Why having a unified tool for data management is important?

[Padmaja_V]

Data pipelines are the foundational blocks of any data management tool and are pivotal for any ML workflow. Machine Learning models are only as good as its data.

With the growing number of smart devices, the variety of primary data sources & hence the complexity to homogenize and preparing ‘machine-learning-ready’ data increases drastically. Further, data at rest quickly obsoletes the ML model, which necessitates continuous ingestion of new data.

Unstructured data comes in various formats such text, images, videos, and sensor data, posing challenges in processing and analysis compared to structured data.

The volume of unstructured data generated is also enormous, which makes it further difficult to store, process, and analyze using traditional methods. Unstructured data comes with more noise, irrelevant information, especially in text data from sources like social media or web scraping.

Integrating unstructured data with structured data sources and existing systems poses integration challenges. Unstructured data needs more processing & refinement before they can be integrated to existing systems/applications.

Can we have a tool which takes care of above challenges?

We need to look at the below points to understand bit more in detail.

Monitoring & logging:

The data pipeline tools provide built-in monitoring and logging capabilities, allowing data scientist to track the execution of pipelines and identify any issues or bottlenecks. In manual script execution, if there are multiple pipelines running at a time, it gets tedious to track all the jobs to completion.

In tools, when there are multiple jobs run, all the jobs come up with automatic timestamp, lineage such as origin of the data source, where is it being stored, the calculated fields etc.

Version control & tracking:

The data pipeline tools support version control of pipelines. If the current version does not fetch favorable ML results, the version can be reverted in the tool.

Data collection & processing:

For collecting data from an API, a simple python script is sufficient. Is it?

But what happens when the number of sources is a mix of various endpoints and databases? What happens when the data collection frequency of these is also different? Each set of sources would need different transformation steps & sequences.

MLDM’s console comes with Directed Acyclic Graphs to define tasks based on the required transformations and sequences. These tasks can also be reused across data sources.

MLDM also processes data incrementally where only the data changes are processed. It saves time & resources significantly.

Collaborative & teamwork:

By centralizing project artifacts in a shared repository like Git facilitates seamless collaboration, enables team members to coordinate their efforts. Different personas can work together collaboratively from data extraction to processing to model building & tracking effectively.

A centralized tool will help understand the changes by various team members, timeline and issue tracking in a structured manner.

The HPE Machine Learning Data Management (MLDM) software solves a variety of machine learning and large-scale data transformation use cases, such as natural language processing (NLP), image/video processing, genomics analysis, Internet of Things (IoT) stream processing, and risk analysis.

Key differentiators of Machine Learning Data Management

If you’d like to know more about MLDM, please visit the link https://www.hpe.com/us/en/hpe-machine-learning-data-management-software.html

Padmaja Vaduguru