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License: MIT License
Collection of data science and machine learning examples with Exasol
License: MIT License
Add an example for the SageMaker guide on how to use the SageMaker model from inside of the Exasol database.
Add an example for connecting from AWS Sagemaker to Exasol.
Test Procedure:
File error_code_config.yml
contains invalid yaml syntax.
Please compare contents of the corresponding file for project-keeper for reference.
Add a Jupyter Notebook example showcasing invoke a trained AzureML model from Exasol Database via UDF.
This should include all functionality and key-words.
Builds on #40
Might be similar to UseSagemakerModelFromExasol.ipynb .
Requirements:
Pip installs tensorflow 2 if the version is not pinned. However, the UDF still uses version 1.3.
Add example for training Sagemaker model with data exasol.
Add a Jupyter Notebook example showcasing how to apply a simple ML algorithm from AzureML on data from an ExasolDB by training and testing the model.
This should include all functionality and key-words.
Builds on #39
Might be similar to TrainSagemakerModelWithExasolData.ipynb .
Add graphics/screenshot, proper layout and full formulations to tutorial made in #40 , so it is ready for use.
exaslct.log
=> main.log
Add a example for the SageMaker guide on how to load example data.
As mentioned here, we directly import of the header of the scania trucks data set into exasol at the moment, and as a result use
all_columns = exasol.export_to_pandas("SELECT * FROM IDA.TRAIN LIMIT 1;")
for the reading of the header, which is not great.
please update in the three files mentioned below by using this or this:
we import the data using this notebook from sagemaker tutorial for the data import which needs to be changed, and then the data is read in the sagemaker tutorial and in the azureml tutorial
Currently, the BucketFS connection information get embedded into the UDF code (classification.ipynb). A much cleaner method is, to provide this information via a connection. Either, by providing the BucketFS URL directly, or by providing a path to a configuration file in the BucketFS.
Currently there is a bug in the saas online interface where the path of bucketfs files is shown incorrectly. this is mentioned in the azureml tutorial. once this bug is fixed, remove the two mentions of it.
We want to reuse the LoadExampleDataIntoExasol.ipynb for the AzureML tutorial.
Add some explanation to https://github.com/exasol/data-science-examples/blob/main/tutorials/machine-learning/python/sagemaker/LoadExampleDataIntoExasol.ipynb to make it more reader friendly.
Add graphics/screenshot, proper layout and full formulations to tutorial made in #39, so it is ready for use.
Add a Jupyter Notebook example showcasing how to connect AzureML to an Exasol Database and access the data from ExasolDB in AzureML.
This should include all functionality and key-words.
Might be similar to ConnectSagemakerToExasol.ipynb and LoadExampleDataIntoExasol.ipynb
Add graphics/screenshot, proper layout and full formulations to tutorial made in #41 , so it is ready for use.
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