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Introduction to Auto ML

50% of respondents said their companies had embraced AI in at least one of their business functions. 1/3 of IT leaders are preparing to use ML for business analytics. 25% of IT leaders think to use ML for security purposes. 16% of IT leaders said they need to use ML in sales and marketing. 91.5% of ongoing businesses have already made investments in AI. More than 44,000 jobs in the US and 98,000 worldwide require machine learning as a necessary skill.

Machine learning has made its pace in almost every industry. Therefore, the demand for machine learning experts has also increased. To fill the gap of Ml experts' scarcity, increase efficiency, and meet the demands, AutoML comes into the market. 61% of the data analytics and decision-makers using AI have implemented or are planning to implement AutoML in their firms. Many organizations have experienced an increase in revenue after implementing AutoML. Consensus corporations have seen a 19% increase in overall financial performance by adopting the AutoML tool.

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Why do we need Auto ML?

Today, If any organization needs to build a machine learning model, it requires.

  • Highly skilled machine learning experts.
  • A long process consists of multiple iterative steps
  • A lot of money

And to manage all three things in one, Auto ML will

  • Bridge the skill gap: Every company needs skilled AI, and machine learning experts with domain knowledge in various subjects like statistics, linear algebra, computer programming, which is a lot to ask from a single person, and therefore choosing the right candidate is also difficult. AutoML will automate most of the steps from a machine learning pipeline and is beneficial for non-machine learning experts to adopt ML and do innovation quickly.
  • Give Best Models: With the adoption of AutoML, one can increase the efficiency of models because AutoML iterates through different models and does hyperparameter optimization resulting in high-performance models, which require plenty of time if done manually.
  • Cost-efficient: Building an end-to-end machine learning is very tedious and costly. The costs include: Salaries of skilled employees. Cost of services used. Machines, whereas AutoML tools are more packet friendly than all these costs.
  • Good start for beginners: AI field is growing very rapidly and is very competitive. For the companies that have never deployed an AI project, AutoML will help them make their first step easy to enter the market.
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What are the Auto ML processes?

The two most important features of AutoML are that they automate the process of hyperparameter optimization, also known as hyperparameter tuning and model selection. AutoML can do experiments with different candidate models during optimization, and hyperparameter tuning starts with random sampling and is done using various sampling techniques.

During optimization, candidate models are ranked on a scoreboard, assuming some target metric to be optimized. The scoreboard will show to the user and automatically selects the best tool. The user can specify which metric to be optimized to choose any other candidate model: RMSE, MAE in case of regression problems, and precision, recall, F1 measure, or ROC - AUC in case of the classification problem. Using the cross-validation technique, the AutoML tool will measure the metrics and select the best score from all the candidate models.

Hyperparameters tuning by AutoML falls into two categories:

  • Engineering Features: In this, the AutoML tool will do experiments with different techniques for imputing missing values, normalization strategies for numerical variables, and encoding strategies for categorical variables. In the case of feature engineering, AutoML will make dimensionality reduction like PCA, etc.
  • Supervised ML model: Here, the AutoML tool will try different models according to the type of problem and then randomly select their hyperparameters to select the best configuration.

What is NAS (Neural Architecture Search)?

NAS automates the architecture design of the neural networks processes. NAS optimize things like which operators to choose and how to connect nodes. For the architecture of specific models, users can also define metrics of accuracy, time, size, etc. The research is going on to make NAS more efficient and reliable.
There are some open-source NAS libraries like NASLib, AutoPytorch available to optimize neural architectures.

What is Meta-Learning?

Meta-Learning, also known as learning to learn, is the ability to observe how different machine learning approaches work on different datasets and learn from their experiences to do new tasks faster. The efficiency of hyperparameter optimization and neural architecture search is improved by using meta-learning in AutoML.

