Predictive Analytics with AutoML

Data scientists traditionally need advanced statistics, mathematics, computer science, and business knowledge. That rare combination of expertise was necessary to create machine learning models that could achieve real business goals. However, the rise of AutoML – automated machine learning – has made predictive modeling available to more data professionals and business leaders.

What is Machine Learning?

Before diving into AutoML, it’s vital to grasp the essentials of machine learning. Machine learning encompasses a variety of mathematical techniques for “training” a computer. The computer “learns” to recognize meaningful patterns in complex datasets. Those datasets can take many forms, from numeric tabular data (like a spreadsheet) to images and text. By identifying notable patterns in its training data, a machine learning model can look at new data it hasn’t seen before and generate a prediction about an outcome you choose for it.

For example, you can train a model using customer data including both customers who have churned and those who haven’t churned. Then, the model can look at other data where you may not know customers’ status. Finally, the model can predict how likely each customer will be to churn.

Many standard data-driven tools you use today — such as business forecasts, facial recognition, and chatbots — are built upon machine learning models. Machine learning also underlies predictive analytics, which many businesses are adopting to get foresight about critical business decisions.

What is AutoML?

Building machine learning models no longer requires complex hand-crafted code written from scratch for every new project. Instead, researchers have designed automated ways to construct these models far more quickly — and as accurately — as in “traditional” data science. As a result, those automated approaches are much more widely available and well-regarded by data experts.

AutoML typically includes automation of:

  • Data preparation: cleaning and combining data to get it into the appropriate format for machine learning
  • Feature engineering and selection: determining the correct variables, plus new aggregations or combinations of variables, that will work best in modeling
  • Algorithm selection: identifying the mathematical technique (model) that is best suited to the data and the outcome to predict
  • Model evaluation: testing models on data they haven’t seen before in the training process to see how they will perform
  • Model tuning: finding the optimal configuration of “parameters” (think of them as “settings”) for the model to help it generate better predictions, based on your performance metric of interest, such as accuracy
  • Model selection: comparing the performance of different models with different parameters, then choosing the one generating the best results for a specific business need
  • Model deployment and monitoring: integrating the model into active business processes on current data, then checking its performance regularly to ensure it continues to return value and making adjustments as needed

How is AutoML being used?

Many data scientists are finding AutoML helpful in accelerating their daily work. For example, data scientists can use numerous Python libraries to automate different tasks in the modeling process.

In addition to using AutoML libraries, data scientists, data analysts, and business teams are increasingly turning to AutoML-powered platforms. That’s because writing brand-new code by hand for every new project is inefficient and time-consuming.

Instead, AutoML platforms give data professionals a customizable, flexible head start on their ML tasks. AutoML platforms automatically perform many of the most tedious parts of predictive modeling projects. Moreover, these platforms have proven their ability to perform as well or better than hand-crafted models.

How does AutoML benefit businesses?

While all AutoML accelerates data science projects, AutoML platforms can be advantageous for businesses. This advantage largely stems from AutoML platforms allowing a wider range of data and business professionals to use data science methods.

In this situation, data scientists’ workload can shift toward more complex tasks that require advanced computational skills, while AutoML and other data professionals capably handle routine ML projects. In addition, this shift means it may not be necessary to hire additional staff dedicated to data science — which is valuable, given that these experts are scarce and expensive.

Furthermore, AutoML’s speed and reliability mean that businesses can achieve a much faster deployment of models than traditional data science projects. That rapid deployment means they can start seeing business results sooner — in weeks instead of months or quarters. Overall, the ROI of AutoML can be quicker and greater, thanks to faster implementation and potentially lower cost.

Explore how automated machine learning can accelerate your team’s ROI from ML. We have a guide to choosing the ideal predictive strategy for your team. When you’re ready to move forward, get in touch to learn more about how Pecan’s AutoML platform offers accessible AI for real business impact.

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