Vertex AI & Vectice - Instant Documentation for all your stakeholders

In this article, we’ll show how to:

  • Overcome the documentation challenges by leveraging Vectice automation on top of Vertex.
  • Continuously document Vertex experiments with a line of code from the Vectice Python library.
  • Generate plain English documentation for your stakeholders in the Vectice Web App

Vectice Auto-Documentation

Vectice seamlessly integrates with Google's Vertex AI platform to automatically generate documentation for machine learning models and projects with just a few lines of code. This integration enables efficient collaboration and continuous alignment among all stakeholders on both the business and technical side throughout the entire model development lifecycle.

Vectice enables the documentation of the work being done directly from the notebook by leveraging Vertex assets and notes

Vectice auto-generates documentation by leveraging the model metadata of your experiments and datasets in Vertex AI. This enables the generation of plain English documentation and seamless collaboration with stakeholders.

The documentation challenge

Data scientists love building models but don’t want to struggle with reporting and documenting the projects. When building ML products, there are 3 key problems that lack of documentation triggers:

  1. Compromise of the maintainability & upgradability of your models
    Poor documentation compromises long-term model maintainability and upgradability. When retraining or enhancement is needed, the lack of documentation leads to inefficient troubleshooting and improvement efforts.
  2. Kills communication, delaying model-to-production
    Inadequate documentation disrupts effective communication, leading to delays in transitioning models from development to production and hindering seamless collaboration among stakeholders.
  3. Jeopardizes your business impact Documentation is at the core of the communication and the alignment between data science teams and their stakeholders. The lack of clear documentation jeopardizes the business impact of your projects.

Vectice solving the documentation challenges with automation

Vectice fully automates documentation based on customizable templates. You can effectively turn hours of information gathering into an easily digestible format to seamlessly update business teams, project managers, legal teams, or other technical teams. Below is an example of a complete documentation auto-generated by Vectice:

Full documentation view in the Vectice App.

Vectice generates AI-generated plain-English documentation, including smart widgets for your assets, plots, and other graphs, tailored to the audience of your choice. Clear documentation is critical for model development, yet creating it can be labor-intensive. Automating your documentation with Vectice enables you to collaborate with all stakeholders, export and share documentation, and review the work being done.

Automatically generate the first draft of documentation for all stakeholders from your code

By simply pointing Vectice to your Vertex experiments and datasets and adding contextual notes, Vectice automatically generates documentation for your code. This saves you and your stakeholders valuable time while continuously capturing and sharing information.

Identify key experiment in Vertex you want to document

Let’s identify the key experiments in Vertex AI that you want to preserve in your projects to share with your team. Below is the Vertex AI experiments overview and the code used to log a Vertex experiment:

Vertex AI runs overview tab in the Google Console

This enables Vectice to leverage the metadata stored in Vertex AI to document the work being done.

Add notes about your work

Vectice is more than just technical documentation. It serves as a hub for all your model development documentation and data analysis, bringing together stakeholders from diverse domains, including technical, risk, governance, and business.

With Vectice, you can easily share notes about your work directly from your notebook:

You can share your thoughts and insights with stakeholders in just a single line of code. Below are the notes we documented in the Vectice:

The notes and assets documented in Vectice

Auto-generate plain English documentation for stakeholders

With all assets identified and documented in Vectice, we can now generate the full project documentation or a report on the current status of your work. With Vectice, you have the ability to customize your documentation to suit your specific audience. The screenshot below shows the different prompts that are customized to each of our stakeholders:

Prompt library in Vectice

This simplifies communicating with various stakeholders, including those in technical roles, legal, model risk management, and business teams.

Transforming Model documentation with Vectice and Vertex AI

In this blog post, we’ve showcased the synergy between Vectice and Vertex AI, to revolutionize the model development documentation process.

Vertex AI ML platform provides foundational and best-in-class features to data science teams to streamline ML workflows, including efficient model deployment, management, and advanced tools like built-in model monitoring. In addition, Vectice's user-friendly interface fosters collaboration among technical teams, legal, compliance, and business units, promoting transparency and cooperation within the organization for AI projects.

Key benefits of documenting in Vectice while using Vertex AI for model development are:

  • Accelerated delivery of data analysis and model to production - 25% faster than similar platforms.
  • Enhanced model upgradability & maintainability in an ever-evolving environment.
  • Cohesive team dynamic and alignment for sustained impact

By leveraging the combined strengths of Vectice and Vertex AI best-in-class ML platform, teams can realize a more collaborative and effective approach to AI projects, driving forward both the efficiency of the model development process and the strategic application of AI within their organizations.

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