Data Pipelines For Machine Learning

Data pipelines are essential for machine learning projects as they help to manage the flow of data from various sources, ensure data quality, and automate the process of data preparation.

What’s the end goal for a data pipeline? The resulting clean and processed data may be used for analysis, business intelligence and reporting. Another common use these days is as the input for Machine Learning (ML).

While data pipelines are often hand-coded in high level programming languages such as Java, there are plenty of configurable (point-and-click) tools available to do this task. One benefit of using such tools is that many of them have built-in components for completing the ML tasks of feature engineering, training, evaluating and deploying ML models. In this blog post, I will compare and contrast SAS, KNIME, Alteryx, and RapidMiner, which I have used extensively.

SAS Viya #

SAS is a powerful analytics and business intelligence tool that offers a range of features for data analysis, reporting, and data visualization. SAS provides a range of tools for building data pipelines, including SAS Viya, which is a visual design tool for building and managing ML data pipelines that can handle a wide range of data sources, transformations, and data quality checks. SAS provides its own proprietary programming language for fine grained control. On the downside, SAS is a very high-end proprietary system that comes with a fairly hefty licensing cost. SAS is required in some highly regulated sectors such as the finance and pharmaceutical industries.

KNIME #

KNIME is an open-source data analytics platform with a free tier, and a paid for enterprise tier. KNIME allows users to build and execute data pipelines for a wide range of data processing and analysis tasks. KNIME provides a visual interface for designing and building data pipelines, and it supports a wide range of data sources and formats. KNIME also provides a range of data processing and machine learning tools, making it a popular choice for data scientists and analysts.

Alteryx #

Alteryx is a data integration and analytics platform that provides a range of tools for data preparation, blending, and analysis. Alteryx provides a visual interface for building data pipelines, and it supports a wide range of data sources and formats. Alteryx also provides a range of pre-built machine learning models that can be used to perform tasks such as regression analysis, classification, and clustering. Alteryx is well-known for its ease of use, and is a good choice for analysts who need to quickly build and deploy models.

RapidMiner #

RapidMiner is an open-source data science platform that allows users to build and execute data pipelines for a wide range of data processing and analysis tasks. RapidMiner provides a visual interface for designing and building data pipelines, and it supports a wide range of data sources and formats. RapidMiner is particularly popular among data scientists and analysts because of its focus on predictive analytics and machine learning.

Conclusion #

Data pipelines are essential for machine learning projects, and there are many tools available for building them that do not require code. All the above tools have the features needed to create complete pipelines, train, evaluate and deploy complete models. The choice of tool can be a matter of preference, so if project costs are an issue, the open source tools with free tiers (KNIME, RapidMiner) are ideal. Alteryx probably has the best ease of use. SAS may be stipulated by industry regulators but in these cases, you’re probably in a large multinational company setting that is well-disposed to swallow the licensing costs.