Enterprise AI in the Cloud

16Choosing Your AI/ML Algorithms

The only source of knowledge is experience.

—Albert Einstein

Data, as pivotal as it is, still requires algorithms to become actionable and give life to your AI initiatives. Choosing the right AI/ML algorithm is like choosing the right tool for a job. This chapter covers different machine learning algorithms and explores aspects such as how they work, when to use them, and what use cases they can be employed for (see Figure 16.1).

FIGURE 16.1: PROCESS DATA AND MODELING: Choose your AI/ML algorithm

Note that the choice of a machine learning algorithm is not a straightforward process, and the relationship between the algorithms and models is many to many; the same use case can be served by more than one algorithm, and the same algorithm can solve many use cases and business problems.

This chapter explores different categories of machine learning, such as supervised learning, unsupervised learning, and deep learning. You'll discover a plethora of algorithms such as linear regression, decision trees, neural networks, and more.

Along the way, I discuss factors to consider when choosing an algorithm, ensuring that you have a robust methodology to align your choice with the problem at hand. By the end of this chapter, you'll have a firm grasp on how to pick the right algorithm.

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