Machine learning workflows specify which steps of an ML project are implemented. Data gathering, data pre-processing, constructing datasets, model training and refinement, assessment, and deployment to production are all common steps. Some parts of the machine learning operations cycle, such as the model and feature selection phases, can be automated, but not all.

While these measures are widely acknowledged as best practices, there is still potential for improvement. When developing a machine learning process, you must first characterize the project before determining the best strategy.

  • Not attempt to fit the model into a predetermined procedure. Instead, create a flexible approach that allows you to start small and work your way up to a production-ready solution.

Phases of Machine Learning Workflow

The actions taken during a machine learning deployment are defined by machine learning workflows. Machine learning processes differ depending on the project, however, four main steps are usually incorporated.

Data collection for machine learning

One of the most critical steps in machine learning workflows is data collection. The quality of the data you acquire during data collection determines the project’s potential usefulness and accuracy.

To gather information, you must first identify your sources and then combine the information from those sources into a single dataset. This might include streaming data from the Internet of Things sensors, obtaining open-source data sets, or putting together a data lake from various files, logs, and media.

Preparing the data

After you’ve collected your data, you’ll need to pre-process it. Cleaning, and validating data into a useful dataset is what pre-processing entails. This may be a pretty simple operation if you are gathering data from a single source. If you’re combining data from many sources, ensure the data formats match, that the data is equally credible and that any duplicates are removed.

Creating data sets

This stage entails dividing the processed data into three datasets: training, validating, and testing:

  • The training set is what’s utilized to get the algorithm started and educate it on how to analyze data. This set uses parameters to construct model categories.
  • Validation set—used to measure the model’s accuracy. The parameters of the model are fine-tuned using this dataset.
  • The test set is used to evaluate the models’ accuracy and performance. This collection is intended to reveal any model flaws

Refinement and training

You may start training your model after you have the datasets. This entails delivering your training data to your algorithm so that it may learn proper classification parameters and features.

After you’ve finished training, you may use your validation dataset to fine-tune the model. This may entail changing or removing variables, as well as adjusting model-specific settings (hyperparameters) until an acceptable degree of accuracy is achieved.

Evaluation of machine learning

Finally, when you’ve discovered an appropriate collection of hyperparameters and optimized your model’s accuracy, you can put it to the test. Testing makes use of your test dataset to ensure that your models are based on correct characteristics. You may return to training the model to enhance accuracy, alter output parameters, or deploy the model as needed based on the input you get.

Best practices

Establish the scope of the project.

Before you begin, carefully identify your project goals to ensure that your models provide value rather than duplication to a process. Consider the following factors while defining your project:

What is your present procedure? Models are often created to replace an existing procedure. It’s critical to understand how the existing process works, what its aims are, who executes it, and what constitutes success. Understanding these factors will help you determine what functions your model must fulfill, what implementation constraints may exist, and what standards the model must meet or surpass.

What do you want to forecast? Knowing what data to collect and how to train models requires a clear definition of what you want to predict. This process should be as precise as feasible, and results should be quantified. You’ll have a hard time ensuring that all of your objectives are accomplished if they aren’t quantifiable.

What data sources do you have? Consider what data your present process relies on, how it’s acquired, and how much of it there is. You should figure out what exact data kinds and points you’ll need to make forecasts based on those sources.

Machine learning workflows are designed to increase the efficiency and/or accuracy of your present process. You must research and experiment to develop a technique that fulfills this aim.