What is Automated ML? Why is automated ML the new trending buzzword? If you are seeking answers to these questions, this blog will help you.
Time is one of the most valuable resources. Therefore, saving time is one of the most important aspects of any industry. Everyone wants to provide the fastest and most optimised service to its end consumers.
This is where Automated Machine Learning, also referred to as automated ML or AutoML, comes in. AutoML is the process of automating the iterative and time-consuming tasks of Machine Learning model development. Automated ML helps develop efficient, production-ready Machine Learning models in a shorter time duration.
Now let us see why this is the new buzzword in the work of Artificial Intelligence. In the recent decade, Machine Learning and Artificial Intelligence have been significantly adopted into various industries. They have transformed businesses like healthcare, financial services, and retail services. Machine Learning is not just good to have technology anymore. It is being used by businesses to understand their customers and innovate new business models.
What is AutoML?
As this technology is growing, the requirement of skilled Machine Learning engineers is also increasing. On the one hand, there is a severe shortage of qualified Machine Learning professionals. On the other hand, companies don’t have the budget or time to train professionals on ML technologies. This is where the emergence and importance of automated ML come into the picture.
AutoML is an automated process that applies Machine Learning technology to real-world business problems. It helps businesses automate the entire pipeline – from data acquisition to the creation of a customized Machine Learning models for the business. This ability enables non-technical users who do not know of underlying technologies of Machine Learning model creation to use Machine Learning efficiently.
Tools and Processes of AutoML
There are various AutoML tools and processes available in the market. Let us understand a few such tools and how they help enable better and efficient business opportunities.
- Amazon Alex is the technology that is used in building Amazon Alexa. It helps companies build advanced chatbots in less time. These chatbots can respond to human conversations by increasing user interface.
- Auto Folio, an algorithm selection tool, helps developers select the right algorithm with accurate hyperparameters, and the best selection tools. This tool saves time and helps in getting accurate results with the use of the best algorithm.
- Auto-sklearn, the software developed by the University of Freiberg, offers unlimited Machine Learning algorithms. Users can select the right algorithm with accurate hyperparameters and get the best results. It offers out-of-the-box supervised Machine Learning techniques to scientists.
- Google’s Cloud AutoMLis a suite of Machine Learning-related products that is useful for users with limited knowledge about Machine Learning models. It has a user-friendly GUI and uses built-in functionalities like neural architecture search and transfer learning to design and deploy Machine Learning models based on the latest data.
All the above-described tools are of great help to developers, business users, and data scientists.
Benefits of AutoML
Let us see how Automated Machine Learning benefits business.
In the traditional ML model, human intervention is required in every phase, starting from data cleanup, data ingestion, data preprocessing, and prediction modeling. Automated ML can automate each of the steps except the data collection. The growing demand for Machine Learning models by enterprises is driving the need to create user-friendly ML systems that can be used as a plug and play system by any business user.
ML automation helps data scientists in improving their productivity, allowing them to focus on core issues while the repetitive ML tasks are automated. It also helps in reducing the human errors that occur in multiple manual steps.
Most of the data scientists time is spent on cleaning, organizing, and collecting meaningful data. Automated Machine Learning helps scientists focus on complex problems and not spend time deciding which model to choose and which model to build as there is no best algorithm for any specific problem-solving. The saving time of skilled professionals is one of the key profits of using AutoML.
AutoML takes care of the quality and accuracy of the model and reduces the chances of human error. Simplicity and flexibility are other pluses in AutoML. Since the hectic task of mining, cleaning, and processing data is automated, the work becomes simple and flexible.
Auto ML enables data scientists to make proper use of Machine Learning to handle Big Data. Accuracy in automated Machine Learning is one step ahead of traditional ML and fine-tunes the data more effectively and reduces the error rate. AutoML uses fewer resources to uphold performance and saves a lot of GPUs and CPUs, making in power efficient. In addition to all these, AutoML makes the processes cost-effective as a smaller number of skilled professionals are required.
The AutoML has just started its course and is in its nascent state. While faced with small imperfections and hiccups now, AutoML will sooner or later overcome them and will win the game of Machine Learning in the long run.
What are your plans to leverage this powerful technology?