Calibration Assignment Submission (Azure Machine Learning) - Revision history

Zhu6: /* Comparison */

2016-09-09T04:32:17Z

Comparison

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	Terms used in the above comparison		Terms used in the above comparison

	[~~[Amazon Web Services\|AWS~~ 3]]: Amazon Simple Storage Service (Amazon S3), provides developers and IT teams with secure, durable, highly-scalable object storage<ref name="Amazon Simple Storage Service (S3) - Object Storage">[https://aws.amazon.com/s3/ "Amazon Simple Storage Service (S3) - Object Storage"]</ref>.		[https://en.wikipedia.org/wiki/Amazon_Web_ServicesAWS 3]: Amazon Simple Storage Service (Amazon S3), provides developers and IT teams with secure, durable, highly-scalable object storage<ref name="Amazon Simple Storage Service (S3) - Object Storage">[https://aws.amazon.com/s3/ "Amazon Simple Storage Service (S3) - Object Storage"]</ref>.

	Azure Machine Learning provides more flexibility in terms of the data formats that can be imported into its environment. It also supports more data visualization methods as compared to Amazon Machine Learning. The missing value imputaion can also be customized.		Azure Machine Learning provides more flexibility in terms of the data formats that can be imported into its environment. It also supports more data visualization methods as compared to Amazon Machine Learning. The missing value imputaion can also be customized.

Zhu6: /* Comparison */

2016-09-09T04:31:50Z

Comparison

← Older revision		Revision as of 04:31, 9 September 2016
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	Google Spreadsheets		Google Spreadsheets

	[[HTTPS]] requests		[https://en.wikipedia.org/wiki/AWS_S3 HTTPS] requests

	API update calls		API update calls
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	Web URL		Web URL

	Hadoop [[HiveQL]]		Hadoop [https://docs.treasuredata.com/articles/hive HiveQL]
	\|-		\|-
	\|Data formats		\|Data formats
	\|[[CSV file]]		\|[https://en.wikipedia.org/wiki/CSV_file CSV file]
	[~~[Amazon S3\|~~S3]] and [~~[Amazon Redshift\|~~Redshift]] database		[https://en.wikipedia.org/wiki/Amazon_S3 S3] and [https://en.wikipedia.org/wiki/Amazon_Redshift Redshift] database
	\|Text file		\|Text file
	Spreadsheet		Spreadsheet

	[[JSON]]		[https://en.wikipedia.org/wiki/JSON JSON]
	\|CSV and text files		\|CSV and text files
	[https://docs.treasuredata.com/articles/hive Hive SQL] tables		[https://docs.treasuredata.com/articles/hive Hive SQL] tables
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	[http://www.cs.waikato.ac.nz/ml/weka/arff.html arff]		[http://www.cs.waikato.ac.nz/ml/weka/arff.html arff]

	[~~[Zip~~ (~~file format~~)\|zip]]		[https://en.wikipedia.org/wiki/Zip_(file_format) zip]

	[http://fileinfo.com/extension/rdata RData]		[http://fileinfo.com/extension/rdata RData]
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	\|100GB		\|100GB
	\|text file: 2.5 GB		\|text file: 2.5 GB
	[[HTTP request]]: 2 MB		[https://en.wikipedia.org/wiki/HTTP_request HTTP request]: 2 MB
	\|10 GB		\|10 GB
	\|-		\|-
	\|[~~[Data type\|~~Data types]]		\|[https://en.wikipedia.org/wiki/Data_type Data types]
	\|boolean		\|boolean
	categorical		categorical
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	string		string
	\|-		\|-
	\|[[Data visualization]]		\|[https://en.wikipedia.org/wiki/Data_visualization Data visualization]
	\|yes		\|yes
	table view		table view
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	\|yes		\|yes
	\|-		\|-
	\|[[Feature extraction\|Feature normalization]]		\|[https://en.wikipedia.org/wiki/Feature_extraction Feature extraction\|Feature normalization]
	\|yes		\|yes
	\|no		\|no
	\|yes using [~~[Principal component analysis\|~~PCA]]		\|yes using [https://en.wikipedia.org/wiki/Principal_component_analysis PCA]
	\|-		\|-
	\|[[Orthonormalization]]		\|[https://en.wikipedia.org/wiki/Orthonormalization Orthonormalization]
	\|no		\|no
	\|no		\|no
	\|yes		\|yes
	\|-		\|-
	\|[~~[Imputation~~ (statistics)\|Missing value imputation]]		\|[https://en.wikipedia.org/wiki/Imputation_(statistics) Missing value imputation]
	\|yes indirectly chain feature		\|yes indirectly chain feature
	imputation model with main		imputation model with main

Zhu6: /* Comparison */

2016-09-09T04:28:30Z

Comparison

← Older revision		Revision as of 04:28, 9 September 2016
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	[https://aws.amazon.com/redshift/ AWS Redshift]		[https://aws.amazon.com/redshift/ AWS Redshift]

