Deployment Risk: AutoML & Machine Learning Without Expertise

Data analysts, industry leaders, and business users will collectively agree on one thing: The democratization of data and insights is crucial today. A recent Google Cloud/Harvard Business Review paper confirmed this, with 97% of professionals surveyed agreeing that business success relies on democratizing access to data across organizations.

AutoML is a critical step toward this goal as it makes it easy to deploy an ML model with less technical expertise. While this opens many doors, business leaders are slowly starting to realize the risks and limitations of deploying a technology without first knowing how it works.

I’ve had the pleasure of working with many teams in my journey as a serial entrepreneur, and I cannot emphasize the power of knowledge enough. In this article I share my experience gleaned in working for organizations across industry sectors, and I cover what exactly AutoML is (and is not), the value data scientists bring to the table, and best practices on how to use AutoML to kickstart projects within your business.

What AutoML Is and Isn’t

Here’s what Automated Machine Learning (AutoML) is in plain technical terms: it automates the selection, composition, and parameterization of ML models. Simply put, AutoML provides you with the methods and processes to accelerate your research and make predictions. The rapid explosion in demand for AI-backed projects combined with a lack of experts in the field meant that the complex tasks had to be left to automation. However, AutoML is not a one-stop shop for guiding a model’s performance, nor can it be used to analyze the findings from the collected data.

One example of the limitations of AutoML is a hill climbing algorithm, where a model is tasked with finding the global optimal result or solution. An AutoML model will often only run until it reaches the peak of the first “hill” — the local maximum. While hill climbing seems like you have found the solution, a data scientist would know that you might not be on the largest hill, and as you continue to expand the model it will get less accurate. A trained data scientist can help to quickly expand the model and find the global optimal maximum.

Extensive training and testing stages are what guarantee the long-term viability of a project. The importance of employing technological expertise in association with such projects becomes clear here. The answer?

Data scientists.

The Value Data Scientists Bring to ML

Automation of machine learning began as a project to make things easier for data scientists. Taking the boring and repetitive tasks out allowed the project to proceed at a much faster pace. Minimal human input also meant minimal human error. What’s clear is that AutoML has always been an add-on, not a replacement to a data scientist’s expertise.

There are several key responsibilities a data scientist takes on with every project, from formulating the problem statement, instructing the algorithm, identifying feature variable correlations, to interpreting the final model’s output.

Data scientists can also draw on past experiences to help guide ML models. Understanding what’s worked best in the past helps data scientists make efficient and intuitive decisions. Think about it: You never see one scientist in a lab working on solving a problem. Working as a team on various hypotheses helps the company come up with the most efficient solutions, something automation has yet to achieve.

The ability to make intuitive decisions and formulate hypotheses also results in an accurate ML model in a much faster timeframe. AutoML may eventually reach 90%-95% accuracy over many iterations. Data scientists can guide the model to reach this level of accuracy very quickly.

Using AutoML as a Starting Point

Here’s a shocking statistic: Less than 15% of firms have deployed AI capabilities in production, according to an article by Forbes. The truth is, AutoML is a great starting point to get to 90% accuracy with your ML model. Going beyond 95% is the real challenge. Theoretically, an accuracy increment for the model by even one extra data point can translate to millions of dollars in revenue.

We’re seeing more companies that have deployed AutoML in some way seek to improve their models. The added value of data scientists is particularly clear when working with an agency that works with multiple clients and industries. The exposure to different data science problems provides a broader expertise than can be applied to each unique problem.

It takes a combination of data scientists and automation to bring out the best of machine learning. While many companies today collect huge amounts of data every day, converting that into actionable advice is where they get stuck.

Many companies turn to AutoML due to a lack of data science expertise — a result of the IT skills shortage. This is where AutoML can serve as a viable starting point. However, when your company has reached the limit of the model accuracy you can achieve through AutoML, or when you want to achieve rapid results from ML machine learning, expertise is an absolute must.