Written By: Chet Hayes, Vertosoft CTO

We recently announced the Vertosoft AI LaunchPad to bring together some of the industry’s best and most innovative technologies to create a platform that supports the rapid adoption of machine learning and artificial intelligence across the public sector.  A machine learning (ML) model thrives on the quality and diversity of data it processes, so let’s delve deeper into how the Vertosoft AI LaunchPad helps agencies build and manage quality data pipelines.

Vertosoft AI LaunchPad: Your Catalyst for Transformation

Imagine the AI LaunchPad as your tool of transformation, steering the direction of machine learning within your organization.

With ‘Data Prep’ so important to a successful ML/AI project, we have partnered with SteamSets to provide enterprise-proven DataOps capabilities to government agencies via the Vertosoft AI LaunchPad. StreamSets takes raw data, refines it, and channels it seamlessly into ML models, fostering data-driven, insightful decisions. In an era marked by a deluge of diverse data, StreamSets serves as a critical conduit, enabling a smooth transition from raw, unprocessed data to insightful, actionable information.

Let’s navigate through the fundamental stages that define the data pipeline architecture on the AI LaunchPad:

  1. Data Extraction: The journey begins here. The AI LaunchPad identifies and gathers raw data from numerous sources. This data can be structured, semi-structured, or unstructured, and sourced from various databases, files, or data streams.
  2. Data Transformation: At this stage, the AI LaunchPad tailors the data into a uniform format compatible with ML models. It involves data cleaning, handling missing values, and encoding categorical values, among other tasks.
  3. Data Loading: Next, the transformed data is loaded into a data warehouse or data lake within the platform, providing a well of data ready for analysis and modeling.
  4. Data Analysis and Modeling: Here, data scientists or ML engineers can analyze the data using the AI LaunchPad, selecting the right ML models for their tasks. Subsequent steps include model training, testing, and validation.
  5. Model Deployment and Monitoring: Once the ML model passes validation, it’s launched into the production environment. The AI LaunchPad provides continuous monitoring to assess the model’s performance, allowing for necessary adjustments over time.

Maintaining a Quality Data Stream

Ensuring an unimpeded, quality data stream is critical to the performance of ML models. The AI LaunchPad implements rigorous validation checks, error logs, and data profiling to maintain data quality, securing a steady flow of invaluable insights.

In Conclusion

Comprehending the data pipeline architecture is key to unlocking the potential of your ML models. As data continues to surge in the digital landscape, Vertosoft’s AI LaunchPad functions as a crucial lifeline, powering the creation, deployment, and improvement of your ML models. By mastering the data pipeline intricacies, you can leverage data’s full potential, leading to informed decisions and pioneering solutions.