8 Steps in Machine Learning Cycle

Creating a machine learning application or operationalizing a machine learning algorithm is an iterative process. You can’t simply train a model once and leave it alone - data changes, preferences evolve, and competitors will emerge. Therefore, you need to keep your model fresh when it goes into production. While you will not have to do the same level of training that was needed when you built the model, you can’t assume that it will be self-sufficient.

The machine learning cycle is continuous, and choosing the correct machine learning algorithm is just one of the steps. The steps in the machine learning cycle are as follows:

#1. Identify the Data

Identifying the relevant data sources is the first step in the cycle. In addition, as you develop your machine learning algorithm, think about expanding the target data to improve the system.

#2. Prepare Data

Make sure your data is clean, secured, and governed. If you create a machine learning application based on inaccurate data, the application will fail.

#3. Select the ML Algorithm

You may have several machine learning algorithms applicable to your data and business challenge.

#4. Train

You need to train the algorithm to create the model. Depending on the type of data and algorithm, the training process may be supervised, unsupervised, or reinforcement learning.

#5. Evaluate

Evaluate your models to find the best performing algorithm.

#6. Deploy

Machine learning algorithms create models that can be deployed to both cloud and on-premises applications.

#7. Predict

After deployment, start making predictions based on new, incoming data.

#8. Assess Predictions

Assess the validity of your predictions. The information you gather from analyzing the validity of predictions is then fed back into the machine learning cycle to help improve accuracy.

After your model begins to make predictions, start the process over again by assessing the data you’re evaluating. Is all of the data relevant? Are there new data sets that could help improve the accuracy of predictions? By continually improving models and evaluating new approaches you will be able to keep your machine learning-based applications relevant.