The Rise of Automated Machine Learning

The Rise of Automated Machine Learning

Significant advancements have been happening in data science. Technological innovations, large-scale academic research, and the growing demand for data science have led to several groundbreaking innovations. You would have heard of large language models (all thanks to Chat-GPT), but that is just the tip of the iceberg.

Other significant innovations include advanced deep learning architectures, Explainable AI, and Graph Neural Networks. One such advancement is AutoML, which has rocked the data science fraternity due to its potential to democratize machine learning.

Let's start by understanding what the term AutoML means.

What is AutoML?

AutoML is short for Automated Machine Learning. It is a framework that automates machine learning (ML) processes, from cleaning, transforming, and mining data to selecting, developing, tuning, evaluating, and validating models. Any tool, method, or framework that allows for automating different aspects of the ML process, from pre-modeling to post-modeling, is called AutoML.?

The AutoML Architecture

How Does AutoML Work?

AutoML allows for the end-to-end automation of a machine learning model building, making such models accessible worldwide.

How AutoML works

Let's understand how AutoML provides the user with a machine-learning model.

The entire process consists of 5 core stages as follows:

Stages of Model Building

1. Data Preparation

AutoML systems start by preparing the data. They typically expect structured datasets in tabular format and perform missing value imputation, outlier capping, anomaly detection, variable encoding and scaling, and even data splitting into train, testing, and validation. Often, the user can dictate specific methodologies for all these tasks.

2. Feature Engineering

The next aspect of automated model building is feature engineering, where feature selection, extraction, transformation, and creation are automated. The AutoML systems use algorithms to enhance the quality of the features and make them fit for model development. Here, various statistical techniques and other approaches, such as genetic programming, are involved.

3. Model Training

The most intensive part of AutoML is at the model training stage, where many algorithms are used, ranging from traditional ones like linear and logistic regression to advanced ones like XGBoost and Artificial Neural Networks.

4. Hyperparameter Tuning

The best model is identified by performing hyperparameter tuning. Here, the AutoML algorithm goes through the various model hyperparameters that control model behavior and searches for that combination of hyperparameters and algorithms that yield the highest accuracy.

Methods like random search, grid search, Bayesian optimization, etc., are used to search the hyperparameter space. Cross-validation techniques like k-fold or leave-p-out validation ensure the model doesn't overfit.

The numerous models are evaluated on performance metrics ranging from simple accuracy and f1-score to more complex ones, such as the ROC and precision-recall curve.

5. Deployment and Monitoring

Lastly, the best model identified by the AutoML system is deployed into a production environment that can be on-prem or cloud. Several systems even help combine multiple base models and deploy ensemble models for better performance.

Certain AutoML systems even provide model monitoring capabilities, which help detect model degradation, data drift, and other issues, ensuring optimal model performance.

Now that we know what AutoML is and how it works let's understand why it is so important.

Why AutoML is Important in The Current Age

The gap between the demand and supply of data science professionals is huge, especially for roles like data scientists, machine learning engineers, and data engineers. Almost 92% of hiring managers face difficulties hiring such professionals due to skill gaps in machine learning and other fields like natural language processing, data analytics, and automation.

AutoML can fill this gap by allowing individuals with limited theoretical knowledge and practical experience to participate in ML and AI model development. AutoML allows for the creation of machine-learning pipelines. This allows individuals to get involved and monitor the machine-learning process end to end.?

Machine learning can be highly complex, with data cleaning and model selection being time-consuming. AutoML automates such processes with simple user interfaces that enable non-technical individuals to use ML in their workflows.

This particular aspect of AutoML leads to a wider discussion topic: the democratization of machine learning through AutoML.

AutoML for Machine Learning Democratization

Democratization of machine learning means making this technology available to a substantially larger audience. If ML gets adopted by several organizations and utilized by non-technical individuals, then it can massively increase the impact of data science on organizations worldwide.

Through AutoML, issues like lack of theoretical understanding of algorithms, limited understanding of key tools and languages, and other skill gaps can be bridged.

Citizen data scientists are a crucial aspect of machine learning demonstration. This upcoming phenomenon refers to individuals without formal training in data science who leverage data science tools and combine their domain expertise to make impactful data-driven decisions.

AutoML allows for the democratization of ML by enabling citizen data scientists to use user-friendly AutoML tools to easily perform tasks such as data preprocessing, exploratory data analysis, visualization, feature engineering, hyperparameter tuning, model evaluation, etc.

