algorithm confirmed that the recognition of emotions from the data of biosensors is possible and works.
- Case Study
Customizable technology boosts customer satisfaction and sales.
- Case Study
The Client received a solution that automated parking sign detection and reduced the workload.
Leveraging CV for Impact
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Failed to send your message. Please contact us by email contact2023@intetics.com or by phone
+1 (239) 217-4907
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+1 (239) 217-4907
Failed to send your message. Please contact us by email contact2023@intetics.com or by phone
+1 (239) 217-4907
Failed to send your message. Please contact us by email contact2023@intetics.com or by phone
+1 (239) 217-4907