Automated Machine Learning Processes and Python Frameworks

Automated Machine Learning 

Automated machine learning (AutoML) project institute numerous discrete steps, i.e., accumulating raw data, integrating data sources, cleaning and preprocessing data, feature engineering, assembling model, tuning of hyper-parameter, testing, validation, and deployment.

However, data scientists can best subsidize their cleverness and imagination to the model building phase. Yet it appears that the utmost time-consuming phases of machine learning are feature engineering and tuning of hyper-parameters.

Hence, due to the time dilation and premature deployment, many models are not as optimal as expected. Automated machine learning short for AutoML diminishes the burden on data scientists and engineers. With the help of this, they can devote very much less time on feature engineering and tuning of hyper-parameters.

Not only that, they can even devote supplementary time to experimenting with distinct model architectures. Also, swift exploration of the solution to a problem, not only permits an engineer to quickly assess a dataset yet they can improve the performance of the model.

Python Frameworks and Libraries of AutoML

AutoML process encompasses various pipeline frameworks as well as tools that can automate both the comprehensive and incomplete business process. Machine learning numerous frequently used processes are very popular such as Auto-SkLearn, Cloud AutoML, and MLBox etc.

Python library encompasses the tantalizing capability to automate machine learning procedures, i.e., Data pre-processing, augmenting ML models, network tuning, and making predictions. There are other assured and convinced features employed in the framework such as deep-learning, tuning hyper-parameters.

Auto-SkLearn is an additional widespread framework built by employing a Python learning package. Auto-SkLearn is assimilated on the top of the machine learning package, i.e., the scikit-learn. It can implement tasks such as selecting the fitting machine learning algorithm, enhancing the hyper-parameters, and many others.

Process of Traditional Machine Learning

As discussed above, traditional machine learning can perform tasks that lead to time-consuming and inefficient modeling. Figure .1. depicts the complete workflow of traditional machine learning.

Process of Traditional Machine Learning

Figure .1. Tradition ML Workflow

Process of Automated Machine Learning

As numerous steps are outside the capabilities and skills of experts, AutoML proposed as a solution based upon artificial intelligence. In other words, AutoML is a sequence of conceptions, functions, and techniques utilizes to automate the processes. Figure .2. illustrates the workflow of AutoML.

Process of Traditional Machine Learning

Figure .2. Automated ML workflow

Benefits and Pros

There are multiple benefits of automated machine learning over traditional machine learning. Some of them listed down below.

      • Easy implementation 

There are experts in industries, to solve real-world problems experts who implement machine learning algorithms. The experts must have certain skills such as computer science skills, domain knowledge, and mathematical understanding. However, AutoML institutes a modular form so it can be apply by non-experts also.

      • Reduce Biasness

Biasness and errors that occur while implementing algorithms and designing models of machine learning can be reduced through AutoML.

      • Time and Cost Saving

AutoML also lessens the time mandatory to construct a model. It also reduces the time required to test a machine learning model. Organizations can also condense the cost of appointing industry experts to implement AutoML.