Amazon Machine Learning | Creating Models Flashcards
What security measures does Amazon Machine Learning have?
Creating Models
Amazon Machine Learning | Machine Learning
Amazon Machine Learning ensures that ML models and other system artifacts are encrypted in transit and at rest. Requests to the Amazon Machine Learning API and console are made over a secure (SSL) connection. You can use AWS Identity and Access Management (AWS IAM) to control which IAM users have access to specific Amazon Machine Learning actions and resources.
Where do I store my data?
Creating Models
Amazon Machine Learning | Machine Learning
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. Data from other products can usually be exported into CSV files in Amazon S3, making it accessible to Amazon Machine Learning. For detailed instructions for configuring permissions that enable Amazon Machine Learning to access the supported data stores, see the Amazon Machine Learning Developer Guide.
Are there limits to the size of the dataset I can use for training?
Creating Models
Amazon Machine Learning | Machine Learning
Amazon Machine Learning can train models on datasets up to 100 GB in size.
How do I know if my data has errors?
Creating Models
Amazon Machine Learning | Machine Learning
You can use Amazon Machine Learning to detect data formatting errors. The data insights feature of the Amazon Machine Learning service console helps you find deeper errors within your data—for example, fields that are empty or contain unexpected values. 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.
What do I do if my data is incomplete or some information is missing?
Creating Models
Amazon Machine Learning | Machine Learning
It is always best to ensure that your data is as complete and accurate as possible. The learning algorithms of Amazon Machine Learning tolerates small amounts of incomplete or missing information without it adversely affecting model quality; as the number of mistakes increases, the resulting model quality will be degraded. Amazon Machine Learning stops processing your model training request if the number of records that fail processing is greater than either 10,000 or 10% of all records in the dataset, whichever comes first.
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. For example, if you find that some rows in an Amazon Redshift table contain invalid values, you can modify the query used to select data for Amazon Machine Learning to exclude these rows.
How do I know if my model is giving accurate predictions?
Creating Models
Amazon Machine Learning | Machine Learning
Amazon Machine Learning includes powerful model evaluation features. You can use Amazon Machine Learning to compute an industry-standard evaluation metric for any of your models, helping you understand these models’ predictive quality. 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. The Amazon Machine Learning service console provides powerful, easy-to-use tools to explore and understand the results of model evaluations.
How do I tune my model if it isn’t giving the results I want?
Creating Models
Amazon Machine Learning | Machine Learning
The best way to increase a model’s quality is by using more and higher-quality data to train it. 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. You can use Amazon Machine Learning to create many prototype models, and you can use the built-in data processors of Amazon Machine Learning to make several common types of feature engineering as simple as editing a line in the built-in “recipe” language. 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. The default settings of Amazon Machine Learning works well for many real-world ML tasks, but can be adjusted as needed by using either the service console or API.
Finally, one important aspect of model tuning to consider is how predictions generated by your ML model are interpreted by your application, to align them optimally with the business goals. Amazon Machine Learning helps you adjust the interpretation cut-off score for binary classification models, enabling you to make an informed trade-off between different kinds of mistakes that a trained model can make. 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. For more information, see Evaluating ML Models in the Amazon Machine Learning Developer Guide.
Can I export my models out of Amazon Machine Learning?
Creating Models
Amazon Machine Learning | Machine Learning
No.
Can I import existing models into Amazon Machine Learning?
Creating Models
Amazon Machine Learning | Machine Learning
No.
