A Proof of Concept (PoC), Or proof of concept is a demonstration or prototype designed to prove the feasibility of an idea, product or technology before it is fully developed. It is a key stage in various fields such as IT, engineering, innovation and project management.
A Proof of Concept is a preliminary stage in validating an innovation before it is developed on a large scale.
As part of an artificial intelligence project, a PoC is a key stage in demonstrating the feasibility of an AI model or AI-based solution before investing heavily in its development and deployment.
🎯 PoC objectives
- Check feasibility Ensuring that a technical solution or approach works in a controlled environment.
- Assessing risks identifying any technical difficulties or limitations to the project.
- Convincing stakeholders To provide tangible proof to investors, decision-makers or customers that the project is worthwhile.
- Optimising resources avoid committing too many resources (time, money, effort) to an unviable project.
As part of an AI project
- Validate technical feasibility
- Test whether the available data can be used to train a high-performance AI model.
- Assess whether a neural network algorithm or architecture meets the needs of the project.
- Assessing data quality and availability
- Check that the data is sufficient, clean and relevant to drive an effective model.
- Identify any gaps requiring additional collection or pre-treatment.
- Testing the model's initial performance
- Measure key metrics (precision, recall, F1-score, AUC-ROC, etc.) to see whether the model achieves an acceptable level of performance.
- Compare different approaches (classic ML, deep learningetc.).
- Demonstrating added value
- Prove to stakeholders that AI can solve the project's specific problem.
- Help in the decision to move on to a more advanced development phase (prototype or MVP).
- Identifying technical and business challenges
- Identify the limitations of the AI solution in the real environment.
- Anticipate constraints in terms of costs, infrastructure and integration with existing systems.
Differences between PoC, prototype and MVP :
- PoC Proof that the idea or technology works (without necessarily producing a usable product).
- Prototype Functional model for testing functions and concepts before final development.
- MVP (Minimum Viable Product) Minimum version of a product with essential functions, used to test the market.
📆 Steps in an AI PoC
1. Definition of the use case
- Identify the problem to be solved.
- Determine expectations in terms of performance and impact.
2. Data collection and preparation
- Check data availability.
- Cleaning, annotating and engineering features where necessary.
3. Selection of algorithms and models
- Choosing the right techniques (neural networks, decision trees, pre-trained models, etc.).
- Use appropriate frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
4. Training and evaluation of the model
- Experiment with different architectures and hyperparameters.
- Test the model on validation data and measure its performance.
5. Demonstration and validation
- Present the results to stakeholders with analyses and visualisations.
- Identify whether the project can evolve into a prototype or whether it requires adjustments.
