Democratizing ML

Voting. Finance. Information. Drones. Software Development. AR/VR…

This list could literally go on and on with examples of things that have been democratized. 

And to democratize something is to make it accessible to everyone. This is a good thing! 

It’s time for the next frontier... 

So, how will we democratize Machine Learning (ML)? What does that even really mean?

That’s where AutoML comes in. 

Wait, what is AutoML?

Ok, AutoML may not be all that is needed to fully democratize ML, or democratize AI.

But it helps. A lot. It certainly starts us down the path of democratization. 

AutoML automates how machine learning builds models.  

According to Geeks For Geeks, AutoML can be thought of as “an approach in machine learning where the process of building and optimizing machine learning models is automated using software tools and algorithms.” 

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.

It can speed up how we generate insights for our businesses.

All other steps in between are done for you.  It helps to see this visually:  

So, a lot of that “middle” work is automated.

This can be a huge time saver for teams across the board. 

What could implementing AutoML change for your team?

What could they spend more time on that they currently don’t get to? 

The potential impact of this technology is HUGE. Are we ready for it? 

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The future of AutoML is going to have major implications for using business data 😎

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.

Wouldn’t that be amazing?  

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.

Parker just became way more efficient, and has better information to go off of. 

But is AutoML really that easy?

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 a line of business workers. But the future is coming quickly...

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. It’s not totally touchless, at least not right now. 

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. It ensures these insights are truly usable! 

Turns out democratization takes a village…

How Tingono is using AutoML to solve for churn and expansion 

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:

• Defined an appropriate scope. AutoML is most effective when the problem space is narrowly focused. Finding that sweet spot of variables is key. Turns out the optimization of NRR for SaaS businesses is the right amount of focus.
• Identified the right type of data. To make predictions on recurring revenue, we mostly use tabular data like product usage metrics or customer journey data. This is a great match for AutoML.
• Identified the right amount of data. With the explosion of business data, enterprises now have loads and loads of both customer and product data. Sometimes more than they know what to do with. And it so happens that this amount of data is required for an AutoML system to effectively build, test, and iterate its models.
• Shared our data science expertise. Our staff of ML engineers have the knowledge and experience to properly evaluate test results and build data models. So, we used this ability to build a proprietary ML engine. We've made this engine easy to implement and access, especially for those without an in-house data science team. Now you can quickly and easily access data models that help you generate revenue.

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.

It’s a seamless way to improve your business's capabilities.

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!

Or, want to see a demo? Whatever you’re ready for- we're here.

We want to see ML democratized, and we think our solution might just be part of that down the line.