/ An executive’s guide to automated machine learning

An executive’s guide to automated machine learning

What is automated machine learning?

Automated machine learning (AutoML) is a tool of predictive analytics. It creates and tests models to make insights about data available to non-experts. AutoML saves time by automating iterative tasks. Typically, AutoML is used for forecasting, but it can also be used for classification and regression.

AutoML uses parameters and hyperparameters as the meta-rules for how algorithms learn. They include any important variables. Hyperparameter optimization eliminates some of the most tedious aspects of modeling by fine-tuning models to fit data sets exactly.

 

 

Supervised vs. Unsupervised learning

In machine learning, the majority of methods can be classified as supervised learning or unsupervised learning. Which method works best depends on the question being asked as well as the data being assessed.

Supervised learning is applied to datasets that contain explanatory variables and target responses. The algorithms learn a function that represents the relationship between the variables and target responses. The purpose of supervised learning is to be able to make generalizations about new and uncollected data.

In other words, supervised learning is used to create predictive models. Supervised learning can also be used for classification and regression problems.

 

 

Examples of supervised learning algorithms include random forest, support vector machines (SVM), decision trees, and logistical regression.

Unsupervised learning, on the other hand, uses machine learning algorithms to group unlabeled data sets. This is the primary distinction from supervised learning.

In unsupervised learning, algorithms analyze data for patterns without human oversight. Because it can be applied to unlabeled data sets, unsupervised learning is used to fill in gaps in domain knowledge.

Examples of unsupervised learning techniques include clustering, association, and dimensionality reduction. Clustering, a data mining technique, groups unlabeled data based on similarities. Association similarly finds relationships between variables in a data set. Dimensionality reduction reduces a large number of inputs to a smaller, more manageable size while still preserving data’s integrity.

 

How can executives use automated machine learning?

Studies suggest that AutoML is actually accelerating time to value. Customers are realizing the value that AutoML adds to their businesses ever more quickly as time goes on because it enables data-driven decision making in every area of business.

AutoML provides actionable insights in everything from marketing and sales to supply chain and operations. As a tool of predictive analytics, AutoML is important in marketing. It can be used to improve customer engagement through behavioral marketing campaigns on social media. Ultimately, many companies are able to use AutoML to use their marketing resources more efficiently to improve their ROI.

 

 

AutoML is an invaluable tool for supply chain management as well. Because AutoML can identify seasonality and trends, it can improve model performance and accuracy accordingly. This affects everyone from shippers to carriers and retailers. AutoML forecasts can help retailers anticipate staffing and inventory needs and help manufacturers know when it may be time to step up or slow down production.

 

Automated machine learning for analysts

Previously, data science was only accessible to highly-trained experts. Automated machine learning tools have changed that by making data science insights available to people who have a deep knowledge of their data but lack data science expertise.

Now analysts don’t need a data science or technical background to automatically integrate, clean data, and run models. Plus, keeping models up to date with new data is simplified.

Because AutoML makes data more accessible, analysts can surface insights for decision makers. No more technical manual work is necessary to gain the real-time insights needed for intelligent action.

 

 

Key pieces in automated machine learning tools

Accessible insights.

Automated machine learning tools should be fast, accurate, and accessible to non-experts. AutoML tools should make actionable insights readily available to users across an organization. After all, an AutoML tool is only valuable insofar as it can enable data-driven decisions that give a company a competitive advantage.

Data input management.

It’s important to remember that AutoML tools, especially those created for supervised learning, require clean data input, with data structured in rows and columns. As such, data preparation is a critical part of data workflows with AutoML.

Once your data is prepared, you can use a tool like Domo’s AutoML. It can ingest data from thousands of different sources and help support your analysis.

Reinforcement learning.

Reinforcement Learning is a type of Machine Learning. In Reinforcement Learning, a machine learns through exploring the consequences of its actions in an environment. It provides data analysis feedback that directs the user to the optimal result.

Reinforcement Learning is an incremental add-on to supervised learning models. Those supervising the machine learning have the ability to influence a given model or algorithm based on actions that are taken based on the primary supervised model.

 

 

How do different industries use automated machine learning?

AutoML is changing innumerable industries by making fast and accurate data analysis available to non-data scientists. Although it doesn’t eliminate the need for data scientists, AutoML gives business analysts and executives more tools for improving operations and, ultimately, boosting revenue. It is a critical tool for gaining a competitive advantage in quickly changing markets.

Supervised learning is far and away the most commonly used form of AutoML. One recent survey showed that 82% of respondents used supervised learning tools.

Supervised learning AutoML is used for fraud detection in banking. Credit card companies, for example, use AutoML to flag transactions that are likely fraudulent. As more data is gathered, learning can be reinforced to improve efficiency and accuracy.

 

 

Medical device manufacturers also have found multiple uses for AutoML. Arthrex Inc., which designs and manufactures orthopedic surgical devices, used Domo AutoML to run hundreds of models simultaneously in order to avoid delays in their product launch lifecycle. AutoML enabled Arthrex to do this by automating the most time-consuming aspects of modeling and avoiding subsequent analysis paralysis.

While AutoML is a significant innovation for making data analysis available to non-experts, data scientists benefit from it as well. AutoML empowers data scientists to work quickly through hundreds of algorithms and tuning solutions. Data Scientists can then determine a reliable baseline and work faster towards the best models.

 

How will automated machine learning evolve in the future?

 

 
As demand for AutoML grows, tools continue to develop to meet demand. Big data allows companies access to vast amounts of information that, once sorted, AutoML can be used to hyper-personalize marketing strategies and products.

AutoML makes data analytics accessible to non-experts. Platforms like Domo’s AutoML allow users to create and run models using data sets, freeing up data scientists to focus on bigger projects and research that will impact key business processes.

Check out some related resources:

Artificial intelligence: The future of BI reporting tools

Natural language processing and data science workflows

Understanding different machine learning techniques

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