In a wide range of industries, companies are deploying AI initiatives for a variety of purposes. Predictive analytics, pattern recognition systems, autonomous systems, conversational systems, hyper-personalization activities, and goal-driven systems are just a few examples of these applications. Each of these projects has one thing in common: they all require a grasp of the business challenge and the application of data and machine learning algorithms to the problem, resulting in a machine learning model that meets the project's requirements.

Machine learning initiatives are often deployed and managed in a similar manner. Existing app development approaches, on the other hand, are inapplicable because AI initiatives are driven by data rather than programming code. Data is used to generate the learning. Data-centric needs drive the correct machine learning methodology and methodologies, which result in projects that focus on data discovery, cleansing, training, model construction, and iteration.

There are three simple rules to follow in any machine learning project to ensure optimal value addition. While these three simple criteria aren't exclusive to machine learning projects, how they should be applied to ML projects differs from how they should be used to other projects.

Rules For Machine Learning Models

● Concentrate On The Financial Impact

What should drive model selection is what you can influence to get business performance. This implies having a set of actionable measures to optimize a set of metrics on which we're ready to make sacrifices.

The technique provides a framework for dealing with metric selection during the defining phase. Focusing on the KPIs that matter to the business has the benefit of addressing the system difficulties with machine learning prediction. A machine learning model's forecast is just one bolt of an engine, and if the system does some random things or modifies your input in an unintended way, you may have given the design some perfect data but still get garbage out.

This method contrasts sharply with what has typically been done in Software Engineering, emphasizing inputs. Because we want to focus on the output of a system, it's critical to track the impact of our model on the system's production. A good measuring and tracking system helps with output optimization and allows for a deeper dive into what drives the intended outcome.

● Concentrate On Concept

Focusing on an MVP in terms of concept, feature, and function, as with most software projects, is the way forward. Academia frequently tries to refine a near-perfect model before releasing it, and an MVP's emphasis forces it out the door to determine if it offers value.

Going straight to a deep learning model, a boosted model isn't worth it from the start. Starting with a simple and understandable model is usually better than optimizing with a better model too soon. If there is enough signal in the data, even simple models should make reasonable predictions. Focusing on simple interpretable models has the extra benefit of providing insight into the datasets and enabling early communication with various stakeholders.

Similarly, it is critical to understand what aspects should be researched and how much time should be spent trying out different features for model creation. Investigating all datasets, features, and transformations is time-consuming and frequently results in data quality issues.

The productionization process should be broken down into tiny steps that are completed quickly, and this should be backed up with an automated, fast-paced, iterative cycle.

● Iterate And Make Changes To The Model

You're not finished with the model just because it's up and running, and you're constantly evaluating its performance. It's commonly claimed that the key to success in deploying technology is to start small, think big, and iterate frequently.

Always go through the process again and make adjustments before moving on to the next iteration; the needs of the business shift. The capacities of technology evolve, and data in the real world grows in unexpected ways. All of this could result in new requirements for delivering the model to new endpoints or systems. Because the end could be the start of something new, it's best to figure out the following:

● model training is being expanded to include more capabilities;

● model performance and accuracy improvements;

● enhancements to the model's operating capabilities;

● Operational needs for various deployments and solutions to "model drift" or "data drift," resulting in performance changes owing to real-world data changes.

Consider what went well in your model, what could be improved, and what is still a work in progress. Continuously looking for improvements and better approaches to fulfill shifting business requirements is a specific strategy to succeed in machine learning models creation.


These three basic rules give a foundation for maximizing the usefulness of machine learning in the workplace. By focusing on an MVP approach and driving small iterative values quickly, showcasing results is possible. It also brings them on the same data-driven journey, which allows an ML user to benefit from a virtuous cycle and operate within a complex system that consumes their prediction to achieve their goal while also gaining insight and support from different stakeholders.