Welcome to Codingcompiler. Amazon Machine Learning is part of Amazon Web Services (AWS) and enables machine learning on an infrastructure provided in the cloud. With Amazon Machine Learning, you can find large-scale patterns, derive machine-learning models, and generate forecasts.
What is Amazon Machine Learning?
Amazon Machine Learning enables machine learning using Amazon Web Services (AWS). AWS provides the tools, applications, and compute resources you need in the cloud. No own infrastructure is needed.
With Amazon Machine Learning, you can analyze and model data, derive mathematical machine learning models, and generate forecasts. Amazon Machine Learning provides the foundation for the development of Artificial Intelligence (AI) applications . Deep specialist knowledge in the field of machine learning and the algorithms or techniques used are not necessary for the development of applications.
Machine Learning (ML) is used, for example, for customer forecasts, fraud detection or transaction analyzes. To work with Amazon Machine Learning, you can use the AWS Management Console, APIs, and various visualization tools.
Running within the Amazon Web Services makes machine learning highly scalable. Many data can be analyzed in real time at high speed and used to generate forecasts. Individual forecasts can be retrieved via a real-time API.
Bulk requests can be transmitted via batch API. The payment model for Amazon Machine Learning is usage-dependent. Customers only have to pay for the services actually used.
[Related Article: Amazon Web Services Cheat Sheet]
The basic concept of Amazon Machine Learning
The basic process of generating forecasts using Amazon Machine Learning consists of three steps. These are:
- The data analysis,
- The training of a model and
- The evaluation of the result.
In the data analysis phase , Amazon Machine Learning calculates the distribution of the data and prepares it for the model training process. During the model training phase, certain patterns are searched for in the data. In the final step, the results and model are evaluated for precision.
To perform the various steps, Amazon Machine Learning combines a variety of powerful machine learning algorithms with interactive visualization tools. They help generate the models and evaluate the results. Integrated data transformation capabilities ensure that the data assets to be analyzed by the model provide optimal results and predictions.
[Related Article: AWS Cloud Support Engineer]
Integration of Amazon Machine Learnings into Amazon Web Services
Amazon Machine Learnings is fully integrated with Amazon Web Services (AWS). AWS provides the machine learning platform. Very easy is the connection of data that is already stored in AWS.
The data to be analyzed can be delivered through web services such as the Amazon Simple Storage Service (S3), Amazon Redshift and Relational Database Service (RDS). In addition, it is possible to integrate data from other Amazon Web Services via CSV files and Amazon S3 buckets. The AWS management console and the Amazon Machine Learning API allow visualization of the data.
Access methods to Amazon Machine Learning
Amazon Machine Learning supports a variety of access methods, including interactive, single, or mass queries. The Amazon Machine Learning Console can be accessed directly from the AWS Management Console. In addition, the AWS CLI (Command Line Interface) provides a command-line interface.
Further access options are available through the APIs of Amazon Machine Learning. They provide ways to automate the creation, modeling, and management of data sources, models, forecasts, and assessments. The real-time API delivers high-throughput, low-latency single predictions. Using the batch API, it is possible to retrieve many records and generate all forecasts in one operation.
[Related Article: Python For Machine Learning]
Benefits of using Amazon Machine Learning
The use of cloud-based Amazon Machine Learning offers several advantages. Models can be easily created and optimized using data from wizards. Scalable algorithms, data processors and interactive tools help build and refine the models. From an existing model, it is possible to generate forecasts for specific applications within a very short time. There is no cost or time to plan, build and operate your own machine learning infrastructure . Amazon Web Services provide high availability and performance to generate large quantities of forecasts in a short time.
[Related Article: What Is Amazon Kinesis Data Streams?]
Amazon Machine Learning Applications
Amazon Machine Learning offers countless applications. It can be used to:
- Analyze and predict customer behavior
- Recognize message content such as spam messages and process or forward appropriately,
- Predict quantities and intervals of customer service inquiries,
- to recognize and prevent fraudulent transactions
- Personalize web services for customers
- to conduct targeted marketing campaigns as well
- Classify documents