Automation offers substantive benefits as companies look for ways to manage evolving workforces and workplace expectations. More than half of U.S. businesses now plan to increase their automation investment to help increase their agility and improve their ability to handle changing conditions quickly, according to Robotics and Automation News.

Businesses also need to be able to solve problems at scale, something that organizations are increasingly turning to machine learning to do. By creating algorithms that learn over time, its possible for companies to streamline decision-making with data-driven predictions. But creating the models can be complex and time-consuming, putting an added strain on businesses that may be low on resources.

Automated machine learning combines these two technologies to tap the best of both worlds, allowing companies to gain actionable insights while reducing total complexity. Once implemented, AutoML can help businesses gather and analyze data, respond to it quickly and better manage resources.

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AutoML goes a step further than classic machine learning, says Earnest Collins, managing member of Regulatory Compliance and Examination Consultants and a member of the ISACA Emerging Technologies Advisory Group.

AutoML goes beyond creating machine learning architecture models, says Collins. It can automate many aspects of machine learning workflow, which include data preprocessing, feature engineering, model selection, architecture search and model deployment.

AutoML deployments can also be categorized by the format of training data used. Collins points to examples such as independent, identically distributed (IID) tabular data, raw text or image data, and notes that some AutoML solutions can handle multiple data types and algorithms.

There is no single algorithm that performs best on all data sets, he says.

Leveraging AutoML solutions offers multiple benefits that go beyond traditional machine learning or automation. The first is speed, according to Collins.

AutoML allows data scientists to build a machine learning model with a high degree of automation more quickly and conduct hyperparameter search over different types of algorithms, which can otherwise be time-consuming and repetitive, he says. By automating key processes from raw data set capture to eventual analysis and learningteams can reduce the amount of time required to create functional models.

Another benefit is scalability. While machine learning models cant compete with the in-depth nature of human cognition, evolving technology makes it possible to create effective analogs of specific human learning processes. Introducing automation, meanwhile, helps apply this process at scale in turn, enabling data scientists, engineers and DevOps teams to focus on business problems instead of iterative tasks, Collins says.

A third major benefit is simplicity, according to Collins. AutoML is a tool that assists in automating the process of applying machine learning to real-world problems, he says.

By reducing the complexity that comes with building, testing and deploying entirely new ML frameworks, AutoML streamlines the processes required to solve line-of-business challenges.

For machine learning solutions to deliver business value, ML models must be optimized based on current conditions and desired outputs. Doing so requires the use of hyperparameters, which Collins defines as adjustable parameters that govern the training of ML models.

Optimal ML model performance depends on the hyperparameter configuration value selection; this can be a time-consuming, manual process, which is where AutoML can come into play, Collins adds.

By using AutoML platforms to automate key hyperparameter selection and balancing including learning rate, batch size and drop rate its possible to reduce the amount of time and effort required to get ML algorithms up and running.

While AutoML isnt new, evolution across machine learning and artificial intelligence markets is now driving a second generation of automated machine learning platforms, according to RTInsights. The first wave of AutoML focused on building and validating models, but the second iterations include key features such as data preparation and feature engineering to accelerate data science efforts.

But this market remains both fragmented and complex, according to Forbes, because of a lack of established standards and expectations in the data science and machine learning (DSML) industry. Businesses can go with an established provider, such as Microsoft Azure Databricks, or they can opt for more up-and-coming solutions such as Google Cloud AutoML.

There are more tools around the corner. According to Synced, Google researchers are now developing AutoML-Zero, which is capable of searching for applicable ML algorithms within a defined space to reduce the need to create them from scratch. The search giant is also applying its AutoML to unique use cases; for example, the companys new Fabricius tool which leverages Googles AutoML vision toolset is designed to decode ancient Egyptian hieroglyphics.

Technological advancements combined with shifting staff priorities are somewhat driving robotic replacements. According to Time, companies are replacing humans wherever possible to reduce risk and improve operational output. But that wont necessarily apply to data scientists as AutoML rises, according to Collins.

The skills of professional, well-trained data scientists will be essential to interpreting data and making recommendations for how information should be used, he says. AutoML will be a key tool for improving their productivity, and the citizen data scientist, with no training in the field, would not be able to do machine learning without AutoML.

In other words, while AutoML platforms provide business benefits, recognizing the full extent of automated advantages will always require human expertise.

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What is AutoML and Why Should Your Business Consider It - BizTech Magazine