Automated machine learning (AutoML) is a technical term used to describe organizations’ modern approach towards machine learning and data science. Machine learning has several applications that allow organizations to find efficient solutions to complex business problems quickly. Here we have presented you with a wiki machine learning guide that discusses different aspects of this revolutionary technology and how it is changing our world.
Applications of AutoML
If you have used an application to book a taxi, you have been part of automated machine learning. The application is designed to automatically detect your location and determine your journeys like home to office or vice versa. It also stores your travel history and travel patterns and gives you suggestions when you use the application. The taxi booking application uses historical trip data as a topic for machine learning. The use of machine learning in application results in 26% exactness in drops and pickups.
The wiki machine learning guide lists the self-driving car as an evolving technology-based in AutoML. Many technology giants like Google, Tesla are testing their self-driving vehicle’s systems. The software and hardware are based on Artificial intelligence that is dependent on an unsupervised learning algorithm. The artificial framework in self-driving cars requires an uninterrupted stream of information to make real-time decisions based on complex situations.
Credit card fraud is a big issue for financial service providers resulting in a loss of $32 billion in 2020. Automated machine learning can help in fraud detection and help prevent losses. Due to the availability of multiple payment channels, people committing fraud are always improving their tactics to avoid detection. AutoML can create predictive models for fraud detection much faster. Organizations can deploy these models quickly on their platforms. Based on the predictive model, they can make their processes more user-centric to prevent fraud.
Advantages of AutoML
- Since all stages of machine learning are automated, it needs a low level of expertise.
- Labor efficient and cost-effective
- Library installation is easy
Understanding AutoML Pipeline
AutoML pipeline refers to applying machine learning technology to solve business problems. The process automates all aspects, starting from obtaining data from various sources and creating a customized machine learning model for your organization.
The traditional machine learning model requires expertise and human intervention at multiple stages. For example, data collection/feeding, processing, and creating prediction models. This limited machine learning applications and required tech-savvy people for implementation.
The arrival of AutoML has changed the ground scenario. The AutoML pipeline would allow non-technical users to benefit from technology without delving deep into its know-how.
Role of AutoML in the future
AutoML is an evolving technology that will change the way you do business. It is expected to solve business problems at scale by creating AI algorithms that learn new things over time. The combination of data science and automated machine learning would allow businesses to streamline decision making. As the technology evolves, automated machine learning would help organizations gather and analyze data quickly and respond efficiently to manage resources.
To sum up, automated machine learning is a promising technology expected to make business processes more efficient by focusing on data-driven insights.