Machine learning models are powering all AI domains, including computer vision, natural language processing, predictive analytics, autonomous systems, and others. These machine learning models have a huge potential to change the landscape in different industries and sectors with their ability to monitor bank transfers, patient care, academic performance, and much more.
Developing, deploying, and managing a machine learning model follows the same pattern. Still, the development of ML models requires a completely different approach since they are not code-centric; they are data-driven. Machine learning models are derived from data.
The process of developing a machine learning algorithm is a complex one that requires experienced developers and skilled data scientists. Though a person without coding skills and in-depth knowledge of particular technologies cannot build a machine learning model, an understanding of the process is important if you are looking to implement machine learning. This guide acts as a basic introduction to developing a machine learning model.
Seven key steps to building a machine learning model
Although different types of machine learning models may require different approaches to their training, there are some steps that most models have in common. Your project will go through the following steps:
Understanding the business side of the problem
The process of development starts with gathering the business requirements. You need to define what problem you’re trying to solve before developing the solution. This step requires interacting with business people such as business analysts, product/business owners, etc. The questions that need answering are:
- Why do you need to build this solution?
- What are your KPIs?
- What are the success criteria for the project?
- What are the data sources?
- Are there any special requirements for bias, transparency, and explainability?
- What are the technical and business issues that need solving?
- How much will it cost to develop and integrate the model?
Identify and understand the data
When developing a conventional software solution, it’s time to start the development process right after all the requirements are clearly defined. But in the case of machine learning, the second step is all about data.
A machine learning model is developed by learning and generalizing the training data, and then utilizing the acquired knowledge for new data to make accurate predictions and fulfill its purpose. At this stage, you need to understand whether you have data for training or you need to gather it.
If the data is missing, you need to set up a data acquisition process. It can be done through partnering with third-party organizations, searching for public datasets, using a paid API, etc.
And if there is data, you need to estimate the quantity and quality of the existing data sets. Check if the data is properly labeled. It’s of great importance when building supervised machine learning algorithms. The next step is to understand the sources of data and its types (images, videos, text documents, etc.)
Prepare and clean the data
Machine learning algorithms cannot go without large volumes of top-quality training. The model needs to identify the patterns and study the relationships between input and output data from training sets.
As a rule, data scientists run the processes of preparing and cleaning the data. It’s a lengthy and labor-intensive process. Data scientists need to label data only for supervised machine learning models. Unsupervised models need only input variables or features. But for both models, the quality of data should remain high to ensure an accurate algorithm.
Validate the data
At this step, data scientists deal with data accuracy and fill the missing values to ensure the completeness of the trained data. Without validating data, there are high risks of basing decisions on data with imperfections that can lead to lowering the accuracy of the machine learning model. Noise removal and dimensional reduction can help remove correlated and unimportant variables. In case there isn’t enough data for training, data scientists can refer to third parties like open databases to fill the missing values.
Define the type of algorithm
In many cases, machine learning algorithms were designed for specific tasks. For example, classical machine learning models work better on tabular data than neural networks. Choosing the best model is based on the following items:
- Check if data contains labels. It is a key factor if a model is supervised, unsupervised, or semi-supervised
- Type of data (image, text, etc)
- Data dimension. Specific algorithms work better with high-dimensional data
- Check for the State of the Art Approaches (SoTA). Review all the available SoTAs to push new innovations in a promising machine learning direction.
At Unicsoft, we’ll guide you through every step of your development journey to ensure a top-quality machine learning algorithm that satisfies your business needs.
Optimize the machine learning algorithm
Running an optimization process allows you to achieve greater accuracy and efficiency and lower the degree of error. Models can be optimized for specific use cases, tasks, and goals. The process of machine learning optimization consists of reconfiguring the model hyperparameters and assessing these changes.
The model cannot configure hyperparameters on its own. The designer of the model sets them, including the structure of the model, the number of data clusters, and the learning rate. After the optimization, the model will perform every task faster and more effectively.
While optimizing, data scientists strive for interpretability since the process of human interpretation goes far beyond the machine’s ability to think. Machines cannot see beyond the picture and think outside the box. Earlier, the process of hyperparameter optimization was carried out through trial and error. Only now, there are optimization algorithms that can help identify the most effective hyperparameters and configure them.
Deploy a model
Before deployment, machine learning models are tested in an offline or local environment in training and testing datasets. Model deployment is the process of integrating an ML-trained model into an existing live production environment dealing with unseen and new data. During the integration, engineers should focus on the following:
- Be able to measure and monitor the model’s performance
- Understand the different resources (cloud providers) available for productization
- Design testable, version-controlled, and reproducible code
- Release of the REST API endpoint if needed
Unicsoft helps your business create, optimize, and deploy custom machine-learning models. Our top-rated data scientists and developers are here to level up your solutions by building the right machine learning algorithms.
Adopt machine learning in your business and improve its efficiency with Unicsoft. Contact our team to get a free kick-off consultation.