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Spreadsheets are widely used by organizations of all sizes for all kinds of basic and complex tasks.
While simple calculations and graphs have long been part of the spreadsheet experience, machine learning (ML) has not. ML is often seen as being too complex to use, while spreadsheet usage is intended to be accessible to any type of user. Google is now trying to change that paradigm for its Google Sheets online spreadsheet program.
Today Google announced a beta release of the Simple ML for Sheets add-on. Google Sheets has an extensible architecture that enables users to benefit from add-ons that extend the default functionality available in the application. In this case, Google Sheets benefits from ML technology that Google first developed in the open-source TensorFlow project. With Simple ML for Sheets, users will not need to use a specific TensorFlow service, as Google has developed the service to be as easily accessible as possible.
“Everything runs completely on the user browser,” Luiz Gustavo Martins, Google AI developer advocate, told VentureBeat. “Your data doesn’t leave Google Sheets and models are saved to your Google Drive so you can use them again later.”
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Holy sheets, Google’s Simple ML can do what with my spreadsheets?
So what can Simple ML for Sheets do? Two of the beginner tasks in the beta release highlighted by Google include the ability to predict missing values or spot abnormal ones. Martins said that those two beginner tasks are easy for anyone to test the ML waters and explore how ML might benefit their business.
Martins noted that beyond the beginner tasks, the add-on supports several other common ML tasks such as training and evaluating models, generating predictions, and interpreting the models and their predictions. In addition, since Simple ML can export models to TensorFlow, people with programming experience can use Simple ML models with their existing ML infrastructure.
Overcoming the challenges of ML complexity with Simple ML for Sheets
It’s possible for Google Sheets users to benefit from ML without Simple ML, but it may not be easy for the layperson.
“We identified knowledge and lack of guidance as the prime factors for non-ML practitioners to easily use ML,” Mathieu Guillame-Bert, software engineer at Google, told VentureBeat. “Using a classical ML tool, like TensorFlow in Python, is like being in front of a blank page.”
Guillame-Bert said that using a classic ML tool requires, among other things, for the user to understand programming, ML problem framing, model construction and model evaluation. He noted that such knowledge is generally acquired through classes or self-taught over a long period of time.
In contrast, Guillame-Bert said that Simple ML is like an interactive questionnaire. It guides the user and only assumes basic knowledge about spreadsheets.
Using decision forests to power Simple ML
Martins explained that under the hood, the Simple ML add-on trains models using the Yggdrasil Decision Forests library. This is the same library that powers TensorFlow Decision Forests.
“For this reason, once trained in the add-on, the advanced user can export the model to any TensorFlow Serving managed service, such as the TensorFlow Serving on Google Cloud,” Martins said.
Guillame-Bert explained that TensorFlow Decision Forests (TF-DF) is a library of algorithms to train new models. In other words, the user provides examples to TF-DF, and they receive a model in return. He noted that TF-DF does not come with pretrained models; however, because TF-DF are integrated in the TensorFlow ecosystems, advanced users may combine Decision Forests and pretrained models.
According to published research, the technology behind TF-DF, which is based on the concepts of Random Forests and Gradient-Boosted Trees, works exceptionally well to train models on a tabular dataset, like a spreadsheet.
Looking forward, Guillame-Bert said Google will be working to further improve the usability of the add-on. Google also plans on adding new capabilities to Simple ML for Sheets that don’t require any ML knowledge from the user.
“During internal tests, we identified several highly requested tasks we think will be popular with users,” Guillame-Bert said. “We hope to get feedback from this public launch to prioritize and design those tasks.”
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