Most importantly, rather than manually building multiple ML models, which generally requires skilled data science and machine learning engineers with deep technical knowledge, AutoML can allow citizen data scientists to explore several advanced ML algorithms and find the best one.

Through AutoML, machine learning can be democratized, leading to several benefits, such as

  • Informed, objective, and data-backed decision-making
  • Broader participation of employees in the ML process enables innovation
  • Better decision-making due to non-technical but domain experts being involved in ML-based decisions?

Democratization of ML has obvious advantages. However, it is important to inspect this technology's pros and cons objectively.

Advantages of AutoML

There are several crucial advantages of AutoML, such as

  1. Efficient

Manually developing and optimizing ML models requires a lot of trial and error, which is a waste of time. AutoML makes the process significantly faster and more efficient.

  1. Easy

AutoML-based software often has an easy-to-use interface that facilitates ML-related processes like data pre-processing, feature engineering, and model selection.

  1. Bias Reduction

AutoML minimizes human involvement in ML processes, which helps reduce model bias. Several key processes are automated.

  1. Higher Accuracy

Several tedious and monotonous tasks need to be performed manually during model development. Regularly performing such tasks can cause fatigue in model developers, resulting in human-caused errors. Through AutoML, such mundane tasks can be automated, leading to better model performance.

  1. Better Resource Allocation

As mundane tasks like data cleaning and pre-processing are eliminated, the precious technical workforce can be reallocated to other business-critical tasks.

  1. Broader Availability of Human Resource

The Royal Society of the UK has warned that the demand for data scientists and ML engineers is rapidly increasing, causing supply-demand issues. As such professionals become difficult to find, AutoML allows citizen data scientists to perform several crucial ML model development tasks, helping to improve the availability of data science professionals.

  1. Better Collaboration

As AutoML relaxes the coding requirement, other professionals, such as business analysts, business leaders, and domain experts, can perform machine learning. This helps better collaboration between domain and technical experts, making the ML model relevant to solving business problems.

  1. Cost Reduction

As fewer human resources get involved due to AutoML, developing machine learning models becomes cheaper for organizations. Also, companies can save money by skipping hiring a large number of highly technical individuals and training staff in data science with the help of AutoML.

AutoML provides several advantages to organizations. Unfortunately, it's not all sunshine and roses, and there are several issues with AutoML.

Disadvantages of AutoML

Disadvantages of automated machine learning

There are several disadvantages and risks with AutoML, as stated below-

  1. Customization Limitations

While AutoML allows for easy and quick model development compared to hand-coding, it also has severe limitations. One such is the lack of customization, and AutoML software cannot always be used to meet the requirements of specific projects or business problems.?

  1. Black Box

AutoML uses pre-written algorithms, with the users having limited control over how the algorithm functions. This makes the models developed through AutoML difficult to interpret. Thus, AutoML models are more of a black box than their manually developed counterparts.

  1. Resource Intensive

AutoML's advantage is that the best-performing model can be found without theoretical knowledge. However, this is achieved through extensive hyperparameter searching, which is highly time-consuming and computationally intensive.

  1. Lack of Support for Unstructured Data

It isn't easy to train models using AutoML on unstructured data. Often, manual intervention is required to make unstructured data fit for model development, making the use of AutoML with datasets like text, images, etc., highly limited.

  1. Dependency on Propriety Software

Advanced AutoML tools are often proprietary software that makes users dependent on their ecosystem. This makes innovation and integration with other tools challenging.

If you are not discouraged by AutoML's disadvantages and still find it highly lucrative, then you are not alone.

Due to its advantages, AutoML is widely accepted, and several industries find it highly useful. Let's look at a few of its key users.

Industrial Applications of Automated Machine Learning

AutoML for Industries

Several players have benefited from incorporating machine learning in their workflows – all due to AutoML. The crucial fields that have majorly benefited from AutoML are as follows-

  1. BFSI

Fintech and traditional financial service companies have adopted AutoML. This has led the industry to address problems like fraudulent transactions, customer churn, and lending risk assessment. Insurance companies have also benefited by optimizing claims management, automating underwriting, preventing fraud, and detecting anomalies.

  1. Healthcare Industry

AutoML has revolutionized the healthcare industry as the introduction of machine learning has aided in medical treatment, diagnosis, research, and management. The models developed through AutoML help diagnose diseases, discover new drugs, optimize healthcare resources, etc.