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AutoML packages and Libraries

Now many companies offer AutoML as a service to upload a dataset and download the machine learning pipeline. Popular companies that provide this service are Microsoft, Amazon, and Google. There are various libraries and packages available that implement AutoML techniques. Some of them are:

H2O

H2O is an open-source and complete pack of tools that manage the data cleaning, model evaluation, and deployment in the entire data analysis cycle. It provides both R and Python clients and scales to enterprise-level deployments very well.

AutoWEKA

It is combined with the WEKA package and automatically selects the machine learning algorithm and its hyperparameters.

MLBox

An AutoMl library will make data preprocessing, optimization, and prediction.

Auto Sklearn

An open-source python library uses a scikit machine learning python library for AutoML.

  • This library is installed using pip
  • After running the code, it will give the statistical report of the search and find out the best-performing model.

Tree-Based Pipeline Optimization (TPOT) tool:

TPOT is a python library for automated machine learning. Its model pipeline is represented by a tree-based structure for a predictive modeling problem which includes data preparation, modeling algorithms, and model hyperparameters.

Fig 4: Use of TPOT in ML workflow

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What are the challenges of AutoML?

Usually, data scientists have to work on many steps while building a machine learning model like data cleaning, preparation, model selection, parameter tuning, and model validation. Some of the steps are iterative, which need time and money.
AutoML has been used for image recognition, NLP, semi-supervised learning, reinforcement learning, etc. But some challenges are faced by the organizations while using AutoML.

  • If we look at the distribution of time spent by the data scientist in solving an ML problem, most of the time is spent on thinking and understanding the problem, which can not be automated. AutoML can only reduce the burden of repetitive work.
  • Models are just as good as the data fed to them. If the data contains some errors, then the human must check the source from where the data is gathered. If the data is inaccurate, it will add bias to the model.
  • The problems the objective faces have multiple objectives, but the current AutoML optimization tools have some defined objectives that do not meet the organization's needs. Hence, one must have a clear understanding of what metric to optimize

What are the Use Cases of AutoML?

The Use Cases of AutoML are listed below:

Google Cloud AutoML Services

Sentiment Analysis: GCP Text AutoML API can be used for sentiment analysis, classifying positive and negative reviews from e-commerce websites, finding relevant tweets for a particular topic, and finding inappropriate content on social media.

Document Classification: GCP Text AutoML API can be used for text classification to find out if the document contains a piece of particular information or not, classifying documents according to content in it, classifying clauses in legal documents, etc.

Text Extraction: GCP Text AutoML API can also be used to extract different types of data such as URLs, e-mail addresses, phone numbers, etc.

Image Classification: AutoML Vision is a service delivered by Google to train machine learning models to classify the images concerning user-defined labels.

  • It will train models from labeled images and evaluate their performance
  • Register trained models for serving through AutoML API.
  • Leverage a human labeling service for datasets with unlabeled images.

Some real-world examples are object detection in images, face detection in images, handwritten text classification in images, etc.

Wildlife Image Tagging: The wildlife organization wants to track wildlife populations in an area. They have better understand human interaction and its effect on ecology first to do this. They need to set up camera traps and tag large amounts of images manually to track and monitor wildlife, which is time-consuming and laborious. But with AutoML, they can automate this process of tagging and analyzing images which will reduce costs and give accurate and fast results.

Retail Industry

AutoML has applications in the retail industry as companies collect large quantities of customers' data. With AutoML.

Sales Forecasting: Retailers can do sales forecasting based on customer data and purchasing season. It enables companies to identify the in-demand products and stocks by simultaneously checking the product availability for the customers.

Personalization: Also, with AutoML, personalization can be done based on previous trends, and brands can also predict future purchases by customized AutoML model.

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Conclusion

Companies are eager to adopt artificial intelligence and machine learning to boost their performance. It allows you to understand and analyze actual phenomena without performing tedious tasks and minimizing errors. Even after having so many benefits, Auto ML cannot replace data scientists, but it will assist data scientists in optimizing their work. It can serve as a tool to optimize their work.

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