# PM's Role in AI/ML Projects ft. Supervised Models

### AI/ML Series: Tech Simplified for PMs and Entrepreneurs

PM’s role in AI/ML projects hasn’t been well-defined in most places. This has happened primarily because so far, we had specialised AI product managers.

With the advent of LLMs, this is going to change. AI is going to seep into every type of product in near future. So if you are a PM or founder, it’s important to build an understanding and have discussion around these. This way when you get a chance to manage AI/ML products, you know how and where to contribute.

In this article, we discuss the types of data science models, go deeper into supervised models, and PM’s role in it.

**Learning AI/ML for PMs**

Learning AI/ML for PMs has to be approached differently because their contribution to AI projects is quite different as compared to data scientists.

Broadly, PMs need to

Understand basic concepts, different types of models and how they work

Know real-life applications of these models so that they can map feasibility of a real-life problem to a AI project

Know PM’s value addition and role in AI/ML projects

Let’s get into supervised models to understand these points better:

**What are Models?**

From a previous post Understanding Large Language Models,

*To understand what a large language model is, we first have to understand what a model is.*

*You may have heard of algorithms if you have done basics of computer science. An algorithm is a set of rules that the computer follows to solve a problem. For example, we can have a set of rules to determine whether a given number n is even or odd, aka algorithm. You can see that algorithm below.*

*Understanding algorithms is prerequisite to understanding models. Many people confuse models with algorithms. Let’s take a machine learning algorithm.*

*In machine learning, every enthusiast starts with linear regression algorithm. It defines the relationship between one independent variable (x) and one dependent variable (y) using linear equation (= a straight line). It is well known that equation for a line is y = mx + c, which can also be written as y=w2+w1x. The mathematical rule that we just defined is an algorithm. Let’s talk about models now.*

*A model is when you determine w1 and w2 in the algorithm based on data you have. Suppose we were trying to find the correlation between the # of hours spent studying and marks obtained on the test. In this case, say we had these data points.*

*0 hours — 0 marks*

*3 hours — 33 marks*

*9 hours — 99 marks*

*We can put the first datapoint (0 hours, 0 marks) in the equation and see that w2=0*

```
0=w2+w1*0
w2 = 0
```

*Let’s put another datapoint (3,33)*

```
33=0+w1*3
w1 = 33/3 = 11
```

*Now, let’s see if y=11x holds true for the third datapoint (9,99)*

`y=11*9 = 99`

*So now we have gotten a model, y=11x using which we can predict things.*

**Machine Learning Process**

The machine learning process can be explained via the diagram below

We have the following steps:

Training data: The training data consists of past data used to train the model

Algorithm: The next step is picking an algorithm depending on the problem we want to solve

Learning/ training: The training data applied to algorithm creates a custom trained model

Results: We test out the trained model to see if it works as per the requirements

Let’s take an example of Supervised Model to understand these 4 steps

**Supervised Models**

A supervised model is the one where we provide labeled training data to create the model.

Here is a good example below where we provide labeled images of apples and oranges as training data.

After we have trained the model, we can then test it with the test images. Note that test images are similar, but not same as training data. If the model can accurately predict the images, we have got a good model :)

**Role of the Product Manager in Supervised Models**

There are two steps where PMs play an important role while working with supervised models:

Training data: Quality of training data determines the effectiveness of a model, PMs can play a good role here. They can use the systems thinking to point out what all parameters should be covered in the training data.

Understanding the effectiveness of the learning model : While measuring how good the model is, PMs can bring external benchmarks. They can also see the critical errors from the user point of view.

**Types of Supervised Models**

Supervised models are of two kind:

Classification: The model tries to classify an input into a category. For example, a particular email classified as spam or not is an example of classification model.

Regression: The model tries to predict a number based on past data. For example, predicting housing prices in an area based on input factors is an example of regression model.

This would be all for this article. In the immediate future articles, we would

Delve deeper into classification and regression

How to test these models

How to measure the efficacy of these models

## Summary

PMs play an important role in data science projects. Albeit the role isn’t sharply defined in most places. To be effective, PMs need to understand basics of data science, different models and their real-life applications, and their role in these projects.

We cover basics of supervised models in the article, and where PMs can play a role in it.

Here's the video of me explaining this in-depth 👇

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