AI News, Amazon Machine Learning for Human Activity Recognition

Amazon Machine Learning for Human Activity Recognition

Nowadays it's possible for anyone to exploit the huge volumes of information available through big data and open datasets, whether you are a data scientist, an enterprise developer, or a small startup.

Amazon Machine Learning as a service offers great potential in making applications smarter, by making it easy to use for developers of all skill levels.

You will walk through the whole process, from the dataset analysis and Datasource creation, all the way to model training/evaluation, and execute a Python script to generate real time predictions.

API-driven services bring intelligence to any application

Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.

The Most Complete Platform for Big Data

AWS gives you fast access to flexible and low cost IT resources, so you can rapidly scale virtually any big data application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing.

Amazon Machine Learning FAQs

You can use Amazon Machine Learning to read your data from three data stores: (a) one or more files in Amazon S3, (b) results of an Amazon Redshift query, or (c) results of an Amazon Relational Database Service (RDS) query when executed against a database running with the MySQL engine.

Amazon Machine Learning will be able to train ML models and generate accurate predictions in the presence of a small number of both kinds of data errors, enabling your requests to succeed even if some data observations are invalid or incorrect.

To correct incomplete or missing information, you need to return to the master datasource and either correct the data in that source, or exclude the observations with incomplete or missing information from the datasets used to train Amazon Machine Learning models.

You can also use Amazon Machine Learning to ensure that the model evaluation is unbiased by choosing to withhold a part of the training data for evaluation purposes, ensuring that the model is never evaluated with data points that were seen at the training time.

Adding more observations, adding additional types of information (features), and transforming your data to optimize the learning process (feature engineering) are all great ways to improve the model’s predictive accuracy.

Additionally, Amazon Machine Learning can automatically create a suggested data transformation recipe based on your data when you create a new datasource object pointing to your data—this recipe will be automatically optimized based on your data contents.

Amazon Machine Learning also provides several parameters for tuning the learning process: (a) target size of the model, (b) the number of passes to be made over the data, and (c) the type and amount of regularization to apply to the model.

For example, some applications are very tolerant of false positive errors, but false negative errors are highly undesirable—the Amazon Machine Learning service console helps you adjust the score cut-off to align with this requirement.

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