we may know, how popular nowadays machine Learning technologies are. It is being
currently utilized in almost every field imaginable filed which has pushed its
importance infinitely. But what about those peoples who don’t have any idea
about Machine Learning? That’s where the need of Automated machine learning or AutoML comes into
machine learning , basically when we try automating the
end-to-end process of applying any machine learning to real-world problems that
are actually relevant in the industry.
Automated machine learning
What is AutoML?
machine learning (AutoML) is basically when we try automating the end-to-end
process of applying any machine learning to real-world problems that are
actually relevant in the industry. In recent years, it has been noticed as well
as proven time and time again that ML or machine learning is the key to the
future for many businesses. It is understandable that this is an up and coming
technology that allows for various directions of research, analysis, and implementation.
the use of this vast and powerful technology is limited to the number of data
scientists and machine learning enthusiasts and researchers, which are low in the number and slowly rising. To bridge this gap the theory or concept of Automated
Machine Learning came into the picture. In any machine learning project, a data the scientist has to apply many different techniques such as the data collection,
data pre-processing, feature engineering, feature extraction,
and feature-selection methods that make the dataset
ready for inference and hence for data analysis. Following those pre-processing
steps, an algorithm must be appropriately selected and hyper-parameter
optimization must be performed to maximize the predictive performance of their
final machine learning model.
many of these steps can only be performed by ML experts, So, seeing the
popularity of machine learning AutoML was proposed for the peoples who don’t
have much knowledge about is as an artificial intelligence-based solution to
the challenge of easily applying machine learning without much expertise.
of the popular AutoML tools available are list below:
is important that this field of Automated machine learning is researched on and
more communities are included as it is an area of utmost importance and a field
of untapped potential. Some of them are mentioned below-
Google one of the
leading tech-giants has released the Cloud AutoML for making
custom machine learning models based on business to business.
Cloud AutoML is a suite of machine
learning products that enables developers with limited machine learning
expertise to train high-quality models specific to their business needs. It
relies on Google’s state-of-the-art transfer learning and neural architecture
Auto-Keras is an open source software library for automated
machine learning (AutoML). It is developed by DATA
Lab at Texas A&M University and community contributors.
Auto-Keras provides functions to automatically search for architecture and
hyperparameters of deep learning models. The goal of AutoKeras is to make
machine learning accessible for everyone.
H2O is an open source, distributed in-memory machine learning platform with
linear scalability. H2O includes an automatic machine learning module also
called H2OAutoML which can be used for
automating the machine learning workflow, which includes automatic
training and tuning of many models within a user-specified time-limit.
Whereas, H2O.ai’s flagship product Driverless AI is for automatic
machine learning. It fully automates some of the most challenging and
productive tasks in applied data science such as feature engineering, model
tuning, model ensembling and model deployment.
out-of-the-box supervised machine learning. Built around the scikit-learn
machine learning library,
auto-sklearn automatically searches for the right learning algorithm for a new
machine learning dataset and optimizes its hyperparameters.
There are some others AutoML
tools are available in the market which is being used for specific purposes.
Thus, we can conclude, AutoML
maybe a new field as of now, however, it has boundless opportunities and may
even be a completely new field of machine learning in the future where a person
with no data science or machine, learning background can use these tools very
comfortably to find some hidden insights may apply to their business model to
be ahead of their competitors.
I hope after reading
this article, finally, you came to know aboutAutoML and some of the different
and popular AutoML tools available in the market. For more details about
different tools I have attached their official website links and I recommend
you to visit once.
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