People are yet not done figuring out machine learning, and now there is a rise of a new advanced term on the market for machine learning, and, i.e. “Automated Machine Learning.” Let’s discuss automated machine learning. Thankfully, automated machine learning is a more straightforward concept, and it makes things easier for developers and professionals.
AutoML is a shift from traditional rule-based programming to an automated form of design where machines learn the rules on its own. In automated machine learning, we offer a relevant and diverse set of reliable data to, in the beginning, to help automate the process of decision making. The engineers will no longer have to spend time on repetitive tasks, thanks to AutoML.
AutoML is the result of the recent research report put forth by Google. In this research paper, it emphasizes neural architecture search with reinforcement learning (NAS). NAS seek for the relevant optimal network architecture that is the best for a specific data set. What is so great about this technology is that it is possible to utilize transfer learning for using the architecture discovered by NAS on small data set on similar larger ones.
The growth in the demand for machine learning professionals will get a massive boost with the rise of AutoML and advanced machine learning platforms. To be a savvy professional, it is an excellent choice to opt for a suitable machine learning certification training course. Joining a class will allow you to learn about various aspects of machine learning and how you can use machine learning in your professional life.
Why is AutoML Getting so Much Hype These Days?
The demand for machine learning platforms is increasing. However, the traditional machine learning platform is not suitable for every business. Unlike traditional machine learning solutions, the companies will no longer have to invest heavily in experts and software to implement machine learning in their companies. In short, machine learning will be available to almost all businesses with the advancement in AutoML.
The huge corporations, such as Disney and Urban Outfitters are already reaping the benefits from AutoML. However, Google plans to make AI accessible to all businesses with AutoML technology.
Here are some of the strong reasons for shifting to AutoML:
Better Accessibility and Greater Reach
The AutoML solutions are affordable for most businesses. We can access most of the packages released by Google for less than $50. Till date, Google has released AutoML for image analysis, a dynamic transition between languages, and natural language for unstructured data.
The users do not need a massive volume of data to use machine learning. They need to upload around 12 or so images and labels to start using AutoML features for both object recognition and binary classification.
Businesses can now use ML for sophisticated business problems without hiring expensive professionals.
The powerful AutoML technology allows quick, high-fidelity prototyping for the creation of enhanced user experience. Within a few minutes, companies will now be able to create models that can deliver near-optimal performances. These processes would have taken months for data scientists to create architecture without AutoML.
Companies will now be able to use exceptional user experience quickly for driving product innovation in diverse ways.
AutoML technology has also been a great boon for citizen data scientists. The citizen data scientists can now utilize their time on broader data analytics tasks, thanks to automation. They can use a simple point-to-click interface inside AutoML platforms for developing ML models.
There are still more problems for citizen data scientists to handle data unification, identifying non-obvious patterns, and so on. However, researchers will most probably resolve these issues in the future.
AutoML enables researchers to create a base for building custom models. AutoML is still not able to exceed the performance of hand-constructed models. However, it can achieve the objective with over 90% accuracy. With AutoML tools, data scientists can use the baseline for efficiently providing direction to model creation and fine-tuning.
Google is Making Massive M
oves in Automating Machine Learning
Martin Gorner in his presentation revealed how it is possible for software engineers to create an object recognition Convolutional Neural Network, even if they have an advanced degree. Thanks to open research environment in machine learning, even a person with a basic understanding of mathematics can learn and understand the latest concepts in ML research.
Google is ahead of the curve right now in their effort of making Machine learning available to masses with their AutoML offering and G Cloud. Earlier in this article, I also talked about the recent report of Google about neural architecture search with reinforcement learning. Hence, Google is investing heavily in making a substantial impact in the AI industry.
What About Competitors?
Google’s AutoML does not entirely own the space of neural architecture search. We can see other competitors, such as AutoKeras, Microsoft AutoML, and so on. The competitors are also in verse of offering similar features. However, we are yet to see the differences in cost and performance in different packages.
The Future of Advanced Machine Learning Platforms
Google has given a clear indication that it is going to focus heavily on advanced machine learning platforms by creating its version of the ML platform. All these advanced machine learning platforms, Google AutoML, AutoKeras, Microsoft AutoML, and so on, aim to assist engineers leverage Machine Learning by offering aided or automated model building.
The advanced machine learning platforms address common problems in production-grade machine learning tasks. These platforms help in reducing human errors, enabling better integration with existing data infra, identifying and implementing best practices, and so on. The businesses can cut down lengthy machine learning process for solving common business problems and getting more output via automation from your data science employees.
The features like automated statistical analysis and exploratory learning are like given in the majority of advanced machine learning platforms.
However, there are some platforms, offering advanced features. Take H20’s Driverless AI, for instance; it is using automated engineering, model building, transformation, and ML interpretation. It was in the top 1% among overall Kaggle competitors. They ranked high for using creative functionality on some specific data sets.
The work on AutoML has just begun. We are yet to see what is in the store in the AutoML field. Hence, we can say that the future holds even simpler solutions that would be easy to use for both businesses and developers.
The rise of advanced machine learning platforms will force general people to be at least aware of necessary AI technologies. In the future, we are likely to see technologies like AI and machine learning as must-have technologies that businesses must integrate. The companies need to get as many valuable insights as possible from these technologies to gain a competitive edge in the future.
The automated machine learning is not a full stop to the advancement in the field of machine learning. We will most likely see more progress in the future in the AutoML field. As a savvy professional, you must always be aware of the latest AI and machine learning trends to be an on-demand professional.