AI Vs Machine Learning

Rule-Based AI Vs. Machine Learning: Which One Is Best For Your Enterprise

Dashrath Singh- April 14, 2021

Artificial intelligence (AI) initiatives, ranging from big data to robotics, are being explored and used by businesses across industries to automate corporate operations, improve customer experience, and innovate product creation. Various aspects of the global sector have been altered by AI. Other significant AI features that are gaining a lot of recognition in the digital era are likely rule-based systems and machine learning solutions.

To help you understand the distinctions and optimum usage, this article compares the primary ways for developing these decisioning engines – Machine Learning technology vs more classic rules-based automation technology.

What is machine learning?

A learning model is a system that achieves artificial intelligence through deep machine learning. The machine learning system creates its own set of rules based on the data it receives. It’s a different approach to dealing with some of the issues that rule-based systems have.

ML systems use only the outputs from the data or experts. A probabilistic method is used in machine learning systems. The ml certification provides hands-on experience with massive datasets.

What is rule-based Artificial Intelligence?

Rule-based AI systems are artificial intelligence systems that use a rule-based paradigm to achieve their goals. A rule-based artificial intelligence generates pre-determined outcomes based on a set of human-coded rules.

These systems are simple artificial intelligence models that make use of the if-then coding rule. A set of rules and facts are the two main components of rule-based artificial intelligence models. These 2 elements can be combined to form a basic artificial intelligence model.


Machine learning vs. Rule-based AI

The following are the fundamental differences between rule-based artificial intelligence and machine learning systems:

  1. Rule-based AI models are deterministic, whereas machine learning systems are probabilistic. Machine learning systems are constantly evolving, developing, and adapting their output to training data streams. Statistical rules, rather than a deterministic method, are used in machine learning models.
  2. The project scale is another significant distinction between machine learning and rule-based systems. Artificial intelligence developer models based on rules are not scalable. Machine learning  on the other hand, can be scaled.
  3. When compared to rule-based models, machine learning systems require more data. Rule-based AI models may work with very primary data and knowledge. Machine learning systems, on the other hand, require complete demographic data.
  4. Rules-based artificial intelligence systems are immutable objects. Whereas , Machine learning are mutable objects that enable organizations to alter data or value using mutable coding languages such as Java.


When should machine learning models be used?

  • Processing of pure code
  • The rapid rate of change
  • Simple rules do not apply.

When should rule-based models be used?

  • Not planning for machine learning
  • Danger of error
  • Speedy outputs

Applying Machine learning and Rule-based systems

The decision to use rule-based vs. machine learning systems is based on the needs of the company.

Machine learning is designed to deal with complicated and intense problems in an unstable environment, whereas a rule-based AI system avoids the complexities of black box training.

Machine learning necessitates a high level of dedication since, to be effectively trained, the algorithm necessitates  large amounts of data and hundreds of thousands of records, to produce accurate predictions.

Rules-based systems work best when there is a little amount of data, and the rules are straightforward. Many organizations utilize rules-based systems that define the cash thresholds that require management approval at various levels for spending approvals.



Machine learning and rule-based models each have their own set of benefits and drawbacks. Which technique is most appropriate for business development depends entirely on the situation. To understand and explore the business, many business projects start with a rule or excerpt-based models.

Aside from the aforementioned aspects, there are a slew of additions to consider before settling on a viable AI application model. As a result, consulting with a professional who can best understand your business demands is the ideal option.

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