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Understanding Automated Machine Learning

Published: May 26, 2022
Editor at Plat.AI
Editor: Ani Mosinyan
Reviewer at Plat.AI
Reviewer: Alek Kotolyan

As AI evolves, real-time data analytics is becoming the standard for businesses seeking to remain relevant. This drives the interest in AI tools and techniques that increase analytical efficiency. One of these tools is Automated Machine Learning.

Woman touching smart tech interface.

In our blog post we have introduced the idea of Automated Machine Learning and its applications. Hereinafter, let’s expand on the arguments and implications surrounding it. For this purpose, we have compiled a comprehensive list of the pros and cons of Automated Machine Learning. 

What is AutoML?

Automated Machine Learning, often abbreviated as AutoML, streamlines the process of developing machine learning models. It is available as open-source libraries, which include predefined scripts for machine learning routines, or as platforms and software featuring intuitive interfaces that facilitate data analysis insights.

AutoML varies in its capacity to automate the modeling process, handling a range from partial to almost complete automation depending on the problem’s complexity. Unlike Manual Machine Learning, which typically involves a repetitive cycle of revising steps for optimization by data scientists, AutoML aims to simplify and expedite the model development cycle.

How Does AutoML Work?

AutoML streamlines machine learning model development through these steps:

  1. Problem Definition: Data scientists define the problem and goal, a step requiring significant domain knowledge and not fully automatable.
  2. Data Pre-processing: AutoML aids in tasks like data cleaning and preparation, though the extent of automation depends on the specific problem.
  3. Feature Engineering: AutoML simplifies adding and selecting features by testing various combinations automatically.
  4. Model Selection and Training: Unlike manual methods, AutoML evaluates multiple algorithms at once, allowing selection of the best performer without iterative testing.
  5. Hyperparameter Optimization: AutoML automates the tuning of hyperparameters to enhance model performance.
  6. Deployment: AutoML simplifies deployment to a one-click process, making the model readily available for use.
  7. Monitoring: AutoML platforms offer real-time performance tracking through dashboards or reports.

When to Use AutoML?

AutoML simplifies and enhances various machine-learning tasks by automating the selection, composition, and parameterization of machine-learning models. Here’s how it is applied across different domains:

Classification Tasks

In classification tasks, AutoML automates the process of choosing the best model and tuning its parameters to classify data into predefined categories. This is particularly useful in applications like spam detection, where emails are classified as spam or not spam, or in medical diagnosis, where diseases are classified based on symptoms or imaging data. AutoML evaluates multiple algorithms (such as decision trees, support vector machines, and neural networks), optimizes their hyperparameters, and selects the model that best classifies the input data.

Regression Analysis

AutoML is applied in regression tasks to predict a continuous quantity, such as house prices or stock market trends. It automates the model selection and tuning process, identifying the best fitting model that minimizes error between the predicted and actual values. By evaluating various regression algorithms (like linear regression, random forests, or gradient boosting machines), AutoML finds the model that best captures the relationship between the dependent and independent variables.

Forecasting Time-Series Data

In time-series forecasting, AutoML is used to predict future values based on past data, crucial in finance for stock prices forecasting, in meteorology for weather prediction, and in the supply chain for demand forecasting. AutoML systems can handle seasonality, trends, and patterns in time-series data, selecting models that can best forecast future data points. Techniques such as ARIMA (AutoRegressive Integrated Moving Average), LSTM (Long Short-Term Memory Networks), and other deep learning models are automatically evaluated and optimized.

Computer Vision Applications

AutoML revolutionizes computer vision by automating the process of feature extraction, model selection, and tuning for tasks like image classification, object detection, and image segmentation. It enables non-experts to deploy complex computer vision models that require minimal manual intervention. For instance, AutoML can identify the most effective neural network architecture (like CNNs – Convolutional Neural Networks) and optimize its layers and parameters to improve accuracy in identifying objects within images or videos.

