6 Steps towards a Successful Machine Learning Project | by Shashank Kapadia | Aug, 2022
from a proof of concept to production and in between
The journey of a machine learning project is a complex process. It requires a thorough understanding of data science and statistical techniques and the ability to work with research, engineering, and product teams to deliver a stellar machine learning-driven product.
In this article, we’ll explore and establish a basic framework for delivering a machine learning project.
- Project Initiation
- Data Exploration
- Data Processing
- Model Development
- Model Evaluation
- Model Deployment
1. Project Initiation: Idea, Requirements, and Data Acquisition
The first step to successfully making a machine learning project is to understand the problem, solve it, and produce an outcome that meets your needs.
Before starting your project, you must understand the problem, data, and context. You also need to know the goal and how it aligns with what’s possible using machine learning techniques.
For example, suppose you want a customer to return for another purchase at the store. You wish first to understand the general purchase behavior of customers at the store. Also, to develop a model, you need to understand the type of user-on available — age, spending habits, location, etc. If sufficient, good-quality data is available, then a machine learning model could be developed; however, if no such user-level information is available — despite the problem being well suited for machine learning use-cases — it is challenging to build a model.
Not all data points are often available in the format or database accessible for model development. You must also align how data will be made available for downstream tasks at this.
2. Data Exploration
Data exploration is examining data to identify patterns and make sense of them in the context of your problem. This is often called a “true data science” stage because it’s where you get down to business by looking at the raw facts and figures without any preconceived notions about what they might mean.
The step involves looking at the available data in different ways — for example, by adding new variables or changing existing ones — and then seeing if there are any interesting relationships between those variables. For example:
- Is there a correlation between age and salary for men? If so (and it might), how does that affect women who work at similar companies?
- What happens when you compare one variable with another? Do they have any effect on each other at all?
It is a crucial part of the model development process. It’s where you get to know your data and decide what questions you want to be answered before conducting a more detailed analysis and developing a model.
3. Data Processing and Feature Selection
Data preprocessing is the process of transforming raw data into a form suitable for analysis and model development. It is one of the most critical steps in determining the success of the final model.
There are several ways to preprocess your data. It may include one or more of the following steps:
- Removing irrelevant features from your dataset
- Filling in missing values
- reducing the size of the dataset and feature set
- Transforming categorical variables into numerical variables (or vice versa)
- Normalizing the data points
4. Model Development
It’s now time to build the model. There are many algorithms and methods available open-source that you may want to choose for your problem — however, it is often wise to start with simple and then reiterate.
While choosing the algorithm, you may want to consider:
- Data Size: How big is the data? Does it need to be processed quickly or slowly? Does your algorithm requires a lot of data to learn, or can it learn from limited data points?
- Type of problem: What kind of problems can this algorithm address? Are there specific data handling needs for a given algorithm? How well does the model respond to missing data?
- Availability: Are there existing libraries or packages available for a given algorithm?
5. Model Evaluation
Once the model is trained, it is essential to evaluate the model and understand how to interpret the results before deploying it.
One of the methods of evaluating models is through cross-validation. In this process, you train the model on some datasets and then test its performance on a completely different training set before using it on actual data. This helps to ensure that your dataset isn’t biased in any way and helps ensure that your model will work well in practice.
6. Model Deployment
Now that you have your first model ready, the final step is to deploy the model into production. It’s one of the most essential steps in machine learning because it allows you to use your data for real-world applications or even make money off it!
You can choose between two methods for model deployment: manual or automatic. Manual means that someone else has to go through all of this step by step; in contrast, automatic means that everything happens automatically without any human interaction required.
However, there are some drawbacks to each method. For instance, manual deployment is time-consuming and requires more resources than automatic deployment; it also relies heavily on people who may not be experts at creating software applications.
Automatic deployment is much faster and less resource-intensive than manual deployment. In addition, it does not rely on any human interaction whatsoever.
The journey of any machine learning project is a long one and takes time and effort before you realize the expected results. There are nuances to each section, and in future posts, I will cover them in more detail. I hope this post has helped you better frame your next machine learning project.
