Besides prediction results, there are several other essential aspects to contemplate when comparing machine learning prototypes. These aspects provide precious insights into the general suitability and effectiveness of the models in real-life scenarios. By focusing not only on predictive power, your probabilities of getting your machine learning solution to production increase.

The aspects are grouped in 4 categories: maintenance, implementation complexity, costs, and business requirements. Up front, the project team should resolve which aspects are essential for the project. During creation of the prototype solutions, developers can already take notes about the various aspects.

How hard is it to collect data or to perform feature engineering? Do you employ many various libraries and is the model sensitive to parameter tuning? Is the project using standard API’s you’ll be able to place in a pipeline? These facets make an answer easier or harder to keep up.

In case your data is coming from many various internal and external sources, it presents an obstacle in comparison with an answer that solely relies on internal company data. The explanation is that you simply cannot completely depend on the external sources to stay unchanged, and any alterations or updates in those sources would require refactoring or adjustments in your solution. That is an example of a maintenance issue that may arise.

One other a part of maintainability is monitoring. This involves tracking metrics, detecting anomalies or degradation in performance, and debugging issues which will arise. Some models provide robust monitoring and debugging capabilities. This could be a bonus over other models.

Implementation complexity measures the problem and energy involved in deploying a model right into a production system. It takes into consideration aspects similar to the availability of vital libraries, the complexity of the model architecture, and the compatibility with existing infrastructure. A model that is simple to implement and integrate into existing systems can save precious time and resources throughout the deployment phase.

One other factor that may influence implementation complexity is familiarity with the approach. Selecting a model that aligns with the team’s skill set can significantly impact the event timeline.

It’s easy to develop a model that costs a whole lot of money. Costs are a vital factor for nearly any company. In case you need an expensive license for a certain solution, you must have the opportunity to justify why that license is definitely worth the costs.

You possibly can spend money on data acquisition, data storage, (re)training, inference or licenses and subscriptions. Also the resources for developing the answer have a certain cost. By making an informed guess about these costs upfront for each solution, it becomes one other factor to match solutions on.

If the prices exceed the budget (or the worth the model will bring), you must reconsider an approach. It could actually even be the case that two solutions rating the identical on all aspects except the prices. In that case the selection is simple, the cheaper solution is the higher one.

Last but not least are the necessities of the business. They’ll are available many forms; listed below are some common ones:

• Interpretability
  Having the ability to understand and explain specific predictions is a crucial a part of some business processes. In that case, a model that is simple to clarify could be of upper importance than predictive power. If interpretability is vital, you must try to maintain the model easy. You possibly can experiment with different interpretation techniques and rating how easy it’s to make use of the technique along with the model.
• Time-to-market
  In competitive industries or when addressing time-sensitive opportunities, the speed at which the model could be developed and deployed could also be a critical business requirement. Minimizing the time-to-market could be essential to realize a competitive advantage. Models that could be developed and deployed quickly, with minimal iterations or complex preprocessing steps, could be advantageous in such scenarios.
• Regulatory compliance
  Certain industries, similar to finance, healthcare, and insurance, have strict regulations and compliance standards. Business requirements may include ensuring that the chosen models adhere to those regulations, similar to data privacy laws (e.g., GDPR), industry-specific guidelines, or ethical considerations. Models should be compliant with relevant regulations to avoid legal and reputational risks.
• Real-time inference
  Some applications require (near) real-time predictions, where decisions have to be made inside strict time constraints. Business requirements may specify the necessity for low-latency models that may quickly process incoming data and generate predictions in real-time. Models that provide efficient real-time inference capabilities are crucial for time-sensitive applications like fraud detection or suggestion systems.