What good is data if you can’t draw any insights from it? AI & ML development service vendors in India are helping businesses make this sense through rule-based AI systems and machine learning models. While both are equally preferred, each has its pros and cons. While both contribute to automating processes and making sense of the data that these processes produce, which one is that you must invest in? We’ll help you figure out if you need to invest in rule-based AI systems or machine learning models. Let’s start with what these are.

What Are Rule-Based AI Systems?

It is a system that realizes artificial intelligence through a rule-based model. Based on a certain set of coded rules as defined by humans, such AI is able to generate predefined results. These simplistic models operate on if-then coding statements/rules. The two major components these systems work on are “a set of rules” and “a set of facts”. Any basic model can be devised based on these two.

When are Rule-Based AI Models Put to Use?

  • When you simply don’t prefer using machine learning
  • When there is more probability of error
  • When you require quick output

What Is Machine Learning?

This system puts in place its own set of rules, based on data outputs. A system that achieves artificial intelligence by way of machine deep learning is called a learning model. And, in fact, ML is an alternative to some challenges that rule-based AI systems offer. The approach here is probabilistic.

When are Machine Learning models put to use?

  • When you prefer and have the capability of pure coding processing
  • When simple guidelines do not apply
While the definitions may sound all too similar to someone who is not initiated into these models as an expert in AI services or Machine Learning development, here are some of the key differences that will make these differences clear.

Rule-Based AI:

  • Deterministic in Nature: RUle-based AI models are not made to evolve and adapt with the training information streams.
  • Models are not scalable
  • These can operate on basic data & simple information.
  • These systems are immutable objects, and thus do not change over the time

Machine Learning :

  • Probabilistic in Nature: Production constantly evolves and adapts as per training information streams because of statistical rules.
  • These models offer easy scalability.
  • Compared to rule-based AI, Machine learning models need more than just basic data. The data needs to be in detail.
  • These models are mutable, implying that businesses can transform the data with the support of mutable coding languages.


While both models are developed by AI Service Vendors in India and world over, both have their pros and cons. While these models are inherently different, one can only be preferred over the other depending on the situation of a business in regards to which approach suits the development of business better. A remarkable number of businesses that are just beginning to explore the use of technologies in business opt for rule-based AI because of its simplicity in development and execution. Other businesses, that are ahead on the curve as well as data, may go for machine learning development. However, as data increases manifold each passing day, the world would be better with probabilistic rather than deterministic models.




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