How to Compare ML Solutions Effectively | By Henny de Harder | July 2023

In addition to prediction results, there are several other important factors to consider when comparing machine learning prototypes. These factors provide valuable insights into the overall suitability and effectiveness of the models in real-world scenarios. By focusing on more than just predictive power, you increase the chances of getting your machine learning solution into production.

The factors are grouped into four categories: maintenance, implementation complexity, costs, and business requirements. First, the project team must decide which factors are important to the project. Developers can already take notes on various factors when creating prototype solutions.

how difficult data collection or to perform Artistic engineering? ̦ Many different libraries And there is a model Sensitive to parameter tuning? Does it use the project? Standard APIs Can you put it in the pipeline? These aspects make it easier or harder to maintain the solution.

If your data comes from many different internal and external sources, this is at a disadvantage compared to a solution that only relies on internal company data. The reason is that you cannot completely rely on external sources to remain unchanged, and any changes or updates to those sources will require rework or adjustments in your solution. This is an example of a technical problem that may occur.

Another part of maintenance Monitoring. This involves tracking metrics, detecting performance anomalies or degradation, and debugging issues that may arise. Some models provide powerful monitoring and debugging capabilities. This can be an advantage over other models.

Implementation difficulty measures the complexity and effort involved in deploying a model into a production system. It takes into account factors such as: Availability of required libraries, Complexity of model architectureand Compatibility with existing infrastructure. A model that is easy to implement and integrate into existing systems can save valuable time and resources during the deployment phase.

Another factor that can affect the difficulty of implementation is Getting to know the approach. Choosing a model that fits the team’s skill set can significantly impact development timelines.

It’s easy to develop a model that costs a lot of money. Costs are an important factor for almost any company. If you need an expensive license for a certain solution, you need to be able to justify why the license is worth the cost.

You can spend money Data mining, data storage, (re)training, conclusion or licenses and Subscriptions. also, resources There is a cost to developing a solution. By making an educated guess about these costs for each solution, comparing solutions becomes another factor.

If the costs exceed the budget (or the value that the model will bring), you need to reconsider the approach. It can also happen that two solutions score the same on all factors except cost. In this case, the choice is simple, the cheaper solution is better.

Last but not least are business requirements. They can take many forms; Here are some common ones:

• interpretation
  Understanding and explaining specific forecasts is an important part of some business processes. In this case, the simple explanatory model may be more important than the predictive power. If interpretation is important, you should try to keep the model simple. You can experiment with different interpretation techniques and evaluate how easy it is to use the techniques with the model.
• Time on the market
  In competitive industries or when considering time-sensitive opportunities, speed of model development and implementation can be a critical business requirement. Minimizing time to market can be critical to gaining a competitive advantage. Models that can be developed and deployed quickly, with minimal iterations or complex preprocessing steps, can be beneficial in such scenarios.
• Regulatory compliance
  Some industries, such as finance, healthcare and insurance, have strict regulations and compliance standards. Business requirements may include ensuring that selected models comply with these regulations, such as data privacy laws (eg, GDPR), industry-specific guidelines, or ethical considerations. Models must comply with relevant regulations to avoid legal and reputational risks.
• Real-time inference
  Some applications require real-time forecasts where decisions must be made within tight time constraints. Business requirements may indicate the need for low-latency models that can quickly process incoming data and generate predictions in real-time. Models that offer efficient real-time inference capabilities are critical for time-sensitive applications such as fraud detection or recommendation systems.