Machine Learning PoC : Only 10% work done.

A product proof of concept (POC) and a machine learning (ML) model POC are two different types of POCs with distinct objectives and outcomes. A product POC aims to validate the feasibility of a new product idea, whereas an ML model POC aims to test the viability of an ML model to solve a specific problem.

Product POCs typically involve developing a prototype of a new product or service and testing it with a small group of users to validate the concept’s feasibility. The goal of a product POC is to determine whether the product can be developed and delivered at a reasonable cost and whether there is a market demand for it. In many cases, a product POC is close to delivering value, and the primary challenge is scaling the product to a larger market.

On the other hand, an ML model POC involves developing an ML model to solve a specific problem, such as predicting customer behavior or identifying fraudulent transactions. The goal of an ML model POC is to test the viability of the model and determine whether it can deliver accurate and reliable predictions. ML model POCs typically require significant investment in data preparation, algorithm development, and model evaluation, and may not be close to delivering value at the end of the POC.

There are various reasons why a machine learning (ML) proof of concept (POC) may look good but fail to deliver value for organizations. Some of the common reasons are:

1. Lack of scalability: Often, POCs are developed in isolation, and the focus is on getting results quickly without considering scalability. As a result, when these models are deployed in the real-world scenario, they may not be able to handle the volume and variety of data, resulting in poor performance.
2. Inadequate testing: In many cases, ML POCs are not rigorously tested before deployment. While the POC may have shown promising results on a limited dataset, it may not perform well on diverse and real-world data. Therefore, organizations should perform comprehensive testing to ensure the model’s reliability and robustness.
3. Limited data: Machine learning algorithms rely on data to learn and make predictions. If the POC is developed on a small or limited dataset, it may not be representative of the real-world scenario, leading to poor performance when deployed.
4. Lack of business value: The success of an ML project depends on its ability to solve real-world business problems. A POC may look good from a technical perspective, but it may not deliver any real business value or solve any actual problems.
5. Lack of integration: Integrating an ML model with existing systems and processes can be complex and challenging. If the POC is not designed with integration in mind, it may be difficult to integrate into the organization’s workflow, resulting in limited adoption and value delivery.