Documentation is considered the bane of corporate existence by some. For others, a great documentation system makes their organized minds super happy. In a few instances missing documentation can be a minor inconvenience - in others, missing a digital paper trail and/or knowledge base can have negative economic impact on the business or customer experience. This is the impetus for the question many business leaders are asking today: how do we scale our AI documentation methods and processes?
A secondary question, that may or may not be top of mind - but should be: how do we scale documentation in a way that is enabling for teams across the enterprise?
As with most technology, the adoption of AI and machine learning has been much faster than the implementation of governing practices for AI and ML - including model documentation.
Many organizations today are using one of the “bandaid” solutions below for documenting ML models, controls, and other important information that supports their AI. None of these solutions are sustainable for scaling enterprise AI.
It might sound dramatic, but going beyond the basics with your machine learning documentation can set you on a path toward excellence - while crushing your competition in the meantime.
Below are some of the benefits to using a scalable software solution built for ML documentation.
The problem with using any of the five options above for ML documentation is that those tools are not enabling for users and stakeholders. Plus, they’re not a real system. Enterprises using disjointed documents, spreadsheets, one-off chats, email, or notebooks, are not only putting the business at risk. Undervaluing the importance of ML documentation also slows down teams and cuts productivity across the company (data science, risk management, IT).
Implementing a documentation system can help teams govern AI and ML by enabling the following (at scale):
Using the five tools above for ML documentation and model governance usually means opting for a short-term convenience over a long-term strategy geared towards excellence. When doing so, teams are missing an opportunity to implement a more robust tool that provides:
One main benefit of integrating other applications with your ML documentation is the ability to provide (and easily access) key evidence related to models. For example, integrating tools for data governance, model monitoring, and model serving helps build important proof that best practices are being followed.
Leveraging these integrations also enables automations and workflows, and decreases having to rely on manual input.
Using one of the five tools above prevents teams from creating the connective tissue between ML documentation and other key technologies that relate to and complete documentation for models.
“Better visibility” is a phrase that’s probably used too much in the realm of enterprise software - but it’s mentioned so often because it’s a central business need. Leveraging a software system for model governance and documentation gives teams:
Building models is already hard enough - documentation and visibility into not just the systems, but the thought process and sets of decisions made when building the model are so important. Model building teams deserve purpose-built solutions to enable capturing these key pieces of the journey. Good AI deserves great governance tools.
Early adopters that get model governance right will enable faster processes and better visibility - and ultimately gain a leg up on their competition.
AI Governance is coming. Like most enterprise initiatives, the project of implementing good governance will be what your team makes of it. Taking the time to implement today’s top technology - for ML documentation and beyond - alongside AI strategy best practices will be game-changing for the companies that get it right. With the challenge of governance comes the opportunity to manage bias and risk, streamline processes in order to optimize for excellence, and set your team up to have strong advantages above your competition.
