(Last Updated On: August 23, 2022)

If you desire to create a machine learning model then it is necessary to have a sufficient amount of data. The information may be available in numerous formats and is frequently gathered from a variety of sources. Data preprocessing and cleaning thus become an important component in the machine learning endeavor.

Before we can utilize the machine learning model to create predictions, we must repeat the same preprocessing steps whenever new data points are added to the current data. This turns into a tiresome and drawn-out process! So what can be the ways to form data pipelines for machine learning? We at DoltHub have mentioned steps that you need to do so. 

1. Data Ingestion

Data entry into a data repository is the first step in every machine learning workflow. The crucial component is that data is preserved unedited, enabling everyone to accurately record the original data. You can get data from various resources, one of them is from Pub/Sub. Streaming data from other platforms can also be used. There is a distinct pipeline for every dataset, allowing for simultaneous analysis. Within each pipeline, the data is divided to make use of several servers or processors.

2. Processing the Data

During this time-consuming stage, input data that is not organized must be transformed into data that the models can use. Processing the data is an important part of the data version control applications. A distributed pipeline assesses the quality of the data at this stage, looking for structural discrepancies, inaccurate or missing data points, outliers, anomalies, etc., and fixing any anomalies along the route. The feature engineering process is also a part of this step.

3. Splitting of Data

Based on the precision of its feature prediction, the main goal of a machine learning data pipeline is to deploy a precise estimation of data that it hasn’t been tested on. You must now separate the existing labeled data into training, testing, and validation data subsets to evaluate how the model performs on fresh datasets. The following pipelines in this stage are model training and assessment, both of which should be able to use the data splitting API.

4. Model Training

The whole library of training model algorithms is included in this pipeline, which you can use frequently and independently as necessary. The pipeline procedure requests the necessary training dataset from the API (or service) built at the data splitting stage, and the model building service collects the training configuration information. Once the model, settings, training parameters, and other components have been established, they are stored in a model candidate data repository to be reviewed and used later on in the pipeline. Error endurance, data backups, and redundancy on training segments should all be considered during model training.

5. Model Evaluation

Up until a model effectively resolves the business challenge, this stage evaluates the prediction performance of the stored models using test and validation data subsets. When comparing forecasts on the evaluation dataset with actual values, the model assessment step employs several factors. Once a model is prepared for deployment, a notification service is a broadcast, and the pipeline selects the “best” model from the assessment sample to forecast future cases. The quality metrics of a model are provided by a library of numerous evaluators, and they are stored in the data repository next to the model.

6. Model Utilization 

The pipeline picks the best model after the model evaluation is finished and distributes it. The pipeline services keep on working on new forecasting requests while dispatching a new model. The pipeline can utilize different machine learning models to ensure a seamless transition between old and new models.

7. Performance Monitoring

Model monitoring and performance scoring is the last step in a machine learning pipeline. To gradually improve the model behavior, this stage involves monitoring and evaluating it frequently. Based on the feature values imported by earlier phases, models are utilized for scoring. The Performance Monitoring Service receives a message whenever a new forecast is made, performs the performance evaluation, logs the results, and raises the required warnings. It contrasts the scoring with the outcomes that were seen once the data pipeline produced them during the evaluation.

Conclusion

Before beginning any activity, having a clear structure in place typically aids in its efficient completion. And this still holds when creating a machine learning model. Once a model has been created using a dataset, it is simple to design a structured machine learning pipeline by decomposing the steps.