OpenCV.ai has published an insightful guide aimed at assisting businesses in budgeting for computer vision AI solutions. The second part of their series, titled “How to Budget for a Computer Vision AI Solution? Part 2 | Software,” dives into the critical software components impacting budgetary considerations.
This article covered hardware selection, and now shifts focus to software, the brain of AI projects, crucial for performance and cost management. The guide emphasizes several key areas:
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- Task Analysis: Understanding the problem is the first step. Analyzing task requirements helps balanced cost, deadlines, and quality. Expert guidance is recommended for initial problem analysis and solution design to foresee potential issues and set clear priorities.
- Data Collection:Â Data quality directly affects AI effectiveness. The article highlights the importance of relevant and diverse data, balancing quality and quantity. Examples illustrate standard versus specialized setups, emphasizing that specific tasks may require costly, targeted data collection.
- Data Annotation:Â Labeling data accurately is crucial for training algorithms. The complexity and cost vary based on the task, with specialized fields like medical imaging requiring expert annotators, increasing costs significantly. The volume of data also plays a role, with larger datasets demanding more annotation time and resources.
- Model Development:Â Developing deep learning models involves experimentation and refinement. Decisions on whether to use existing open-source models or build custom solutions impact costs and timelines. The choice of development framework and hardware for deployment are also significant factors.
- Pipeline Implementation:Â Integrating multiple models into a seamless workflow is essential. This includes pre-processing raw data, synchronizing model outputs, and managing data flow efficiently. Complex projects, like tracking individuals across multiple cameras, demand careful consideration of each component’s interaction.
- Pipeline Deployment to Hardware:Â The choice of deployment platform affects costs and implementation complexity. From on-premise servers to edge devices and cloud solutions, each option has its own set of challenges and advantages. The guide emphasizes the importance of ensuring compatibility and optimizing performance for real-time applications.
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