Datatron Technologies
April 14, 2021
Machine learning isometric banner, artificial intelligence science, computer algorithm. Octopus robot with many hands hold
Machine Learning

Do you have a project idea but you don’t know where to start? Or maybe you have a dataset and want to build a machine learning model, but you’re not sure how to approach it?

In this article, I’m going to talk about a conceptual framework that you can use to approach any machine learning project. This framework is inspired by the theoretical framework and is very similar to all of the variations of the machine learning life cycle that you may see online.

So why is a framework important?

A framework in machine learning is important for a number of reasons:

  • It creates a standardized process to help guide one’s data analysis and modeling

With these points in mind, let’s talk about the framework!

The Machine Learning Life Cycle

While there are many variations of the machine learning life cycle, all of them have four general buckets of steps: planning, data, modeling, and production.

1. Planning

Before you start any machine learning project, there are a number of things that you need to plan. In this case, the term ‘plan’ encompasses a number of tasks. By completing this step, you’ll develop a better understanding of the problem that you’re trying to solve and can make a more informed decision on whether to proceed with the project or not.

Planning includes the following task:

  • State the problem that you are trying to solve. This may seem like an easy step, but you’d be surprised at how often people try to come up with a solution to a problem that doesn’t exist or a problem that isn’t really a problem.

If you complete this step and are confident with the project then you can move to the next step.

2. Data

This step is focused on acquiring, exploring, and cleaning your data. More specifically, it includes the following tasks:

  • Collect and consolidate the data that you specified in the planning phase. If you’re obtaining data from multiple sources, you’ll need to merge the data into a single table.

3. Modeling

Once your data is ready to go, you can move on to building your model. There are three main steps to this:

  • Select your model: The model that you choose ultimately depends on the problem that you are trying to solve. For example, whether it’s a regression or classification problem requires different methods of modeling.

4. Production

The last step is to productionize your model. This step is not talked about as much in courses and online but is essential especially for enterprises. Without this step, you may not be able to get the full value out of your models that you build. There are two main things to consider in this step:

  • Model Deployment: Deploying a machine learning model, known as model deployment, simply means to integrate a machine learning model and integrate it into an existing production environment where it can take in an input and return an output.

And that’s the general layout of the machine learning life cycle.

Here at Datatron, we offer a platform to govern and manage all of your Machine Learning, Artificial Intelligence, and Data Science Models in Production. Additionally, we help you automate, optimize, and accelerate your Machine Learning models to ensure they are running smoothly and efficiently in production — To learn more about our services be sure to Request a Demo.

Follow Datatron on Twitter and LinkedIn!

Thanks for Reading!

<< Back to All Blogs