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Amazon Machine Learning to build and deploy Predictive Models
Amazon Machine Learning to build and deploy Predictive Models
Amazon Machine Learning to build and deploy Predictive Models : Amazon Machine Learning, is a fully managed service that makes it easy for any developer to use historical data to build and deploy predictive models. These models can be used for a broad array of purposes, including detecting problematic transactions, preventing customer churn, and improving customer support. This is based on the same highly scalable machine learning technology used by developers across Amazon to generate more than 50 billion predictions a week. Amazon Machine Learning enables to use powerful machine learning technology without requiring an extensive background in machine learning algorithms and techniques. The process of building ML models with Amazon Machine Learning consists of three operations: data analysis, model training, and evaluation. The data analysis step computes and visualizes the data’s distribution, and suggests transformations that optimize the model training process. The model training step finds and stores the predictive patterns within the transformed data. In the optional final step, the model is evaluated for accuracy. Amazon Machine Learning combines powerful machine learning algorithms with interactive visual tools to guide towards easily creating, evaluating, and deploying machine learning models. Its built in data transformations ensure that input datasets can be seamlessly transformed to maximize the model's predictive quality.
Amazon Machine Learning’s APIs and wizards guide developers through the process of creating and tuning machine learning models that can be easily deployed and scale to support billions of predictions. Amazon Machine Learning is integrated with Amazon Simple Storage Service (Amazon S3), Amazon Redshift and Amazon Relational Database Service (Amazon RDS), making it easy for customers to work with the data they’ve already stored in the AWS Cloud.
Amazon Machine Learning to build and deploy Predictive Models
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 Machine Learning allows developers to visualize the statistical properties of the datasets that will be used to “train” the model to find patterns in the data. This saves time by allowing developers to understand data distributions and identify missing or invalid values prior to model training. Amazon Machine Learning then automatically transforms the training data and optimizes the machine learning algorithms so that developers don’t need a deep understanding of machine learning algorithms or tuning parameters to create the best possible model. Using the Amazon Machine Learning technology, a single Amazon developer was able in 20 minutes to solve a problem that had previously taken two developers 45 days to solve – none of these developers had prior experience in machine learning, and both models achieved the same accuracy of 92 percent. Once a model is created, developers can then easily generate batch or real time predictions directly from Amazon Machine Learning without having to develop and manage their own infrastructure to do so.
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