What is Automated Machine Learning

Automated machine learning, or AutoML, refers to tools and platforms that automate parts of the machine learning workflow — tasks like feature engineering, model selection and hyperparameter tuning — so that people without deep machine learning expertise can build working models. It's used in every industry — manufacturing, finance, health care, information technology, commerce and more — to make model-building faster and more accessible.

In this article, you'll learn what AutoML actually does, how it differs from machine learning and artificial intelligence more broadly, data scientists' roles in the process, and common careers that use machine learning.

Why Is Automated Machine Learning Important?

In science fiction movies and TV shows, the future is overrun by machines and robots that become so smart, they take over the world and threaten to extinguish humanity.

In reality, automated machine learning is created by data scientists so that non-scientists can gather information and make informed decisions.

The “automated” part of automated machine learning, sometimes shortened to AutoML, is important because it takes over some of the more repetitive, expertise-heavy steps of building a machine learning model — trying different algorithms, tuning their settings and engineering useful features from raw data — so people can get to a working model faster. AutoML tools are used across every sector of our economy:

• Banking, finance and insurance
• Health care
• Manufacturing
• Marketing
• Retail
• Sports and entertainment

AutoML enables data analysts to build machine learning models without needing the same depth of specialized training a data scientist has. The automation is designed to remove tedious, error-prone manual work, but it isn't intended to remove people from the process of deciding what problem to solve, cleaning the underlying data, or interpreting the results.

Examples of AutoML platforms

AutoML isn't a single product — it's a category of tools built into major cloud platforms and offered by specialized vendors. Some widely used examples:

1. Google Cloud's AutoML tools

Google offers no-code AutoML capabilities for tabular data, images, text and video, letting users train a model by pointing it at a dataset without writing training code themselves. These tools currently live within Google's broader Gemini Enterprise Agent Platform (formerly known as Vertex AI), reflecting how quickly this part of the industry has consolidated and been renamed in recent years.

2. H2O.ai and DataRobot

Both platforms specialize in automating end-to-end model building for business use cases, handling everything from data preprocessing to model comparison and deployment, with an emphasis on making the process auditable for regulated industries like finance and insurance.

3. Auto-sklearn and PyCaret

These are open-source Python libraries that bring AutoML capabilities directly into a data scientist's existing workflow, automatically searching across models and hyperparameters within the familiar scikit-learn ecosystem.

4. AWS SageMaker Autopilot and Azure Automated ML

Amazon and Microsoft each offer their own AutoML tooling as part of their broader cloud machine learning platforms, aimed at teams that are already building on AWS or Azure infrastructure.

Data Scientists’ Roles in Automated Machine Learning

One of the myths surrounding automated machine learning and artificial intelligence is that they eliminate the need for data scientists. This couldn't be further from the truth. AutoML tools can automate several specific steps in the workflow, but data scientists still play essential roles:

• Data cleansing (aka data cleaning) — Removing incomplete, incorrect, duplicate and corrupt records from a dataset. Most AutoML tools assume reasonably clean input data; getting the raw data into that state is still largely manual work.
• Feature selection — Choosing the variables that go into a dataset. Many AutoML platforms can automate parts of this, but deciding which features are meaningful for a given business problem still benefits from domain expertise.
• Model selection — Selecting a machine learning approach, or mathematical representation of a real-world process. This is one of the steps AutoML tools most directly automate, testing multiple algorithms and comparing performance automatically.
• Parameter selection — Configuring a model's variables, also known as hyperparameter tuning. This is another step AutoML platforms are specifically built to automate.
• Critical analysis — Interpreting results and applying the knowledge to future decisions. No AutoML tool can substitute for a person deciding whether a model's output actually makes sense for the business problem at hand.

How does AutoML differ from AI?

Automated machine learning and artificial intelligence aren't synonymous terms. AutoML is best understood as a tool that sits inside the broader practice of machine learning, which is itself a subset of artificial intelligence. SAS, an analytics software development company, describes the relationship this way: AI is the broad science of mimicking human abilities, while machine learning is a specific subset of AI that trains a machine how to learn. AutoML narrows that further still — it's specifically about automating the process of building a machine learning model, not the broader task of getting a machine to mimic human decision-making.

Think of artificial intelligence as a sophisticated if-then formula. For example, AI can be used in a smart home when the owner connects thermostats, security cameras, smart speakers and other technology. Based on a series of data points, the devices can learn a family’s habits and “make decisions,” such as setting thermostats to different temperatures based on when the home is occupied versus empty.

Careers That Use Machine Learning

There are many career paths for people interested in automated machine learning.

1. Machine learning engineer — an engineer who runs experiments using programming languages (Python, Java, etc.) to build models, design architecture and help computers “learn” autonomously. Requirements: computer science, statistical modeling, data evaluation and modeling, mathematics, data architecture.
2. Data architect — a person who works as part of a data management system and designs blueprints for the system. This is a highly collaborative role that works with data scientists, analysts, business managers and operations managers.
3. Marketing analyst — marketing agencies and departments thrive on automated machine learning to make predictions about their target audiences. Marketing teams rely on AutoML to set pricing, create campaigns and retarget buyers with other sales opportunities.
4. Data scientist — an expert who uses tools such as AutoML and predictive modeling to collect large amounts of data, segment the data and analyze results that businesses and organizations use to make decisions.

Explore Online Data Science Degrees

Throughout this website, you’ll find a wealth of resources, including online programs for earning a bachelor’s degree in data science and master’s in data science and related fields. You can also search data science programs by state to learn more programs available across the states or check out list of master's in data science online programs.