A Guide To The End To End Machine Learning Workflow

Machine learning has become one of the most important aspects of modern AI, doing everything from helping segment audiences for business marketing to recommending a movie for you to watch on Netflix.

Whatever it is you want to use machine learning for, it’s important to understand how the machine learning workflow is set up. Here’s an end to end look at the process, so you can see how an AI will gather and use data as needed.

The Three Main Phases Of Machine Learning

In most machine learning software, there will be three main phases they go through to get you the results that you’re looking for. These are:

Data Engineering: Acquiring and preparing data to be used in the model being created.

Machine learning model engineering: Setting up the model, training it to look for the data you need.

Code engineering: This is how you’ll use machine learning in your final product.

Everything you do in the machine learning process will go through these three phases, so it’s important to know how the workflow goes.

Phase One: Collecting Data

“This is potentially the most expensive and time consuming step of the machine learning process. It will depend on where you’re getting your data from.

For example, it may be quicker if you’re using readily available data, such as customer data you already have from sales on a website” says Felix Hunter, a technical writer at Academized.

This step is critically important to the process. If you’re not starting with the right data, or an incomplete set of data, then you’re not going to get the results that you’re looking for. To ensure this step is done correctly, you’ll need to follow these steps to get your data:

Step one: Collect the data you’ll need. This can be done via different frameworks and formats, such as Spark, HDFS, and so on.

You also have the option to use synthetic data generation for this step. The data can come from several different sources, and can be made up of anything from text, to audio files and images. As such, using an API or collaboration app will be the best way to collect it all.

Step two: This is where you’ll engage in data profiling, to get information about the content of it. This should give you metadata, for example min and max values.

At this point you’ll also go through data validation, which scans the dataset looking for user defined errors. It’s vital that you have clean, well defined data. If you do, you’re going to get the best possible results from your machine learning software.

Step three: Now the data needs to be reformatted, and any errors are corrected at this stage.

Step four: Each data point in your data set will be given a specific category, allowing them to be easily sorted. Depending on the system you’re using, you’ll be able to easily view all your data, allowing you pick out all the categories that would work with your machine learning model. This should be easy to do if all data is well labeled.

Step five: All data will then be split up into training, validation and test datasets. These will be used during the machine learning stages to get the machine learning model.

Phase Two: Creating The Model

Now you have your data, you’ll be looking to create the machine learning model. You’ll write and execute machine learning models, in order to get the final machine learning model created and ready for use.

At first, the machine learning algorithm will be used on the training data that was created during the last phase. That will train the machine learning model, so you can start putting the final model together. At this point, you’ll also start fine tuning the hyperparameter.

Once you’ve been through this process, you’ll be able to evaluate and validate the model. You’ll check to see that it meets the objectives that you had for it, before you can use the machine learning model with the end user.

Again, depending on the system you’re using, you’ll be able to see the model work in real time. That will allow you to see what’s working, and what may need retraining.

Now that the model has been validated, it’ll then be tested using the hold backtest dataset. That will allow you to perform a ‘Model Acceptance Test’.

If this works as needed, then the model will be reformatted into a specific format. This could be PMML, PFA, or ONNX, depending on the needs of the end user.

Phase Three: Coding The Model Into An End Product

You now have a fully functioning machine learning model, that you can start using in your product. At this point, it needs to be coded in so it’s part of the product and easily used by the end user you have in mind. This could be either a desktop or a mobile application.

To integrate the machine learning model into the product, you’ll first need to go through model serving. This operation will address the model artifact in a production environment.

Then, you’re ready to go through model performance monitoring. At this point, you’ll be watching the model as it works, working on both live and previously unseen data.

This should show up any prediction deviation from the model’s previous performance. If you’re seeing any deviation, that may be a sign that the model needs to be retrained, as described above.

As you’re working on integration, you should be seeing model performance logging. This is where every request is recorded in the log, which can be checked at a later date.

Once integration is done, the machine learning software is ready to be used in any program that needs it.

That’s a basic overview of the end to end machine learning workflow. You can see what’s needed to put a machine learning system together, how that system is trained to work with the data given, and how it’s integrated with the final product.

Read: How To Use Machine Learning In Your Business?

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