Amazon is giving its machine learning capabilities to developers as an Amazon Web Services offering. According to the company, it makes it easier for developers to use historical data to build and deploy predictive models, which can be used for things like detecting problematic transactions, preventing customer churn, and improving customer support.
The offering is based on the same machine learning technology Amazon’s own developers use, which the company says includes generating over 50 billion predictions a week.
Amazon Machine Learning hooks developers up with APIs and wizards that guide them through creating and tuning machine learning models that can be “easily deployed and scale to support billions of predictions.”
It’s all integrated with Amazon Simple Storage Service (S3), Amazon Redshift, and Amazon Relational Database Service (RDS), so it will work with data that’s already stored in the AWS cloud.
“Until now, very few developers have been able to build applications with machine learning capabilities because doing so required expertise in statistics, data analysis, and machine learning,” Amazon explains. “In addition, the traditional process for applying machine learning involves many manual, repetitive, and error-prone tasks such as computing summary statistics, performing data analysis, using machine learning algorithms to train a model based on data, evaluating and fine tuning the model, and then generating predictions using the model. Amazon Machine Learning makes machine learning broadly accessible to all software developers by abstracting away this complexity and automating these steps. With Amazon Machine Learning, developers can use the AWS Management Console or APIs to quickly create as many models as they need, and generate predictions from them with high throughput without worrying about provisioning hardware, distributing and scaling the computational load, managing dependencies, or monitoring and troubleshooting the infrastructure. There is no setup cost, and developers pay as they go so they can start small and scale as an application grows.”
“Amazon has a long legacy in machine learning. It powers the product recommendations customers receive on Amazon.com, it is what makes Amazon Echo able to respond to your voice, and it is what allows us to unload an entire truck full of products and make them available for purchase in as little as 30 minutes,” adds Jeff Bilger, Senior Manager of Amazon Machine Learning. “Early on, we recognized that the potential of machine learning could only be realized if we made it accessible to every developer across Amazon. Amazon Machine Learning is the result of everything we’ve learned in the process of enabling thousands of Amazon developers to quickly build models, experiment, and then scale to power planet-scale predictive applications.”
Amazon Machine Learning lets developers visualize statistical properties of datasets that will be used to train the model to find data patterns, which the company says saves time by helping devs understand and identify data distributions and missing or invalid values before actually training the model. The training data is automatically transformed, and the machine learning algorithms are optimized so the developers “don’t need a deep understanding” of such algorithms or tuning parameters to come up with the best possible solution.
The offering also includes built-in quality alerts to help developers build and refine models.
According to Amazon, in 20 minutes, one developer was able to use the technology to solve a problem that had taken two developers 45 days to solve. None of them had any experience in machine learning.
Comcast is already using Amazon Machine Learning for data science analytics.
“We particularly liked the ability to visually explore the tradeoff between parameter settings and classification performance during the evaluation,” said Jan Neumann, Manager of a Data Science Research team at Comcast. “With Amazon Machine Learning it was quite simple to prepare and clean the input data and train a model on large data sets in short order.”
Now, if they can just come up with an algorithm for customer service.
On the Amazon Machine Learning site, the company lists the following as popular use cases: fraud detection, document classification, content personalization, customer churn prediction, propensity modeling for marketing campaigns, and automated solution recommendation for customer support.
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