Amazon Web Services Inc. is lowering the barrier to entry for machine learning with the launch of its new Amazon Redshift ML service, which it made available starting today.
Amazon Redshift ML enables you to build, train, and deploy machine learning models using basic structured query language commands. It works in tandem with Amazon SageMaker, the company’s fully managed machine learning service, so users don’t need to move their data first.
The service is built on Amazon Redshift, a fully managed cloud-based data warehouse service that has already proven to be hugely popular. With Amazon Redshift, users can use SQL to query and combine huge amounts of structured and semi-structured data fed into it from the various databases and applications they use.
Although Amazon Redshift makes data analysis easier, it has always been much more difficult to use the data it contains to train machine learning models, AWS Evangelist Danilo Poccia said in a blog post. .
“What if you wanted to go one step further and process that data to train ML models and use those models to generate insights from the data in your warehouse?” ” He asked. “Previously, you had to export training data from Amazon Redshift to an Amazon Simple Storage Service bucket, then configure and start a machine learning training process. This process required many different skills and usually more than one person to complete. “
Amazon Redshift ML makes life easier because it allows businesses to use SQL statements to build and train models using Redshift data, and then use those models for a range of use cases, including prediction. opt-out or fraud risk assessment, Poccia said.
It works by automatically finding the best model and then tuning it based on the training data available using Amazon SageMaker Autopilot. Users can also select a specific model type if they wish. Users then set their parameters and the service will automatically build, train and deploy the model on the Redshift data.
“You can get predictions from these models trained using SQL queries as if you were calling a user-defined function and take advantage of all the benefits of Amazon Redshift, including massively parallel processing capabilities. Poccia said. “You can also import your pre-trained SageMaker Autopilot, XGBoost, or MLP models into your Amazon Redshift cluster for local inference.”
Constellation Research Inc. analyst Holger Mueller said he believed three trends shaped Amazon’s launch of Amazon Redshift ML. The first and most obvious, he said, is that there aren’t enough data scientists in the world to create all the artificial intelligence and machine learning models it needs. The easiest way to get around this problem is for AI and machine learning to be done by developers who are familiar with SQL, he explained.
“Second, successful machine learning and AI must be close to the data they use, hence the integration with the Amazon Redshift database,” Mueller said.
The third trend noted by Mueller is that Amazon has recently created a number of high profile offerings aimed at making it easier for developers and other customers to use some of the more complex services it provides.
“Amazon is doing it here too, bringing Redshift together with SageMaker and its Neo function which creates the SQL for a machine learning model,” Mueller explained. “These are all key advancements for companies that want to accelerate their business processes and become more agile.”
Amazon said Amazon Redshift ML leverages existing cluster resources for its predictions, which means customers don’t have to worry about additional charges. When creating a model in Amazon Redshift, they only have to pay the associated SageMaker costs, the company added.
Amazon Redshift ML is now available in US East (Ohio), US East (N. Virginia), US West (Oregon), US West (San Francisco), Canada (Central), Europe (Frankfurt), Europe (Ireland ), Europe (Paris), Europe (Stockholm), Asia-Pacific (Hong Kong), Asia-Pacific (Tokyo), Asia-Pacific (Singapore), Asia-Pacific (Sydney) and South America (São Paulo) Regions.
Photo: Tony Webster / Flickr
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