Performing our day-to-day activities online, we leave a pretty traceable digital footprint, everywhere on the Internet. The digital age made it much easier for companies to harvest large amounts of data from the customers, thus gaining a better understanding of their needs. Besides the in-depth analysis of human behavior for a better understanding of how we buy, companies adopting artificial intelligence and machine learning are also experiencing an increase in efficiency and reduced expenses.
For some time now, these new areas of transformation and digital innovation are within the scope of large organizations. Before automation, machine learning technology was accessible only to those kinds of large organizations with vast resources at their disposal. Also, the bigger the organization is, the larger the amount of data ought to be processed and learned from is generated. That brings us to a situation where, at some point, large organizations need a lot of data specialists, not to mention that smaller businesses are also part of the same demand. Although the demand for data specialists is currently on the rise, right now there are not nearly enough of them, and the ones that are available have to deal with time-consuming activities that can and should be automated. For example, cleaning and organizing data takes around 60% of the total time. Annoying, right? That’s why Automated Machine Learning (AutoML) is an innovation that is supposed to really make a difference and make your life easier while doing data science.
Today, the majority of predictive models are custom-built and not only do they take a lot of time but they are also prone to error and vary substantially in quality. That is why AutoML is not only a productivity tool but also an assurance of quality. And the most interesting part of this innovation is making data science more accessible. Now, thanks to automation, machine learning can be used in every industry to leverage technology previously only available to large organizations. AutoML puts an end to resource-intensive traditional machine learning methods. In times of data experts scarcity, the goal is to improve their productivity so they can focus on more complex problems. That is where AutoML comes in handy to improve ROI in data science initiatives and reduce the amount of time it takes to capture value.
To understand what an AutoML is and why it is an innovation that makes a difference, we will start with naming stages of the machine learning process that are the main targets of automation:
- Data preparation
It is very important to do quality data preparation because if data is missing the algorithm can’t use it and, if it is invalid, it will produce less accurate or misleading model outcomes. The automation of this stage of the machine learning process is important in order to produce more practical and accurate models.
- Feature engineering
Feature engineering is considered to be one of the most valuable techniques of data science because it gives you a deeper understanding of the data. Although it is one of the most challenging, automation in this area empowers it to result in more valuable insights by executing it more efficiently and eliminating human error factors.
- Hyperparameter optimization
Also known as Model Tuning, allows you to customize your models. AutoML ensures a significant improvement in the accuracy level of outcomes and highly valuable insights into your data. This facilitates making the most effective business decisions.
- Model selection
Automation is done by running the same data through several algorithms with hyperparameters set by default and it determines which one can learn best from the data.
Companies are eager to adopt artificial intelligence and machine learning to boost growth by providing a better overall experience for their customers. AutoML allows us to understand and analyze actual phenomena without having our data specialists performing tedious work, and it is here to minimize the error rate during the process. We have already seen innovative and practical uses of this technology but there is certainly more to come. Predictive modeling is most valuable to the top management because it assists them in making informed business decisions based on real-time analytics. After the machine identifies patterns, the C-level can interpret them and recommend a course of action respectively. And now, thanks to this innovation, you may witness the progress in resource allocation while you’re at it.
We are already using recommendation engines big time and although there is a fear of misuse of our data, most of the time it is beneficial for us and makes our lives simpler. Based on our digital footprint, we are targeted with ads of things we are really interested in buying, products that match our previous searches and even more importantly, we have been influenced to raise awareness and actually do something about our health. There’s only one question left to ask - how soon will it become mainstream for every industry to structure their decision-making process around machine learning technology?