  1. Marketing

Marketing teams utilize AutoML to identify trends, allowing them to create effective campaigns. ML models help cross-sell and upsell products, optimize ad placements, and perform customer segmentation and product recommendations.

  1. Manufacturing

AutoML has helped manufacturing companies reduce costs, streamline operations, solve supply chain issues, predict stock requirements, etc.

With such widespread adoption across industries, many AutoML tools have emerged. Let's examine the best ones.

Top AutoML Tools to Explore

AutoML Tools

There are several AutoML systems out there that use different techniques to optimize and find the best ML model. A few of the most crucial AutoML tools are as follows-

  1. Google AutoML
  2. Microsoft Azure Automated Machine Learning
  3. AutoKeras
  4. Auto-Sklearn
  5. H20.ai
  6. Databricks AutoML
  7. TIBCO Data Science
  8. AutoKeras
  9. Auto-Py Torch
  10. DataRobot
  11. Amazon SageMaker Autopilot

Now that we have reviewed the core concepts surrounding AutoML, it's time to discuss a crucial topic—will AutoML take over Data Scientists' jobs? Where is the AutoML vs. Data Science heading?

AutoML vs. Data Scientist

Disclaimer: AutoML won't eliminate data scientists

So far, no Doom's Day predictions regarding AutoML replacing data scientists have come true. While it might seem that AutoML is there to take over the job, it has proven to be more of a support than a competitor.

Through AutoML, a data scientist can automate repetitive mundane tasks and focus on jobs requiring high technical skills. Organizations have become more efficient as they have refocused their data scientists on defining business problems and innovating better solutions.

The misconception that AutoML is a threat to data scientists stems mainly from a lack of understanding of how AutoML practitioners and Data scientists differ. The two have several crucial differences.

Different Objectives

  • The inherent flaw in confusing AutoML practitioners and Data scientists is that their fundamental purpose is completely different.
  • AutoML comes into play when an organization or individual wants to try machine learning without investing much time, money, or other resources. This allows them to understand the machine learning landscape, set expectations, identify objectives, and more. AutoML may not be able to provide a full-fledged and highly sophisticated ML solution, but it can give a taste of what a proper solution might look like.
  • On the other hand, data scientists get involved when an organization is completely aware of the ML world, understands how ML can help solve its business problem, knows what kind of ML product it wants to build, and has the ample resources required to do so. Here, data scientists with a deep understanding of machine learning algorithms and other aspects of model building, such as feature engineering and model tuning, can meticulously build sophisticated models. These models are not generic like the ones provided by AutoML but tailor-made to the business's requirements.

Domain Expertise and Experience

  • Data scientists possess domain-specific knowledge and experience, enabling them to understand the context and nuances of the data they work with.
  • This expertise allows them to frame analytical questions, interpret results, and provide actionable insights tailored to specific industries or domains.
  • Such a level of understanding cannot be achieved using AutoML.

Custom Solution Design and Optimization

  • Data scientists can design customized solutions for unique data challenges and business requirements.
  • They can optimize machine learning models and algorithms to improve performance, scalability, and efficiency based on specific objectives and constraints.
  • Conversely, AutoML practitioners are bound by the capability and capacity that the AutoML software provides them.

Innovation in Problem Solving

  • Data scientists excel in tackling novel and complex data problems by applying innovative machine-learning techniques. They deeply understand machine learning principles, algorithms, and methodologies.
  • This knowledge allows them to effectively leverage advanced machine learning techniques and methodologies to solve complex data problems and novel business problems. Also, their creativity, critical thinking skills, and problem-solving abilities enable them to develop new approaches and strategies to solve challenging data scenarios by combining disparate existing ML techniques.
  • Such a level of proficiency that novice AutoML practitioners might lack may make them unable to handle problems that are not traditional.

Model Interpretation and Debugging

  • Data scientists are adept at interpreting model outputs identifying patterns, trends, and insights hidden within the data.
  • They possess the expertise to debug flawed models, diagnose performance issues, and ensure the reliability and fairness of machine learning models.
  • AutoML, conversely, is highly rigid and a complete black box that concerns many companies, especially today, when privacy and AI fairness are hot topics.

Therefore, the simple answer is that AutoML won't make data scientists disappear, and both are here to stay. Whether you aspire to be a citizen data scientist or are a data scientist and wish to reduce your workload and focus on learning highly technical skills, it's time to try AutoML.

And that's a wrap.


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