AI Computer Vision Solutions

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.