The machine learning lifecycle is a process in data science that machine learning and artificial intelligence projects follow. It outlines the steps necessary to develop and maintain machine learning models to maximize business value.
Here is an example of the machine learning lifecycle:
The first step in the machine learning lifecycle is to identify the goal of the project. Do you wish to increase customer loyalty, or have a better understanding of what your consumers like?
Clearly defining this goal will help you determine the appropriate features to use for the algorithm and what the target variable should be. The clearer the objective is, the more accurate your model will be.
The next part of the lifecycle involves collecting and preparing the data to be used by the machine learning model. When looking at your target variable, you must decide what data needs to be collected to properly train the algorithm to predict information based on new inputs.
Once you build the data set, it is time to determine the target variable and perform feature selection. This will allow your model to train on the data and detect the underlying trends and patterns.
Now we have entered the deployment phase, where the machine learning model is ready to be used. Interpreting the results from the models is one of the most challenging aspects of data science and the machine learning lifecycle. Still, it is essential to understand why the algorithm made the predictions it did.
In this phase, you must also communicate the results to leaders in your organization to use those insights to make data-driven decisions. Similarly, the inner workings of the algorithms must be explained to regulators to ensure that you are compliant with applicable laws.
The final stage of the machine learning lifecycle is one that never ends – implementing, documenting, and maintaining the data science project.
Your business is continuously evolving, so you must ensure that your model remains relevant and up to date with new data. Likewise, you must implement the knowledge gained from artificial intelligence and adapt your business processes accordingly.
The machine learning lifecycle is a critical way to outline the role of machine learning models within your business, as well as the personnel involved in completing each step.
It gives a high-level overview of how the models will work from construction to deployment and can serve as a rubric for implementing them within your organization.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage the machine learning lifecycle or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from the machine learning lifecycle.
The LogicPlum automated machine learning platform was built with the machine learning lifecycle in mind – we aim to simplify the most challenging and time-consuming aspects of this process to focus on gaining insights from the models and applying them to your business.
We offer built-in tools to increase the interpretability of your models, such as model blueprints and prediction explanations. Our goal is to help you have a seamless data science process!
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