Automated machine learning (AutoML) is a process of making machine learning accessible to non-experts by automating the development of machine learning models. AutoML can process large amounts of data to determine the most accurate models for data analysis.
Because AutoML automates time-consuming, iterative tasks, it saves considerable time. It also eliminates the need for machine learning experts to develop resource-intensive models. As such, many industries are finding more and more ways to use AutoML.
Parameters and hyperparameters
Parameters and hyperparameters are the meta-rules for how an algorithm will learn in AutoML. These can include technical variables such as the number of neurons in a neural network and activation functions.
Hyperparameter optimization controls the learning process, and it avoids the errors common in older, time-consuming methods of manually tuning a machine for new tasks. AutoML software automates the tedious parts of modeling, making data accessible to more than just data scientists.
Additionally, AutoML Tables tell you how much each feature impacts a particular model. The values are provided as a percentage for each feature: the higher the percentage, the more strongly that feature impacted model training.
Traditional AI requires highly qualified and hard-to-find personnel. Across industries, analysts and developers implement AutoML because it enables non-programmers to implement machine learning solutions. Companies can leverage their existing talent without hiring additional data scientists.
In addition to saving time and money, AutoML makes actionable insights available quickly. Companies can use AutoML to analyze customer data automatically, leading to better decisions about marketing and sales. Marketers can determine the best prospects to focus their efforts on while sales teams can make smarter decisions about which leads to follow.
Integration and automated machine learning.
AutoML begins with the automated input of data. This data “trains” the machine, and it can come from sources as big as Salesforce or as small as an Excel spreadsheet. The machine can merge data as necessary to be classified and encoded for the modeling process.
Once the data is entered, automated model selection and training occur. The model selected depends on the type of problem. AutoML then trains on the dataset.
Once the machine is trained, AutoML enables results to be communicated and deployed easily. Because real-world variables change over time, it is important to re-train models as new data emerges.
As you integrate data from more and more sources, you’ll be able to gather more insight into how the models are performing over time. With more data on performance available, you’ll be able to continually re-train and refine the models to arrive at the most accurate predictions for your organization.
Ensemble models are an important feature of AutoML. Instead of using a single model, ensemble learning combines multiple models to improve machine learning results.
AutoML uses voting and stacking ensemble techniques to combine models. In voting, AutoML predicts using probabilities for classification tasks or predicted regression targets for regression tasks. Stacking, on the other hand, combines heterogeneous models. Then it uses the output from individual models to train a meta-model.
What should I look for in automated machine learning tools?
AutoML is becoming a mainstay in many industries. Forbes reports that in 2020, over 76% of organizations prioritized machine learning and AI over other IT initiatives. Another survey found that 83% of IT leaders credit AI and machine learning with transforming customer engagement. Finding the right AutoML learning tools is critical for the future.
Although there are many AutoML tools available, it is important to find one that is fast, accurate, and accessible for all users. The democratization of data within an organization as well as fast insights gives a business a competitive advantage over competitors using traditional AI software.
Domo’s Auto ML provides automatic insights in a way that is accessible to users. Users identify what part of their data they want insights on, and Domo applies hundreds of machine learning models to get fast results.
How do different industries use automated machine learning?
In the last decade, a variety of industries have applied AutoML to find patterns in vast amounts of data. AI solutions, for example, can be prototyped and moved to market more quickly than before. Even smaller companies use it to predict customer behavior.
Insurance companies can use AutoML to simplify workflows, improving actuarial processes. Insurers have more free time for analysis and validation. This ensures that they can justify policies to regulators.
Telecommunications companies can use AutoML to predict which customers will renew their contracts. This may affect the company’s decisions in many areas from marketing to customer service.
AutoML is often marketed as useful for non-experts, which it is, but it also empowers data scientists. AutoML enables data scientists to use many algorithms to find the best models faster.
How will automated machine learning evolve in the future?
AutoML is a field of research that continues to expand rapidly. At the Fifth International Workshop on Automation in Machine Learning, researchers presented innovations that will be used to solve real-world problems. New developments in AutoML are centered largely around hyperparameter autotuning, Neural Architecture Search (NAS), automated assessment of fairness, and automated fake news detection.
Hyperparameter tuning is becoming increasingly common in AutoML frameworks. One of the trickiest parts of AutoML research is determining what hyperparameters to use for a problem. Automating hyperparameter optimization saves time and improves accuracy for machine learning models.
NAS generates, rather than selects, models, making it different from traditional AutoML. It can outperform human-designed architectures. Data scientists are just beginning to use this tool for improving accuracy benchmarks.
Automated assessment of fairness assesses whether an algorithm is working as it should. Researchers are exploring how AutoML can be applied to real-world settings, including criminal justice systems and self-driving cars. Automating assessments in these areas require scientists to confront important questions about standards of fairness in designing them.
One of the most timely applications of AutoML in recent years has been to detect fake news on social media platforms. AutoML detects fake news more efficiently than human agents can, and it has become a vital tool against deadly misinformation during the COVID-19 pandemic.
As researchers continue to refine and expand the capabilities of AutoML, it will become an increasingly important tool for modeling across industries.
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