Many organizations are using AI for wide range of applications But AI is not a one size fits all technology. Every AI project is customized to solve a specific business problem with machine learning models. These models which rely on data and algorithms are what address the project needs. For many organizations machine learning model development is new and daunting activity. But some established methodologies help ensure success. Here are the key steps for building a machine learning model.
Identification of Business Problem
The first stage of building a machine learning model is to define the problem and understand it. We need to understand the objective and requirements of project before decoding the problem. Then, we reshape this knowledge into a suitable problem definition for the machine learning project.
Collection of Data
Once the problem definition is defined, we need to start collection of data from various sources. The focus must be on data identification, requirements and quality of data. The quality and quantity of data you get is very important since it directly affects the accuracy and reliability of the model.
Data Preparation
This step is most time consuming process. It takes overall 70 to 80% of the overall project time. It requires more man power. Data preparation task includes data cleansing, data visualization , aggregation , labeling normalization and transformation or any other activity for structured, unstructured and semi-structured data.
Following is the procedure for data preparation:
- Standardization of data
- Replace incorrect data
- Enhance and augment data.
- Enhance data with third party data
- Reduce noise reduction and remove ambiguity
- Split data into training, test and validation set
Model Selection
Once the data is in usable shape and you know the problem you are trying to solve then you can move to the next step. There are several models you can choose from according to your project objectives and the data that you are going to process such as images, videos, audio, text, etc. You can choose from models such as Classification, Prediction, Linear Regression, Clustering or Deep Learning. In the following table, some models are mentioned with their application.
Model Training
In this step, we train our model to learn from the quality data that we have prepared by applying the preprocessing techniques and algorithms. We use various training datasets to train our model using different machine learning techniques. It is a very important and required step so that the model can understand the various patterns, rules and features.
Evaluation
Once our machine learning model is trained on a given dataset, our model is ready to test. In this step, we check the accuracy level of our model by providing the test dataset to it. Model evolution can be considered as quality assurance of machine learning model. Evaluating model performance against requirements determines how the model will work in real world scenarios.
Iterate and Adjust
Iterate the process and make improvements in every subsequent iteration to improve performance and accuracy. You should also consider adjusting the model as real world data or even business requirements may change in unexpected ways. Changes to the model may also create new requirements for deploying models onto new systems or an endpoint.
Prediction and Inference
Now we are ready to use our machine learning model for inferring result in real world scenarios. Models can be deployed on various platforms such as web, mobile, desktop and even on IoT devices.
Conclusion
This was an overview about different stages of building a Machine Learning model. To summarize, we first identify our business requirements. Then we collect and process the data from various sources to be fed into a machine learning model. In the next stage, we select from a variety of models depending on our requirements and type of data. We apply the training data to train the model and then evaluate its performance using the test data. We then optimize and adjust the model to satisfy the changes in the data and business requirements. Finally, our model is deployed for inference or prediction.
References
- Life cycle of Machine Learning — Javatpoint
- 4 Stages of the Machine Learning (ML) Modeling Cycle | LinkedIn
- Machine Learning Life Cycle | Top 8 Stages of Machine Learning Lifecycle
- Machine Learning Concepts Every Data Scientist Should Know | by Aman Kharwal | Becoming Human: Artificial Intelligence Magazine
Credits
This article has been written written by Ms. Megha Atale — member of Data Science team at KC E-Cell. She is currently pursuing a bachelors degree in Information Technology Engineering from K.C. College of Engineering, Thane. Other than being a core member of Data Science team at KC E-Cell, she is also a part of NSS (National Service Scheme) Unit of KCCOE. She is also well versed in web development and digital marketing.