Natural Language Processing 

In NLP, AutoML simplifies text analysis tasks such as sentiment analysis, text classification, and language translation. It automates the selection of models (like RNNs – Recurrent Neural Networks or Transformers) and the preprocessing of text data (tokenization, embedding, etc.), making it easier to derive meaningful insights from large volumes of text. AutoML tools can also fine-tune models on specific datasets, enhancing their ability to understand and generate human-like text.

What Are the Advantages of AutoML?

AutoML has several advantages, a few of which we will discuss below. It can help you save resources, reduce your time on the market, improve performance, and increase accessibility by your team.

Saving Resources

Automation can be viewed as a gadget that accelerates the workflow of seasoned data scientists and allows more time for solving complex and innovative problems as opposed to performing rote tasks. But most importantly, it maximizes the data mining capacities of a business by making the machine learning process tenfold more efficient. Ultimately, less time and capital are required to build a model with AutoML.

Currently, there is an acute shortage of specialists who possess the depth of knowledge and skill required to handcraft ML models. Considering this, it is oftentimes more affordable and logical to outsource your AI solutions and make a one-off investment into an AutoML tool, rather than maintaining a team of data scientists in-house.

Reducing Time to Market

Boosting the speed of model research, automation cuts down the time to market for model implementation. Although some may argue that AutoML models rank lower than hand-tuned ones, they are not inferior in performance to conventional machine learning models. Moreover, when it comes to competition, it is crucial to get your foot in the door first and fast, then adjust later. 

Improving Performance

An AutoML system creates many model pipelines with different algorithms and returns the best results. Automation also eliminates the chances for human error, addresses human bias, reinforces the replicability of the analyses, and promotes collaboration. 

Automation thus enhances not only the speed but also the quality of the research, improving both the efficiency and the effectiveness of machine learning model development.

Increasing Accessibility

An automated machine learning platform can be operated by your data or business analyst or anyone, irrespective of their level of data expertise. Thus, businesses don’t have to hire additional specialized staff or spend money on employee training. 

Now that machine learning is automated, as well as more resource-efficient, more companies can consider using it to attain their business goals.

What Are the Limitations of AutoML?


Let’s take a look at the flip side of the matter to weigh out all the pros and cons of ML automation.

Processing Capacity

Before choosing the best algorithm, an Automated Machine Learning system may try nearly every model out there. This process consumes a substantial amount of computational power. Conversely, in the manual version of the process, the data scientist initially eliminates many certainly unsuitable algorithms from consideration. 

Nevertheless, not all AutoML platforms work with the multi-pipeline principle, and often, the available processing capacities suffice for machine learning operations.

Flexibility and Precision

AutoML frameworks are set up to accommodate most datasets, but this generalization may still leave out datasets that are uncommon in one way or another. As a rule, Automated Machine Learning does a pretty good job in most cases, but for certain problems, it may not be flexible enough to match the precision and the persistence of the manual approach. 

Nonetheless, this statement is a generalization on its own, as everything depends on the issue and the dataset, as well as on the portions of the model development we are executing through AutoML. Furthermore, machine learning automation continuously advances and adapts to cover the gaps of inflexibility and facilitate more tasks involved in model building.

Model Explainability

Model explainability is one of the most important points to address when considering the pros and cons of machine learning automation.

Machine learning, as a problem-solving or decision-making tool, does not exist in a vacuum but should be seamlessly integrated into business processes. It is imperative to ensure that AI solutions are translated into comprehensive terms for the stakeholders, who should know not only what to do, but also why. For this purpose, model explainability is key. Machine learning transparency enables replication, collaboration, and knowledge transmission. 

Due to a knowledge gap, it may be difficult for AI engineers or data scientists to explain to non-experts how an algorithm delivers an outcome. The latter coupled with poor model interpretation creates a vague understanding of AI processes and generates distrust towards their results and the technology. This phenomenon is called the “black-box effect.” AI has been long subject to black box criticism.

