Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real–world business problems is time–consuming, resource–intensive, and challenging. It requires experts in several disciplines, including data scientists some of the most sought–after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data what is often referred to as the signal in the noise. Automated machine learning incorporates machine learning best practices from top–ranked data scientists to make data science more accessible across the organization.
Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills which is a lot to ask of one company, let alone one data scientist (provided you can hire and retain one). Not only that, there are countless opportunities for human error and bias, which degrades model accuracy and devalues the insights you might get from the model. Automated machine learning enables organizations to use the baked–in knowledge of data scientists without expending time and money to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.
Specification: Automated Machine Learning Masterclass: 15 (AutoML) Projects