If you have any questions or comments, please leave them below, and I’ll respond to them at the earliest convenience
from a proof of concept to production and in between
The journey of a machine learning project is a complex process. It requires a thorough understanding of data science and statistical techniques and the ability to work with research, engineering, and product teams to deliver a stellar machine learning-driven product.
In this article, we’ll explore and establish a basic framework for delivering a machine learning project.
- Project Initiation
- Data Exploration
- Data Processing
- Model Development
- Model Evaluation
- Model Deployment
1. Project Initiation: Idea, Requirements, and Data Acquisition
The first step to successfully making a machine learning project is to understand the problem, solve it, and produce an outcome that meets your needs.
Before starting your project, you must understand the problem, data, and context. You also need to know the goal and how it aligns with what’s possible using machine learning techniques.
For example, suppose you want a customer to return for another purchase at the store. You wish first to understand the general purchase behavior of customers at the store. Also, to develop a model, you need to understand the type of user-on available — age, spending habits, location, etc. If sufficient, good-quality data is available, then a machine learning model could be developed; however, if no such user-level information is available — despite the problem being well suited for machine learning use-cases — it is challenging to build a model.
Not all data points are often available in the format or database accessible for model development. You must also align how data will be made available for downstream tasks at this.
2. Data Exploration
Data exploration is examining data to identify patterns and make sense of them in the context of your problem. This is often called a “true data science” stage because it’s where you get down to business by looking at the raw facts and figures without any preconceived notions about what they might mean.
The step involves looking at the available data in different ways — for example, by adding new variables or changing existing ones — and then seeing if there are any interesting relationships between those variables. For example:
- Is there a correlation between age and salary for men? If so (and it might), how does that affect women who work at similar companies?
- What happens when you compare one variable with another? Do they have any effect on each other at all?
It is a crucial part of the model development process. It’s where you get to know your data and decide what questions you want to be answered before conducting a more detailed analysis and developing a model.
3. Data Processing and Feature Selection
Data preprocessing is the process of transforming raw data into a form suitable for analysis and model development. It is one of the most critical steps in determining the success of the final model.
There are several ways to preprocess your data. It may include one or more of the following steps:
- Removing irrelevant features from your dataset
- Filling in missing values
- reducing the size of the dataset and feature set
- Transforming categorical variables into numerical variables (or vice versa)
- Normalizing the data points
4. Model Development
It’s now time to build the model. There are many algorithms and methods available open-source that you may want to choose for your problem — however, it is often wise to start with simple and then reiterate.
While choosing the algorithm, you may want to consider:
- Data Size: How big is the data? Does it need to be processed quickly or slowly? Does your algorithm requires a lot of data to learn, or can it learn from limited data points?
- Type of problem: What kind of problems can this algorithm address? Are there specific data handling needs for a given algorithm? How well does the model respond to missing data?
- Availability: Are there existing libraries or packages available for a given algorithm?
5. Model Evaluation
Once the model is trained, it is essential to evaluate the model and understand how to interpret the results before deploying it.
One of the methods of evaluating models is through cross-validation. In this process, you train the model on some datasets and then test its performance on a completely different training set before using it on actual data. This helps to ensure that your dataset isn’t biased in any way and helps ensure that your model will work well in practice.
6. Model Deployment
Now that you have your first model ready, the final step is to deploy the model into production. It’s one of the most essential steps in machine learning because it allows you to use your data for real-world applications or even make money off it!
You can choose between two methods for model deployment: manual or automatic. Manual means that someone else has to go through all of this step by step; in contrast, automatic means that everything happens automatically without any human interaction required.
However, there are some drawbacks to each method. For instance, manual deployment is time-consuming and requires more resources than automatic deployment; it also relies heavily on people who may not be experts at creating software applications.
Automatic deployment is much faster and less resource-intensive than manual deployment. In addition, it does not rely on any human interaction whatsoever.
The journey of any machine learning project is a long one and takes time and effort before you realize the expected results. There are nuances to each section, and in future posts, I will cover them in more detail. I hope this post has helped you better frame your next machine learning project.
If you have any questions or comments, please leave them below, and I’ll respond to them at the earliest convenience
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