Automated Machine Learning, or AutoML, is currently one of the most discussed topics among data scientists. By definition, it is the process of automating time-consuming and iterative tasks involved in machine learning model development. It assists data scientists, analysts, and developers in building machine learning models with large scale, efficiency, and productivity.
Automated machine learning also enables developers to maintain the quality of learning models. However, the reason behind its popularity lies in its ease of use and great efficiency. So, let’s explore some more facts about this.
When Should You Use AutoML: Classification, Regression, and Forecast
As mentioned earlier, Automated Machine Learning provides ease of use along with great efficiency, gaining huge popularity among industry newcomers in data science. You can apply automated ML to various industries such as healthcare, financial markets, banking, marketing, public sector, retail, sports, and manufacturing.
Automated Machine Learning democratizes the machine learning development process and empowers its users. Some specific cases of AutoML use by data scientists, analysts, and developers across industries include:
- Use ML solutions without extensive programming knowledge.
- Save time and resources.
- Provide agile problem-solving.
- Reduce the need for skilled data scientists, lowering the total cost.
Classification
Classification is a common machine learning task and a type of supervised machine learning. In this task, models learn from training data and apply those learnings to new data. The primary aim is to predict suitable categories for new data, with examples including fraud identification, handwriting recognition, and object detection.
Regression
Regression, another supervised learning task, predicts numerical output values based on independent predictors. Unlike classification, regression deals with numerical output values instead of categorical ones. Regression focuses on establishing relationships among independent predictor variables, such as predicting automobile prices based on features like gas mileage and safety rating.
Time-series Forecasting
Building forecasts is crucial for businesses, covering aspects like revenue, inventory, sales, or customer demand. Automated Machine Learning is employed to obtain recommended, high-quality time-series forecasts. AutoML combines techniques and approaches to achieve accurate results, incorporating advanced forecasting configurations like holiday detection, time-series and DNN learners, and configurable lags.
How Automated Machine Learning Works
During training, Azure Machine Learning creates several pipelines working in parallel to try different algorithms and parameters. The service iterates through machine learning algorithms paired with feature selections, producing models with training scores. The process stops when it meets the exit criteria defined in the experiment.
To work with AutoML using Azure Machine Learning, follow these steps:
- Identify the ML problem: Classification, forecasting, or regression.
- Choose between Python SDK or studio web experience.
- Specify the source and format of labeled training data.
- Configure the compute target for model training.
- Configure AutoML parameters for iterations, hyper-parameter settings, advanced processing, and metrics.
- Submit the training run and review the results.
Why is Automated Machine Learning Important?
Automated Machine Learning is essential because constructing a machine learning model manually requires domain knowledge, mathematical expertise, and computer science skills. Manual processes are time-consuming, prone to human error, and bias, affecting model accuracy. AutoML allows data scientists to create efficient training modules without significant time, effort, and money, resulting in high-performance models with minimal errors.
Sectors Using Automated Machine Learning
Almost every industry currently utilizes AutoML, including healthcare, financial markets, banking, public sectors, marketing, retail, sports, and manufacturing. It enables data scientists to focus on more complex problems, increasing productivity and efficiency.
For instance, Japan’s largest credit card company, SSMC, applies Automated Machine Learning in risk modeling and customer marketing applications, effectively increasing productivity in analyzing credit card data.
Conclusion
Automated Machine Learning software is the most effective way to enhance the workflow of analysts and data scientists. The benefits of using AutoML, along with the detailed process, make it a wise decision for the future. Jumping into AutoML will undoubtedly contribute to increased efficiency and productivity.
