Machine learning (ML) is postively impacting businesses across nearly every industry resulting in greater efficiencies, better experiences, and more revenue. Examples of machine learning can be found throughout our daily lives including automated email replies, search engine results filtering, commute and flight time estimates, fraud detection and prevention alerts, and content recommendations.
The challenge with machine learning is that it can be time-consuming and cumbersome to implement, often taking many months or even years to deploy and show results. Each new machine learning initiative involves the same rigorous path from data preparation, through training, until actual deployment with few efficiencies gained from prior efforts. In addition, extensive technical expertise – requiring seasoned data scientists, analysts, and developers – is needed which can deplete an organization’s available resources and directly impact ROI.
Enter Automated machine learning.
Automated machine learning (AutoML) is a process that automatically performs many of the time-consuming and repetitive tasks involved in model development. It was developed to increase the productivity of data scientists, analysts, and developers and to make machine learning more accessible to those with less expertise.
The emergence of easy-to-use AutoML platforms like LityxIQ have helped organizations across nearly every industry realize the full potential of automated machine learning. Benefits of AutoML include:
- Building ML models that can more rapidly scale, provide greater efficiency, and productivity while maintaining model quality thus speeding up the machine learning queue.
- Allowing the introduction of prediction into an automated task such as predicting when an action should be taken (e.g., sending a reminder or offer, or specific content type).
- Much improved model accuracy and associated insights by reducing opportunities for bias or error based on best practices established by experts.
- Promoting transparency and the incorporation of ModelOps to streamline production and reduce the time required to train and deploy.
Gary Robinson, COO of Lityx adds, “By leveraging a powerful AutoML platform like LityxIQ, a user can establish, and subsequently other users can follow, a structured and specific set of steps and approaches that help ensure the creation of robust strong models. This means that models are not just easier but are better. For example, LityxIQ includes options like hyper-parametrization where you can tune the hyperparameters of a model to achieve the most accurate predictions.”
User friendly AutoML helps get the whole team involved.
Perhaps one of the most important effects of the advent of AutoML platforms is that they enable organizations to significantly reduce the staff resources and skill levels required to train and implement machine learning models. This is good news for organizations that struggle with finding and retaining data scientists, developers, and senior analysts.
“AutoML platforms enable businesses to implement machine learning solutions with greater ease by automating the essential tasks necessary to develop and deploy machine learning models. This allows a wider-range of analysts and roles within an organization to implement machine learning solutions while allowing more experienced data scientists to focus on even more complex challenges.”
Paul Maiste, CEO of Lityx LLC
Model development expertise requirements are substantially reduced with AutoML, which lowers the barriers to entry for industries and organization that were not able to leverage machine learning in the past. The result is greater innovation across more industries that ultimately strengthens and raises the level of competition for more organizations.