5 Steps to build an AI Vision MVP

Five Steps to Build an Efficient Computer Vision MVP

I’ve built dozens of MVPs (Minimum Viable Products) for computer vision solutions across various clients, including companies, researchers, universities, and students. These MVPs ranged from automating internal processes to game functionalities, object detection, and segmentation. Through this experience, I have developed a five-step process that ensures efficiency in building MVPs that satisfy clients and users. Also to Lean Startup methodology MVP is an important component.

In this article, I will walk you through these five steps to help you build efficient computer vision MVPs.

Understanding MVP vs. Proof of Concept (POC)

Before we dive into the steps, let’s clarify the difference between an MVP and a POC (Proof of Concept).

• MVP (Minimum Viable Product): An MVP is a functional version of the product that offers some features and works with the final client to gather feedback. It’s not the final product, but it’s a working solution with key features that can be tested by users.
• POC (Proof of Concept): A POC comes before the MVP and is designed to demonstrate the feasibility of a concept. It helps prove to stakeholders that a particular solution is possible, often to secure funding or investments.

Example:

Let’s say a company’s goal is to build software to automatically identify defects on products moving on a conveyor belt. A POC might demonstrate that a deep learning algorithm can identify defects in video footage. On the other hand, the MVP would be a basic version of the actual software that can detect at least some defects in real time on the production line, ready for feedback from the client.

Step By Step, building an AI Minimum Viable Products

To better understand the differences, let’s see the step-by-step instructions to easily build a computer vision MVP

Step 1: Define the Problem of AI Vision MVP

Before you start building, you must clearly define the problem. Without a well-defined problem, your project risks veering off course.

When defining the problem:

1. Identify the Core Objective: Be specific. For example, if the goal is to automate defect detection, define exactly what the software should do, such as identifying defects in real time and alerting users.
2. Narrow the Scope: Break down complex problems into manageable tasks. For instance, if there are multiple types of defects (e.g., scratches, color variations, size discrepancies), focus on the most important one for the MVP.
3. Define Success Metrics: Determine how success will be measured. Is it speed, accuracy, or user satisfaction? For example, if the client’s goal is to improve defect detection accuracy from 70% to 95%, then accuracy is the primary success metric.

Step 2: Choose the Right Tools

Selecting the right tools is critical, and this involves both software (libraries and frameworks) and hardware.

1. Software Tools: Choose libraries and frameworks that enable fast and efficient development. These tools should make things simpler, not harder. You can always switch to more specialized tools later as the product evolves.
2. Hardware: Start simple with hardware. For instance, if your MVP can run on a standard laptop, there’s no need to complicate things by jumping to specialized hardware like Nvidia Jetson boards unless it’s a strict requirement from the beginning.

Step 3: Data Collection & Annotation

Data is the backbone of any deep learning project, and proper data collection is vital for success. Many developers underestimate this step, which leads to major problems later on.

1. Collect the Right Data: Ensure the data you collect is specific to your project. Pre-trained models often won’t work perfectly because they weren’t trained on data relevant to your application. For example, if you’re working on CCTV footage for people detection, a generic model won’t perform well with low-light or night vision.
2. Annotate Accurately: Accurate data annotation is essential. Poorly annotated data leads to unreliable models, so take the time to ensure your bounding boxes and labels are precise.

Step 4: Make It Simple to Use

A well-built MVP should be easy for the client and users to use. Simplicity ensures that you won’t need to spend a lot of time providing support.

To achieve this:

1. Easy Installation: Make the installation process a no-brainer, especially for non-technical users. Provide installers or pre-configured SD cards if needed, so the client can run the software with minimal effort.
2. Simple Interface: Keep the interface clean and minimal. Only display information that is necessary for the user. Hide technical details or statistics that don’t add value to the user experience.
3. Documentation: Provide concise documentation. Ideally, a simple two-page PDF that explains the basic features and instructions. Keep it short and focused.

Step 5: Simplify the Feedback Collection of the MVP

The purpose of an MVP is to gather feedback. Make it easy for users to share their experiences and report issues.

1. Automate Feedback: Build mechanisms to automatically collect usage data and errors. This can be done by logging events or gathering feedback with simple forms after key actions.
2. Automatic Bug Reporting: Set up systems that automatically collect debugging information and send it to a database or store it locally for analysis.

Conclusion

These five steps—defining the problem, choosing the right tools, collecting accurate data, making the product simple to use, and simplifying feedback collection—are the keys to building efficient computer vision MVPs. Following this process ensures that your MVP functions effectively and meets the needs of both developers and clients.

If you want to see a similar process you can see how I made it How I built a Computer vision prototype in less than 48 hours