The remarkable progress in machine learning, driven in large part by the advancement of AutoML, has paved the way for transformative applications across various industries. In finance, it aids in fraud detection, risk assessment, and algorithmic trading. In healthcare, AI plays a crucial role in revolutionizing patient care, diagnosis, and treatment. In the realm of software development, AI technologies streamline coding processes, enhance efficiency, and automate repetitive tasks.
Seen as part of Artificial Intelligence (AI), Machine Learning (ML) is the study that allows algorithms to process data and learn from it automatically. This capability allows for the algorithm to use the processed data and make decisions or estimate outcomes without being specifically programmed to do so. In everyday life, machine learning improves the quality of computer vision tasks that we use on a daily basis. AutoML takes that technology to a new level of efficiency that can only improve on itself and, with time, yield better results. In this article, we will walk you through the following:
- What is Automated Machine Learning?
- How AutoML works in practice? AutoML applications and products
- ML Automation and the Future of data scientists
- AutoML for advanced research purposes
What is Automated Machine Learning?
What is automated machine learning (AutoML), and how does AutoML work? ML automation is the process of performing machine learning tasks by applying automation processes. As a field comparably new, it may seem terrifying as to what extent it will replace (or will it?) the human factor in machine learning and data analysis. From the standpoint of ML engineers, AutoML is more of a project than a replacement. For automation, AutoML requires hand-coding from ML engineers, as well as maintenance and model building. After all, this is an AI technology that is man-made at its core and needs to be trained to learn to perform the tasks that it’s aimed for.
Instead of having humans concentrate on complex tasks of advanced machine learning, we are capable of training data and artificial intelligence to perform those tasks instead.
For the ML model to work, a variety of skills need to be involved, from programming to ML and domain knowledge, as well as linear algebra. This is where AutoML comes in to make it easy for non-experts to optimize ML pipelines. So AutoML can potentially take care of pre-processing of data, training, tuning, and evaluation.
With this technology, non-data scientists or ML professionals can implement AutoML solutions for the fields where it’s not essential to rely on hand-coded algorithms. Currently, automated learning is not as perfect, but ML engineers globally indeed see it excelling in the near future. Human-centered AI, as well as AutoML, require improvement by people in the fields of expertise to which it will be applied to.
How AutoML Works in Practice: AutoML Applications and Methodologies
As a method, AutoML aims to automate the design and development of machine learning tasks and applications. Since the data to be processed and available to be able to build machines for various scenarios is rapidly growing, there is a gap that AutoML is filling in for ML engineers and experts.
In short, AutoML is the study that allows us to find solutions to dealing with ML methods with minimized interaction from the users. Most of the studies concentrate on supervised learning practices, even though semi-supervised and unsupervised are becoming more and more common. For AutoML supervision means that the method is trained to map and label objects based on a sample provided to it. Respectively, unsupervised methods mean that the learning is initiated by the machine, and semi-supervised allow for partial training but leave room for the machine to improve on the labeling methods.
Some applications of AutoML are but are not limited to:
- Text classification and annotation
- Face recognition
- Spam filtering
- Handwriting recognition
Supervised AutoML has most of the applications in real life and has been the most extensively studied. When provided the data set, the machine will learn from the samples and be able to perform labeling, classification, and form models.
In the scope of AutoML methodologies, some experts suggest reviewing those in waves of their emergence. Each consecutive methodology has come to improve on the gaps in the previous one, and since 2006 we have seen three phases of methodologies. This does not limit us here, but for the sake of the article, we are looking at the representative methodologies that brought innovation and contributed to the development of the field.
Phase one: The beginning
As one of the pioneers of what is now known as AutoML methodologies, PSMS (Particle Swarm Model Selection) has a full ML pipeline model. It entails both the initiation, data procession, and extraction but also the optimization of all parameters to fit into the model. Some more have come along, but PSMS and its variations (Ensemble PSMS) are still at the core of the modern AutoML code. Another honorable mention needs to be the GPS system, in which the originator took a fitting pipeline template and then proceeded to optimize hyperparameters for it.
Phase two: Era of alternatives
When phase one came to an end in late 2010, the era of improvement and ideation came to be. Here is where we got the models based on the SMBO (Sequential Model-based Optimization). The model mainly focused on using surrogate models.
Other notable methodologies of this era are:
- GAPSO
- Auto-WEKA
- AutoSkLearn
- TPOT
Phase three: The Present and the Future
Finally, phase three is still in action and has brought us one of the most revolutionary discoveries in regard to AutoML, neural architecture research. The advancement that was achieved in just 10 years going into the third phase of Automated machine learning is complex and opens doors to a variety of new possibilities.
These advancements have skyrocketed AutoML into the field of deep learning. Neural Architecture Search, also referred to as NAS, is the greatest of those advancements and executes a search for architecture and hyperparameters in order to apply solutions to the models. This is the technological breakthrough that has given us the ability to execute many of the applications mentioned before. But, the community of ML engineers doesn’t stop there and sees a lot of room for development and improvement.
ML Automation and the Future of Data Scientists
A lot of debate and concern in the ML community concerns if AutoML is going to replace data scientists. In short, no! As we discussed, ML automation has one purpose concerning data scientists, and it’s to help them avoid doing time-consuming manual data labeling tasks when they can focus on processes like AutoML feature engineering or hyperparameter optimization, while in turn allowing the AI to optimize data labeling and other AutoML solutions. Automated machine learning operations assure that data scientists can provide machine learning solutions without limitless inquiries on model hyperparameters and selection, lengthy data preparation tasks, and more.
What else can the AutoML framework support data scientists with? Well, many tasks still performed by data scientists are connected with modeling, evaluation, and algorithm selection. So those can be trusted in AutoML frameworks and let data scientists take up the jobs that an algorithm can never perform.
If there is still a concern, remember how in the early 90s, Personal Computer was considered a threat to mathematicians. Today, we see that those allow mathematic minds to perform more complex tasks and escalate innovative evolution.
Wrapping up
As AutoML continues to progress, it holds the promise of enhancing efficiency and accuracy in machine learning tasks. However, it is crucial to strike a balance between automation and human expertise, leveraging AutoML as a valuable tool while still relying on domain knowledge and the skillful guidance of ML professionals. With continued advancements and collaboration, AutoML has the potential to drive innovation and create new opportunities in the realm of artificial intelligence and data analysis.
About the Author
Melanie Johnson, AI, and computer vision enthusiast with demonstrated experience in technical writing. Passionate about innovation and AI-powered solutions. Loves sharing expert insights and educating individuals on tech.
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