Is AutoML Worth It? The Risks of Deploying Machine Learning Models You Don’t Understand

3 Benefits of Working with Distributed Teams

AI and machine learning rank at the top of the new technologies helping organizations work smarter with a higher rate of efficiency. Still, the lack of IT professionals with relevant AI experience hampers its timely adoption. 

This AI skills gap led to a movement to democratize access to machine learning. The goal involved making the technology available to a wider portion of any business, including employees with little to no technical experience. One of the offshoots of this approach is known as Automated ML or AutoML. While AutoML makes it easier to deploy and train machine learning models without technical expertise, it comes with a few significant risks.

With a goal of successfully adopting AI and machine learning at your business, let’s take a closer look at AutoML. We analyze what makes the technology transformational and whether its associated risks make using it smart for businesses trying to compete in the current economy. In the end, partnering with IT experts with practical experience building and deploying ML models remains the wisest choice for any organization hoping to leverage the promise of AI. 

A Closer Look at AutoML 

When machine learning first made an impact in the business world, companies typically used data scientists in the role of training and interpreting the output of ML models. This approach led to a great demand for the services of these high-end technology professionals. Scarcity typically follows any strong need for tech talent. So not surprisingly, it’s currently difficult to source experienced data scientists, and their salaries continue to increase as a result. 

Since necessity is the mother of invention, this data science and AI skills gap fostered the initial development of AutoML, as noted earlier. Some business technology pundits describe AutoML as a technology using machine learning to generate more accurate machine learning. It allows businesses to develop analysis pipelines able to solve complex business problems like never before. Most importantly, it accomplishes this without a strong need for data scientists and their high salaries. 

While the democratization of machine learning offers many benefits to businesses of all sizes, a variety of critical risks must be considered. As such, any organization looking to implement AutoML must first perform a detailed risk analysis. Failure to do so might result in an adverse effect that garners poor model output, bad publicity, and a potential loss of customers. Let’s look more closely at some of the most important risks encountered when using AutoML.

What Aspects of Machine Learning Are Automatable?

Only certain parts of the machine learning model development and training processes are able to be automated. Other aspects still need the deft touch of an experienced data scientist. Let’s look more closely at what pieces benefit from automation.

Model selection involves choosing the right ML algorithm to solve the underlying business problem. While this process is able to be automated, using a data scientist to make the initial selection of an algorithm remains a wise choice in some scenarios. The amount of data used by the model as well as the data type also influences whether or not automating this function makes sense.

The chosen algorithm also performs feature selection within the model. While this is another automatable process, some initial input from a data scientist is necessary to set the values for the parameters used by the algorithm’s methods. At that point, the algorithm then chooses features based on their importance.

Data scientists also decide on hyperparameter values before a model gets trained. The model is then able to tune those values in an automated fashion during the training process. This is a case where automation speeds up the overall model training process compared to data scientists manually managing hyperparameter selection.

However, some parts of machine learning model development aren’t able to be automated. For example, model evaluation when using unsupervised learning without labels becomes too complex for automation. Since data cleaning requires a human eye, it also remains a poor fit for automation. In fact, most data scientists spend a majority of their time performing this critical task. 

The human touch is also required when formulating the initial problem the machine learning model hopes to solve. It requires significant business domain knowledge in addition to the creativity and problem-solving acumen typical of experienced data scientists. Finally, the data collection process generally requires data scientists, although some of the more repetitive tasks benefit from automation. It ultimately depends on the specific scenario.  

The Risk of Fully Automating The Machine Learning Development Process

Completely removing the human element from the process used for developing and maintaining machine learning models needs to be avoided. While recent advancements in ML models allows them to be trained and modified automatically based on model output and data, this approach includes risks.

When everything goes correctly with an AutoML project it can save a great deal of time and money. When things go wrong or there are significant modifications that need to be made, organizations can run into trouble. Model output might become compromised due to poor data or mistaken assumptions by an automated model. Since they didn’t rely on any machine learning expertise to generate the models, troubleshooting those models becomes very difficult.

Again, disconnecting the human eye from this process remains largely unnecessary. Instead, leverage AutoML to augment ML model maintenance, adding some computational heft to the process. In a similar manner as other business use-cases for AI, data scientists or other technical personnel are now able to focus on value-added tasks, without devoting inordinate attention to some of the more granular aspects of the model maintenance process.

One scenario where using only AutoML to train models is relatively risk free involves solving simple business problems. Companies gain additional boosts in efficiency without worrying about implausible model outcomes due to the AutoML process making the wrong assumption. At this point, the risks involved in more complex machine learning use-cases still require the deft human touch of a data scientist or a software engineer with significant experience in AI. 

Data Scientists and AutoML Provide The Best of Both Worlds 

Thus, leveraging AutoML in tandem with talented professionals experienced in crafting and training ML models offers companies meaningful benefits. AutoML provides the computing power and automation to build, train, and deploy simple models with minimal human interaction. On the other hand, some business situations demand a data scientist with the insight and intuition to design effective algorithms. In short, some scenarios include enough risk that deploying a model without a full understanding of it is the wrong approach.

Using AutoML and machine learning experts together also helps a business get the most from its investment in human talent. As noted earlier, data scientists and those in similar roles accomplish more when their work is augmented by automation. They become able to design and build more effective models when leveraging AutoML in the development process. Avoiding the repetitive tasks performed more efficiently by automation simply makes them better at their job.

In the end, your organization is likely to need both AutoML as well as human machine learning experts, either data scientists or software engineers. This remains the best approach for reaping the full benefits of AI without the inherent risks of deploying models without a proper understanding of their operation. While AutoML by itself provides some advantages to the forward-looking business, as with any other emerging technology innovation, partnering with experienced tech experts is the wiser approach.

Partner With Experts For Your Company’s Machine Learning Voyage 

Ultimately, the Automated machine learning vs. data scientist question gets answered with “both.” Don’t think diving right into AutoML without the help of technology experts puts your business ahead of its competition. You might end up causing more problems for your organization as ML models return inaccurate results, leading you to the wrong answers for important business questions. Partnering with experts in this nascent technology remains the right choice.

When searching for a tech partner for your machine learning journey, look no further than Gigster. The Gigster Talent Network includes hundreds of experts in machine learning and AutoML. We can assemble an expert team and provide the right approach to ensure your first steps into business AI provide your company with the promised benefits. Connect with us to discuss your specific project ideas. 

Share This Post

Share on facebook
Share on linkedin
Share on twitter
Share on email

More To Explore

Blockchain

When is the Ethereum 2.0 Release Date?

ETH 2.0 will address challenges like congestion, scalability, and high fees – which should all be major concerns for businesses building on Ethereum.