Automated Machine Learning or AutoML is currently one of the most discussed topics among the data scientists. By definition, it is the process of automation of the time consuming and iterative tasks of machine learning model development. It helps data scientists, analysts, developers. It builds machine learning models having large scale, efficiency, and productivity.
The automated machine learning also allows the developers to maintain the quality of the learning models. But the reason behind its popularity is the ease of use and great efficiency. So let’s know some more facts about this.
When You Should Use AutoML: Classify, Regression and Forecast
As we have mentioned earlier, Automated Machine Learning provides you with the ease of use along with great efficiency. So it has gained huge popularity among the newbies of the industry data science. You can apply automated ML to various industries like healthcare, financial markets, banking, marketing, public sector, retail, sports, and manufacturing.
Automated Machine Learning democratizes the machine learning development process. And it also empowers its users. We have found some particular cases of the use of the AutoML by data scientists, analysts, and developers across the industries. These are:
- Use ML solutions without extensive programming knowledge.
- Save time and resources.
- Provide agile problem-solving.
- Reduce the need for skilled data scientists. This will help in lowering the total cost.
Classification is a common machine learning task and a type of supervised machine learning. In this, the models learn by training data. And then apply those learning to new data.
The primary aim of classification models is to predict the suitable categories for the new data. Common classification examples include identification of fraud, handwriting recognition, and object detection.
This is also a common supervised learning task, like classification. Along with similarities, there are some differences between classification and regression. Regression models predict numerical output values based on independent predictors. Whereas in the case of classification, predicted output values are categorical.
The primary focus of regression is to establish the relationship among the independent predictor variables. It is done so by estimating the impact of one variable on the others. As an example, automobile prices get affected by features like gas mileage, safety rating, etc.
Building forecasts is a fundamental part of a business. It includes revenue, inventory, sales, or customer demand. Automated Machine Learning is used to get a recommended, high-quality, time-series forecast. AutoML combines techniques and approaches to get the result.
The approach of automated time-series experiments has an advantage. It can incorporate many contextual variables. And then evaluate their relationship with one another during training. Advanced forecasting configuration includes:
- Holiday detection.
- Time-series and DNN learners (Auto-ARIMA, Prophet, ForecastTCN).
- Many models support through grouping.
- Rolling-origin cross-validation.
- Configurable lags.
- Rolling window aggregate features.
Thus Automated Machine Learning helps to make an accurate prediction, classification, and regression. Now let’s see how it works.
How Automated Machine Learning works?
At the time of training, the Azure machine learning creates several pipelines. Also, these work in parallel to try different algorithms and parameters. The service iterates through machine learning algorithms paired with featured selections. And some models with a training score are produced.
The score decides the ‘fitness’ of your data in the model. The process stops when it hits the exit criteria defined in the experiment.
With Azure machine learning, you can design and run the automated ML training experiments with these steps:
- Identify the ML problem to be solved: classification, forecasting, or regression.
- Choose between Python SDK or the studio web experience. For limited or no code experience, you should try Azure machine learning.
- You should specify the source and format of the labelled training data.
- Then configure the compute target for model training,
- Finally, you have to configure the Automated Machine Learning parameters. These parameters will determine how many iterations over different models, hyper-parameter settings, advanced processing, and the metrics to look at to determine the best model. When you complete all the processes, you can submit the training run. And don’t forget to review the results.
That’s it. The process of working with AutoML is very simple, and that is the cause behind its popularity. Now we will discuss the importance of Automated Machine Learning.
Why is Automated Machine Learning Important?
You can construct a machine learning model in manual mode. For that, you require domain knowledge, mathematical expertise, and great skill of computer science. Also, you have to go through several multistep processes that are time-consuming. Not only that, but there are also great chances of human error and bias, which affects the model accuracy.
Automated Machine Learning opens the door of opportunity for the data-scientists. They can also create efficient training modules. They use their baked-in knowledge to create without spending a lot of time, effort, and money.
The training models which has created using AutoML has proven the best performance-giver, with almost no error. So we hope that you can understand how important the AutoML is.
Sectors Using Automated Machine Learning
Almost every industry is currently using AutoML. Healthcare, financial marketers, banking, public sectors, marketing, retail, sports, and manufacturing are the most common names. Automated Machine Learning also allows data scientists to focus on more complex problems.
Let me give you an example of Automated Machine Learning. SSMC, Japan’s largest credit card company, has applied Automated Machine Learning. They use it in risk modelling and customers’ marketing application. The use of AutoML has also proved to be effective to increase productivity in the process of analyzing credit card data.
Automated Machine Learning software is the most effective way to increase the workflow of analysts and data scientists. We have mentioned the benefits of using AutoML and the process of using this in detail. So, we would like to suggest you that jumping into AutoML will be a very good decision for the future.