Voting. Finance. Information. Drones. Software Development. AR/VR…
This list could literally go on and on with examples of things that have been democratized.
So, how will we democratize Machine Learning (ML)? AutoML.
Ok, AutoML may not be all that is needed to fully democratize ML. But it helps. A lot. It certainly starts us down the path of democratization.
AutoML automates how machine learning builds models. It streamlines the time-consuming steps in the ML lifecycle. Specifically, it simplifies data prep, feature engineering, model selection, and model training.
Seems like a lot? Sounds amazing? Yes, it is…
If done correctly, AutoML lets the implementor focus on the first step of acquiring data and the last step of evaluating predictions. All other steps in between are done for you. It helps to see this visually:The potential impact of this technology is HUGE.
First, as stated, it sets us down the path of democratizing ML. The end goal is to empower line of business workers to access the power of AI without requiring data science resources.
In this future world, Parker, our SalesOps Lead with no ML skills, could easily automate lead scoring and prospecting.
You see, Parker has a spreadsheet with multiple years of manually entered customer data. With AutoML, Parker could just upload this spreadsheet and click a button. And voila! Just like that, Parker would have a ready-to-use lead qualification list based on a sophisticated ML model.
Many data scientists argue that AutoML is overhyped. And as of now, it might be.
This is because AutoML currently requires a nuanced understanding that only a trained engineer has. For example, a trained machine learning engineer knows how to optimize a data model. They can easily evaluate test results and select the best way to tune parameters. These are not things one can intuitively deduce.
And for these reasons, AutoML currently is better suited to be a tool to assist data scientists with their development efforts. Meaning, it’s not quite ready for line of business workers.
Also—as a preview for what will likely be a future blog post—AutoML alone is not enough to operationalize machine learning models. Line of business teams will also require help from ML Ops / IT.
These teams will need to deploy and manage things like the model lifecycle and workflow execution tools. This will ensure others on the GTM team are alerted about timely customer insights.
Turns out democratization takes a village…
Of course, every so often the stars align and you find a use case where a promising technology can be put to use right away.
That is the case for how Tingono is leveraging AutoML.
We’re using AutoML so you can easily retain and grow customer accounts. To do this, we’ve:
The result of this effort is your line of business workers are empowered by data models they didn't need to build (or understand). Specifically, this helps your GTM teams scale their efforts to retain and expand revenue.
So, while we’re all waiting for AutoML to democratize ML, there’s an interim solution that helps you drive more revenue. And we’d love to help you implement this. Let’s talk!