Advantages of Machine Learning Accelerator in Looker
Business users are keen to understand the benefit that can be gained from using machine learning, which offers up potential to obtain more value out of data. On the other hand, the machine learning specialists of today are being bombarded with requests, and the need for their knowledge is high. Imagine for a moment when data analysts were able to develop and evaluate their very own machine learning models.
In our opinion here at Google Cloud, this would make it possible for analysts to speed up the incorporation of predictive analytics into their business intelligence systems. When you develop machine learning models in your Looker environment using controlled data, it can help you convert the insights you gather across all elements of your organization, and it can make those insights accessible to your whole team at the same time.
In the beginning of this year, we launched a new application for Looker called the Machine Learning (ML) Accelerator for Looker. This application connects Looker with BigQuery ML. It does this by making it easier for users to use machine learning tools and by enabling them to utilize trustworthy data in the training and execution of machine learning models. This makes machine learning workflows more efficient.
The program manages all stages of model building, from training to assessment to the formation of inferences, all of which are dependent on the “source of truth” data stored in Looker. Data analysts will be more likely to apply machine learning if they are given the possibility to do so inside their existing business intelligence platforms and without the need of writing any code. This opportunity is provided by Looker. Every Looker instance is capable of having the Machine Learning Accelerator added to it. This add-on may be downloaded for free from the Looker Marketplace.
How exactly does it operate?
It is possible for business customers who depend on Looker, analyst teams that work with BigQuery, and data scientists who rely on Vertex AI to collaborate thanks to the ML Accelerator for Looker’s complete integration with the Google Cloud environment. For instance, a business user may identify a need and then analyze many models in order to narrow down their options until they locate one that satisfies those requirements. After that, they are able to inquire for new data from the BigQuery data team in order to improve the model. After that, a data scientist may take it one step further and put it into production so that the model can be used on a larger scale.
Today, we are pleased to announce the addition of a new ML model type to BigQuery ML called Single Time Series Forecasting utilizing the ARIMA Plus model. We are really thrilled to provide this model type to Looker customers since it is one of the most popular model types available in BigQuery ML. You need just pick a Looker Explore, decide on any measure, and then choose the temporal dimension that corresponds to that metric. Soon, you will have a prediction for that metric. The Time Series Forecasting model type is one that may be used for the purpose of predicting future trends for business metrics such as total revenue or product sales.
Additionally, the ML Accelerator for Looker supports the classification and regression model types, which together cover a broad range of potential applications. Analysts that investigate Looker data regarding previous customer churn may use the same Looker data sets in conjunction with a classification model to forecast the likelihood of future customer turnover. A marketing team that already has data on the return on advertising spend (ROAS) of previous marketing campaigns may use a regression model to estimate the ROAS of future campaigns if they have access to that data.
Both of these kinds of models make use of the Boosted Tree method, which is included in Explainable AI. As a result, they are able to determine which features are most significant in order to accurately forecast the outcome. Our road plan currently includes the addition of support for other BigQuery ML model types; however, if there is a particular kind of model that you are interested in, please get in touch with the Looker account team.
Get going right away with it
Installing the ML Accelerator for Looker from the Looker marketplace is the first step in getting started with it. In addition to this, you may see our demonstration on YouTube, install the accompanying lesson block, or enroll in a training course via Qwiklabs. Looker, BigQuery ML, and Vertex AI are all examples of products that can be used to take advantage of machine learning, and Google Cloud Professional Services can assist your company in getting ready to do so. You may learn more by signing up for a free trial of Looker or getting in touch with your local Google Cloud agent.
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