12Dec - by aiuniverse - 0 - In Machine Learning


AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

How it Functions

Using AutoML, businesses can automate tedious and time-consuming manual work required by today’s data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Automating ML Workflow

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

  • Data science is like any other business function that must be performed with due diligence and needs creative thinking and human skills to get the best results.
  • Data science is like babysitting, and one has to take care of the ML models, data and other assets regularly.
  • AutoML is still in infancy. Once it’s ready, living without data scientists could be possible, at least for the most part.
  • When one gets the results and business insights, the individual would still need the data workers to interpret them and communicate them to business.

Future of AutoML

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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