One of the opinions is that AutoML reduces black box criticism by making ML and AI available to anyone regardless of their data competence. With the user-friendly interface and buttons labeled with natural language, technology may seem less mysterious. 

However, if we tackle the actual explainability of AutoML processes happening behind the clicks, automation may as well aggravate the black-box effect. Depending on its level, many processes can be hidden in the background of the Automated Machine Learning workflow, which introduces more possibilities for bias. Hence, an AutoML user should be especially careful to account for any domain-specific or dataset-related biases.

With AutoML, model development might get so easy, fast, and accurate that we might not even need to understand what the system does or why it brings about this result. In fact, if we lose track of model interpretation, we may easily jump to the wrong conclusions. To put it short – the higher the level of automation, the more opportunity for AutoML to exacerbate the issue of a black box model. 

But fear not. This is where data scientists come into play. 

There is a common misunderstanding that machine learning automation might replace data scientists. If we underestimate the role of data professionals, we run the risk of losing all model commands and explainability. A human should remain in the loop of model development to keep track of its logical chain, maintain control, assess, and oversee the process without having to perform boring repetitive tasks.

To summarize all the pros and cons of AutoML, at least for now, it cannot serve as a replacement for human knowledge; thus, the best practice would be achieved with the human–machine (AutoML) collaboration. 

What Are Some Examples of AutoML Applications?

AutoML finds diverse applications across sectors:

Finance: AutoML aids in detecting credit card fraud, assessing risks, and predicting real-time gains and losses in investments.
Healthcare and Medical Research: With the adoption of electronic health records, AutoML facilitates early detection and diagnosis of diseases like liver and kidney ailments, diabetes, and cancer, improving treatment outcomes. It also predicts patient deterioration.
Marketing: AutoML analyzes customer data and advertising effectiveness to predict sales, enhance profits, and boost customer satisfaction.
Agriculture: AutoML uses satellite imagery analysis to predict crop quality and optimize irrigation practices.
Manufacturing: AutoML helps predict delivery delays, estimate seasonal demand, ensure product quality, enable predictive maintenance, and reduce production costs.

Automated Machine Learning + Plat.AI

Besides custom modeling solutions, Plat.AI offers a fully operational Automated Machine Learning software that creates real-time models and estimates outcomes based on the uploaded data. It requires no coding experience and offers seamless integration with current lending and marketing data sources. 

Our software offers:

  • Access to training guides to help you maximize the software’s functionality.
  • Professional consulting for custom model building, and 24/7 support for technical difficulties.
  • Lower latency for real-time predictions and decisions in under 200 milliseconds.
  • Easy model integration with any of your current systems and platforms.
  • Quick responses and model performance monitoring.

Why should you choose Plat.AI? There are several reasons:

  • Ideal for users with limited coding experience.
  • Affordable compared to currently available tools.
  • Easy and fast to build and integrate.
  • Fully interpretable results.
  • Real-time predictions in milliseconds.
  • Sophisticated monitoring tools.
  • Support and consultation services for the duration of your contract.

Using Plat.AI’s service is simple, and only requires a few steps:

  1. Define your objectives and select your target data.
  2. Upload and process your data.
  3. Build and evaluate your model.
  4. Turn predicted values into an actionable scoring system.
  5. Easily integrate your new model into your current system.

We are quick, efficient, and secure. At Plat.AI, we work hard to enhance your team’s analytical capabilities and provide you with the best-in-class AI solutions.

Sophia Beglaryan

Sophia Beglaryan

Data AnalystSophia Beglaryan is a data analyst for Plat.AI. Sophia is an avid learner, ever curious and fascinated about a wide array of topics: from natural sciences to arts and crafts, foreign languages, and beyond. Sophia has experience translating, as well as writing, various content in English, Armenian, Russian and French.

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