How Does Auto ML Help Ask the Right Business Question?

By Data Science Salon

Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. This success, however, crucially depends on human-machine learning experts to perform data pre-processing and cleaning, model selection, hyperparameter optimization, and model inferences and result analysis. 

The complexity of data management is often beyond the expertise of non-ML professionals. In addition, the growing importance of machine learning applications has created a need for off-the-shelf machine learning approaches that are ready for deployment without expert expertise. 

Automated machine learning, often known as automated machine learning or Auto ML, automates the time-consuming, iterative processes of building machine learning models. It enables data scientists, analysts, and developers to create machine learning models at a large scale, with higher accuracy and productivity, while maintaining model quality.

Changing the Industry Landscape

Auto ML has the potential to enhance data scientists' productivity significantly while democratising machine learning techniques. Auto ML tools and services are becoming increasingly popular as the demand for data scientists grows, allowing businesses to leverage machine learning to extract business insights following a more efficient and scalable approach.

Auto ML for Superpowering Decisions

When we talk of business and the industry, noted theoretical neuroscientist and artificial intelligence expert Dr. Vivienne Ming said, "AI is when you talk to investors, machine learning is when you talk to scientists."

When an algorithm is trying to mimic human abilities by teaching how to tell a dog from a muffin, the same algorithm (with different hyperparameter scores) teaches how a systems machine will benefit machines by being faster, less expensive, and more consistent. Can machine learning improve human abilities, especially in computation where we make decisions, better than what humans can factor in? And in which decisions can we superpower? Here are a few very common use cases:

  • Credit risk- Auto ML algorithms harness data-build credit scoring models to generate real-time predictions and make better decisions that lower credit risk.
  • Predictive maintenance: Auto ML models identify emerging asset failure patterns within equipment data and provide an early warning with sensor-level intelligence. This helps to avert unplanned downtime, meet production goals and inform maintenance teams of the work that needs to be done.
  • Customer churn: Auto ML models create and deploy models and run predictions that transform customer relationship management to a better customer experience. Helping businesses to enhance their retention abilities and expand their client base much faster in a more cost-effective way.
  • Cyberattack: Auto ML models detect any cyber threat, APTs, and ransomware and stamp out cyber threats while bolstering security infrastructure through pattern detection, real-time cyber crime mapping.

The Big Elephant in the Room

Solving the dirty data challenge remains one of the primary objectives of data science professionals before even they begin to build a model. Focusing on this challenge from the bottom up creates a comprehensive use case that questions the business problem at hand. Step one is identifying the key questions behind the business's daily decisions. For instance, how to price a product with an online retailer like Amazon, what factors will drive the price, will it be the demand for that product, and what is the competition going to do? Evaluating the impact of improving the decision comes next. Dirty data or data pulled from different places ultimately affects the ROI. Another is the lack of data science talent. Addressing the dirty data challenge with Auto ML can allow data scientists to become the experts of their domain rather than focusing on data pre-processing and management. 

Applying Auto ML

Leveraging automation into machine learning optimizes hyperparameters, selects metrics to give the best model, which can be constructed of a single algorithm or an ensemble of models. Several open-source tools and some paid options are available in the industry, like Autoscaler open-source libraries. However, some paid options are very specific. For example, Google Cloud Auto ML is very specific for vision. 

Automating Search Parameters in Auto ML

Whatify (Formerly is an Auto ML platform for data science professionals. By automating the entire model building process from start to finish, Whatify gives data scientists the capabilities to create and d

eploy multiple high-scoring and explainable models, all while successfully meeting their organizational business goals. Whatify’s technological edge lies in its holistic approach to the automation process. For each of the hundreds of models trained, a single pipeline of hyperparameters and ensembles are created and simultaneously optimized, significantly improving the models’ overall performance against the business problem. Here is how Whatify’s Auto ML works to address model explainability challenges- 

  • Case study 1: Homeguard Security- This real case is based on rule-based video analytics and explains how Auto ML solved an existing ML model’s inefficiencies in railway track management for the home guard security. 
  • Case Study 2: Cybersecurity- This case study is based on NATE network data to identify cybersecurity attacks.

To Summarize

The impact of Auto ML on the future cannot be underestimated—Auto ML can help us put more things into production without necessarily compromising desired accuracy levels. However, what it cannot do is to learn how to ask the right business questions. It cannot learn how to convey intelligent insights to an organization and make it a reality. And this is where we need really strong data scientists to collaborate in automating applying machine learning to real-world problems, and the possibilities are limitless.


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