Artificial intelligence (AI), machine learning (ML), and data science are transforming modern organizations in new ways. 

These highly advanced technologies—along with a long queue of related technologies—have recently reshaped global organizations of all shapes and sizes. The cumulative success of these technologies in businesses is so huge that now the remaining organizations are all playing “catch-up” to survive or remain relevant.

The unique feature of ML technology is that it allows a computer to learn on its own—by using models (algorithms) and studying sample data to determine the correct answer. In ML, machines do not need prior programming to produce results; they can research sample datasets and train themselves to provide desirable results. The algorithm contains all the intelligent logic to test data at every processing step to arrive at the correct conclusion.

With the widespread penetration of AI across industry sectors and business practices, ML technology is being used across sectors such as manufacturing, finance, healthcare, insurance, etc.

Machine Learning Best Practices

This primarily deploys routine processes and tasks at high speed and automates many human and machine functions to increase efficiency and reduce errors. With the rising popularity of AI and machine learning technologies and tools, businesses need a set of “best practices” to benefit from their technology investments.

In case you forgot or needed a brush up on the best ways to use and deploy the right AI, ML, and data science strategy, here are a few things you should know:


Like all Data & Analytics strategies, there needs to be a concise and accurate assortment of data ready at the disposal of its users. Data is the centerpiece of any machine learning model; therefore, possessing data is crucial for model quality. Controlling the quality of the data and sanity-checking external sources reduces the risk of inaccurate models and outages in production. Besides performing sanity checks on the input data, it is recommended to check for data evolution constantly. In a continuously evolving environment, the data distribution will grow over time. For example, your user distribution per geographical region may change with time, leading to future biases towards over-representative areas.

Training & Teams

Alignment and training between disciplinary teams create a seamless and airtight process that optimizes a sound ML & AI strategy. With clear training objectives, your team can meet the demand of end-users and potentially democratize the applications they use for the organization. When working in a diverse group, it is essential to understand the background and roles of each member to avoid miscommunications and misunderstandings. Sometimes, different team members may fail to agree on the actual objective or misinterpret it together. This practice ensures that effort is not spent on futile activities and enhances team communication and efficiency. Moreover, it facilitates alignment with the team’s goal and ensures that ML & AI can correctly evaluate the training outcomes.


When making changes, existing code can quickly introduce new defects in the data. Setting up a suite of automated tests can help circumvent these defects and correct the problems early in development. These computerized tests can also allow for the experimentation of new functions rather than worrying about not breaking existing functionality. When adding new code, development teams should write tests to ensure that regression testing may also find bugs in the future. The style of programming called test-driven development goes one step further and advocates for writing a test for new functionality and only writing the code that provides that said function.


If your model is not deployed into production, does it exist? Once the team is ready for deployment, it’s essential to recognize the intricacies that go along with that. Monitor the behavior of the deployed models to avoid unintended mishaps and undesired outcomes. The performance between training and production data can vary drastically; it is essential to continuously monitor the behavior of deployed models and raise alerts when unintended behavior is observed. With successful monitoring, a team enables automatic rollbacks for these models. This paves the way for performance checks at a high and granular level.


Governance is arguably one of the essential aspects of this list, and stakeholders must be aligned on all your machine learning application's ethical values and constraints. Even without malicious intent, avoiding negative impacts requires all stakeholders to operate according to the same ethical values. A good starting point for sharing moral values across organizations is subscribing to a code of conduct. You can create one specifically for your situation or refer to a general governance framework. Defining or subscribing to a code of conduct helps to build trust with users and enhances the audibility of your development process and your applications.

With these highly advanced technologies reshaping global organizations of all shapes and sizes, The cumulative success of businesses depends solely on their ability to catch up to the market. Remember to be robust and organized at every step, as most of these ML & AI technologies never go into production.