Careers In AI




From Zero to Hero: Develop and Deploy Machine Learning Models.


Machine learning models depend on data and algorithms to solve real-world problems. A machine learning model is a program (file) that has been trained to detect patterns and trends in data and predict results. Machine learning models are trained to learn from a set of data and once trained, they can be employed to generate predictions over data they have not seen beforehand. Deploying a machine learning model requires placing the machine learning model into an environment where it can function and perform what it was designed to do.

In this article, we will delve into the process of developing and deploying a machine-learning model. There will be an in-depth explanation of several concepts. This article will cover:

  • When to Use a Machine Learning Model
  • Collecting Data 
  • Preprocessing Data 
  • Splitting Data Correctly
  • Selecting the Suitable Model
  • Model Training 
  • Model Evaluation
  • Hyperparameter Tuning/Optimization
  • Prediction (Use Machine Learning model)
  • Deploying Machine Learning Models (introducing your product to the shelf)

When to Use a Machine Learning Model 

Before developing a machine learning model to solve a problem, it is necessary to provide answers to these questions: 

What is/are the performance(s) of non-machine Learning processes?

Is a machine-learning solution necessary at this point?

Should I put my time and money into a machine-learning solution?

Machine learning models can solve complex real-world problems and understanding when to use a Machine Learning Model is important. A machine learning model can be used if the problem at hand requires a prescriptive or predictive solution. The availability of Data in large amounts should also be given consideration, as the Machine Learning model solves complex problems by analyzing substantial amounts of data. But before we can solve a problem with machine learning, it is necessary to define that problem.

Defining the Problem

It is important to note that a machine learning solution assumes that the future would behave just like the past, therefore a machine learning model discovers patterns in the data it was trained with and when given new data, it can predict based on what it has seen before.

Once there is a decision to employ a machine learning model solution, the next thing to resolve is to properly define the problem at hand.

To achieve this, we have to:

  • Identify what the model will predict.
  • Identify the input data and the desired outputs.
  • State the kind of problem we are trying to solve (Clustering or Binary Classification).

1. Data Collection, Data Preparation and Data Pre-processing 

When the problem at hand has been clearly defined, it is time to gather the data that will be used to train a machine learning model. Collecting data is an important aspect of developing machine learning models, good data in large amounts means that the machine learning model we build will do well. 

Data can be collected via:

  • Web Scraping.
  • Surveys with online tools such e.g. Google Forms.
  • Using Public Datasets from organizations.
  • APIs to collect data from social media platforms.

After Data has been collected from a reliable source, it is necessary to ensure the data is relevant to the problem and the data contain a few missing values. In that case, the data can be prepared by:

  • Placing all the data together and cleaning to separate unwanted data, missing values, duplicate data and so on
  • Visualizing the data to understand the structure of the data and identify the relationships between the variables and classes of the data
  • Splitting the data into training and testing sets

2. Model Selection

The choice of a machine learning model depends on the problem at hand. Researchers in the field of machine learning, Machine learning engineers and Scientists have developed different models for different problems. The kind of data collected also influences the choice of a model as some models are suited for numerical data while others are suited for categorical data.

Selecting the right machine learning model for a specific problem requires special deliberation as there are a variety of models to choose from, and each Machine learning Model has its strengths and weaknesses, so it’s vital to perform the research and choose the one that best fits the problem.

3. Model Training 

Once a Machine Learning Model has been selected, it is time to train it using the pre-processed data. Training a model involves feeding the model the data and adjusting the model’s parameters until it can accurately predict the desired outputs.

This is the most crucial stage of developing Machine Learning Models, training the model allows the model to learn from the data (Training Dataset) and find patterns to make predictions when given data (Test Dataset). With time, the machine learning model gets better at predicting desired outputs.

4. Model Evaluation 

After the model has been trained, it is important to evaluate its performance to ensure that it is producing accurate and reliable results and this involves testing the model using new data that has not been used before by the model and comparing the model’s predictions to the actual outcomes.

There are several metrics used for evaluating machine learning models, including:

The accuracy Score measures the proportion of correctly classified instances to the total number of instances.

The precision score measures the proportion of correctly predicted positive instances to the total number of positive instances.

ROC AUC measures the area under the receiver operating characteristic (ROC) curve, which is a plot of the true positive rate against the false positive rate.

A confusion Matrix is a table used to evaluate the performance of a classification model, the table summarizes the predicted and actual class labels.

Mean Squared Error (MSE) is another metric used to evaluate the accuracy of regression models that measures the average squared difference between the predicted and actual values.

5. Hyperparameter Tuning: Model Optimization

If the machine learning model is not performing as expected, it may be necessary to adjust the hyperparameters of the model to optimize its performance. This involves tweaking the model’s variables to find the best combination of parameters/variables for the specific problem. Once the model has been developed and evaluated, it can be improved by hyperparameter tuning.

6. Using the Machine Learning Model

Now, the model is performing well, it is time to put it into use, the model can be used to make predictions or recommendations, and the model can also be used to gain insights into new data. The model can be used on new data to also make accurate predictions.

7. Deploying Machine Learning Models

Finally, we have a model that has been used on new data and a model performing well, it is time to use it on a larger scale by deploying it. Deploying a machine learning model is similar to bringing your finished product to a store where it can be bought for use. 

This involves integrating the model into existing systems or applications so that it can be used by others.

Among the methods of deploying a machine learning model are:

  1. Saving the Machine Learning model as an object (Pickle object in python; RDS object in R)
  2. Deploying the model as a function: by transferring, Machine learning derived rules to a custom function, then applying this function to make predictions.
  3. Web Frameworks: Creating APIs to receive input and make a prediction.
  4. Embedding Machine Learning Model inside a web application.

Final Note

This article presents an overview of building and deploying machine learning models. It is necessary to explore this step-by-step guide in more depth so that the concepts and methods described can be understood better, and applied better to real-world problems. 

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