Automated Machine Learning

November 23, 2022 Maitri Katti
Reading Time: 6 minutes


Any information technology intended to direct the operation of machines is referred to as machine automation. Many modern pieces of equipment come equipped with some kind of computer control. In many instances, business systems that supply control inputs are also connected to the machinery. In the discipline of data science, automated machine learning (AutoML) is now one of the most dynamic subfields.

How does the AutoML process work?

AutoML is typically a platform or open source library that simplifies each step in the machine learning process, from handling a raw dataset to deploying a practical machine learning model. In traditional machine learning, models are developed by hand, and each step in the process must be handled separately. Users with minimal machine learning and deep learning knowledge can then interface with the models through a relatively simple coding language like Python. For a particular assignment, AutoML automatically finds and employs the best kind of machine learning algorithm. This is accomplished using two aspects:

  • Neural architecture search, which automates the design of neural networks. This helps AutoML models discover new architectures for problems that require them.
  • Transfer learning, in which per-trained models apply what they’ve learned to new data sets. Transfer learning helps AutoML apply existing architectures to new problems that require it.

Features of Automated Machine Learning:

  1. Preprocessing of Data: A data mining technique that involves transforming raw data into an understandable format. Every algorithm functions differently and has various information needs. For instance, some algorithms need normalization of numerical features, whereas others do not.
  2. Feature Engineering: Feature engineering, which is frequently time-consuming and expensive, is the process of changing the data to improve machine learning algorithms. The majority of feature engineering is generic, while some feature engineering does require domain understanding of the data and business processes.
  3. Diverse Algorithm: Every dataset contains unique information that reflects the individual events and characteristics of a business. Due to the variety of situations and conditions,  Because of this, we need access to a diverse repository of algorithms to test against our data, in order to find the best one for our particular data.t.

  4. Algorithm Selection: It’s nice to have access to hundreds of algorithms, but unless you have more patience than I do, you won’t have time to test each and every one of them on your data.Some algorithms won’t function effectively on your data because they aren’t appropriate for your data, the amount of your data, or both.
  5. Ensembling: Teams of algorithms are referred to as “ensembles” or “blenders” in data science terminology.The advantages of each algorithm counteract the disadvantages of the others.Due to their variety, ensemble models frequently outperform individual algorithms.

Why is Automation Required? 

Automation of machine learning is crucial because it enables businesses to drastically cut the amount of knowledge-based resources needed to develop and deploy machine learning models. Organizations with less experience in the relevant fields, computer science, or mathematics can nonetheless use it effectively. This lessens the strain on both enterprises and data scientists to locate and stay in their current positions.
By minimizing potential for bias or inaccuracy, autoML can also help businesses increase model accuracy and insights. This is due to the fact that machine learning automation is created using best practices chosen by knowledgeable data scientists. AutoML models don’t rely on businesses or developers to apply best practices on their own.

A significant change in how businesses of all sizes approach machine learning and data science is represented by automated machine learning (AutoML).It takes a lot of time, resources, and effort to apply conventional machine learning techniques to actual business challenges. Automated machine learning represents a dramatic revolution in how firms of all sizes handle machine learning and data science (AutoML).
Applying traditional machine learning methods to real-world business problems requires a lot of time, money, and effort. Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.

Businesses across all sectors, including healthcare, financial markets, banking, the public sector, marketing, retail, sports, manufacturing, and more, can now benefit from machine learning and AI technology, which were previously only accessible to organizations with significant financial resources. Automated machine learning enables business users to easily apply machine learning solutions, freeing up an organization’s data scientists to work on more challenging challenges by automating the majority of the modeling processes required to construct and deploy machine learning models.

Different ways to use AutoML:

AutoML shares common use cases with traditional machine learning.

  • Fraud detection in finance. It can improve the accuracy and precision of fraud detection models.
  • Research and development in healthcare, where it can analyze large data sets and draw insights.
  • Image recognition, which is useful for facial recognition.
  • Risk assessment and management in banking, finance and insurance.
  • Cyber security, where it can be used for risk assessment, monitoring and testing.
  • Agriculture, where it can be used to expedite the quality testing process.
  • Entertainment, where it can be used as a content selection engine.
  • Malware and spam, where it can be used to generate adaptive cyber threats.
  • Marketing, where it can be used for predictive analytics and improved engagement rates. It can also be used to improve efficiency of behavioral marketing campaigns on social media.

What is AutoML Python?

Auto-sklearn is an extension of AutoWEKA using the Python library scikit-learn which is a drop-in replacement for regular scikit-learn classifiers and regressors. Auto-PyTorch is based on the deep learning framework PyTorch and jointly optimizes hyper parameters and the neural architecture.

AutoML libraries for Automated ML:

  1. Auto-Sklearn: One of the best open-source AutoML libraries for machine learning applications is Auto-Sklearn. As an automated machine learning toolkit, it is well-known for releasing users from algorithm development and hyper parameter adjustment. The projects using AutoML for ML make use of Bayesian optimization.
  2. Auto-ViML: Auto-ViML is used for completing machine learning projects out of the huge AutoML libraries. It was designed for developing high-performance interpretable models with fewer variables. It helps to automatically build different machine learning projects with a single line of code. There are attractive features in this AutoML library such as SMOTE, Auto_NLP, data time variables, and feature engineering.
  3. MLBox: MLBox is a popular AutoML library for machine learning projects with different features such as fast reading, distributing data preprocessing or formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization, and prediction with models interpretation. It is focused on drift identification, entity embedding, and hyper parameter optimization.


By automated ML most of the modeling tasks necessary in order to develop and deploy machine learning models, automated machine learning enables business users to implement machine learning solutions with ease, thereby allowing an organization’s data scientists to focus on more complex problems. AutoML is a growing field that seeks to automatically select, compose, and parametrize machine learning models.

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