Machine Learning Pipelines

Machine Learning (ML) models depend highly on suitable data to deliver accurate insights and predictions. A machine learning pipeline consists of automated steps that prepare data for machine learning model training and deployment.

Why are Data Pipelines Important for Machine Learning?

To get the most value from investments in machine learning, it is vital to provide the highest quality of data to machine learning models. If low-quality data is used to train a machine learning model, its effectiveness is reduced, resulting in unreliable predictions and missed correlations. Investment in data pipelines increases the quality of the insights that decision-makers rely on, increasing the probability of a positive outcome.

Machine Learning Data Pipeline Steps

The following data pipeline process examples improve data used for machine learning.

Profiling Source Datasets

Source datasets can be analyzed to understand the contents and help decide what tasks are required in the data pipeline. Profiling also provides valuable information such as data volumes, variability, levels of duplication, structure, and content. Some of the statistics that profiling can provide include Min, Max, Mean, Median, Mode, Standard Deviation, Sum, and Variance.

Data Reduction

A machine learning model has to be focused on just relevant data. Outlying values and data that are not relevant can be removed by filtering. If unique records are needed, then duplicates need to be removed. Reducing the volume of data in the data pipeline will improve throughput rates. If the analysis does not report discrete values, data can be grouped into age ranges, for example.

Data Enrichment

Data can be enriched by filling gaps using calculated values or merging datasets. Empty fields can use default or extrapolated values where appropriate.

Formatting Data

In this step, gaps in the data can be addressed. Data can be formatted to make it more consistent by ensuring date formats are consistent, removing leading or trailing spaces and checking the use of any embedded currency symbols.

Data Masking

When dealing with sensitive data, personally identifiable data can be masked or obfuscated to preserve customer anonymity.

Data Loading

The data pipeline usually ends with a data load into a database or distributed file system. Both data loading and access by the machine learning model can be parallelized by portioning the data using a key value or calculated hash value to ensure an even distribution.

Data Pipeline Automation

A data integration such as Actian DataConnect can orchestrate a chain of data pipeline processes with centralized visibility of all pipelines and their schedules. The benefits of an automated data pipeline include the following:

• Better data quality improves the business’s decision-making and enables it to respond more to market conditions and changing customer preferences, improving competitiveness.
• Data engineers are more productive as model training times are reduced.
• Machine learning models deliver more accurate predictions with prepared data.
• Once data is prepared for machine learning, it can also be used for additional analysis projects.
• Once proven, most data preparation tasks are reusable by other data pipelines so that they can be constructed, tested and deployed faster.

Actian and Data Pipelines

The Actian Data Platform makes it easy to automate data preprocessing using its built-in data integration capabilities. Businesses can cost-effectively analyze their operational data using pipeline automation. Organizations can get total value from their available data assets by making it easy to unify, transform, and orchestrate data pipelines. Integration connectors make it easy to integrate and extract data from hundreds of data sources, including streaming data servers.

The Vector columnar database can be loaded with prepared data to deliver high-performance analytics and extract, load and transform (ELT) capabilities.

DataConnect provides an intelligent, low-code integration platform that addresses complex use cases with automated, intuitive, and reusable integrations. DataConnect includes a graphical studio for visually designing data pipelines, mapping data fields and data transformations. Data preparation pipelines can be centrally managed, lowering administration costs.