	[[AWS S3]] table		[https://en.wikipedia.org/wiki/AWS_S3 AWS S3] table

	\|Text file uploaded into		\|Text file uploaded into

Zhu6: /* Azure Marketplace */

2016-09-09T04:27:50Z

Azure Marketplace

← Older revision		Revision as of 04:27, 9 September 2016
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	[https://datamarket.azure.com/dataset/aml_labs/log_regression Binary classifier API]		[https://datamarket.azure.com/dataset/aml_labs/log_regression Binary classifier API]

	Binary Classifier API is an example built with Microsoft Azure Machine Learning that fits a [~~[Logistic regression\|~~logistic regression model]] to data input by the user and then outputs the predicted value for each of the observations in the data.		Binary Classifier API is an example built with Microsoft Azure Machine Learning that fits a [https://en.wikipedia.org/wiki/Logistic_regression logistic regression model] to data input by the user and then outputs the predicted value for each of the observations in the data.

	==Comparison==		==Comparison==

Zhu6: /* Azure Machine Learning Studio */

2016-09-09T04:27:16Z

Azure Machine Learning Studio

← Older revision		Revision as of 04:27, 9 September 2016
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	==Azure Machine Learning Studio==		==Azure Machine Learning Studio==

	[https://azure.microsoft.com/en-us/documentation/articles/machine-learning-what-is-ml-studio/ Azure Machine Learning Studio] is an interactive, visual workspace to build, test, and iterate on a [[predictive analytics]] model. Following are the components of the Machine Learning Studio:		[https://azure.microsoft.com/en-us/documentation/articles/machine-learning-what-is-ml-studio/ Azure Machine Learning Studio] is an interactive, visual workspace to build, test, and iterate on a [https://en.wikipedia.org/wiki/Predictive_analytics predictive analytics] model. Following are the components of the Machine Learning Studio:

	===Experiments===		===Experiments===

Zhu6: /* Build and deploy machine learning models in Azure Machine Learning */

2016-09-09T04:26:51Z

Build and deploy machine learning models in Azure Machine Learning

← Older revision		Revision as of 04:26, 9 September 2016
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	# Import raw data		# Import raw data
	# Preprocess the data		# Preprocess the data
	# Do feature engineering and data labeling (for [[supervised learning]] such as classification)		# Do feature engineering and data labeling (for [https://en.wikipedia.org/wiki/Supervised_learning supervised learning] such as classification)
	# Train, score, and evaluate the model		# Train, score, and evaluate the model
	# Model comparison and selection		# Model comparison and selection

Zhu6: /* Data Collection & Management */

2016-09-09T04:26:23Z

Data Collection & Management

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	===Data Collection & Management===		===Data Collection & Management===

	It allows to use data through blobs and tables ([https://azure.microsoft.com/en-us/services/storage/ Azure Storage]), [~~[Relational database\|~~relational data]] ([https://azure.microsoft.com/en-us/services/sql-database/ Azure SQL Database]), [~~[Apache~~ Hadoop~~\|Hadoop]~~] ([https://azure.microsoft.com/en-us/services/hdinsight/ Azure HDInsight]) and massive data stores ([https://azure.microsoft.com/en-us/solutions/data-lake/ Azure Data Lake]).		It allows to use data through blobs and tables ([https://azure.microsoft.com/en-us/services/storage/ Azure Storage]), [https://en.wikipedia.org/wiki/Relational_database relational data] ([https://azure.microsoft.com/en-us/services/sql-database/ Azure SQL Database]), [https://en.wikipedia.org/wiki/Apache_Hadoop Hadoop] ([https://azure.microsoft.com/en-us/services/hdinsight/ Azure HDInsight]) and massive data stores ([https://azure.microsoft.com/en-us/solutions/data-lake/ Azure Data Lake]).

	===Machine Learning Service===		===Machine Learning Service===

Zhu6: /* Introduction */

2016-09-09T04:25:38Z

Introduction

← Older revision		Revision as of 04:25, 9 September 2016
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	==Introduction==		==Introduction==

	Azure ML offers features such as visual composition, large pallet of modules, an extensive library of starting templates and powerful [https://en.wikipedia.org/wiki/Machine_learning_algorithms machine learning algorithms], a large collection of built-in transformation tasks, and support for frequently used data science programming languages like [https://en.wikipedia.org/wiki/R_(programming_language) R] and [https://en.wikipedia.org/wiki/Python_(programming_language) Python] which makes building common predictive analytics models quick and easy. After developing the model, one can deploy it on [https://en.wikipedia.org/wiki/Microsoft_Azure#Cloud_services Azure Cloud] as a scalable and [https://en.wikipedia.org/wiki/Fault_tolerance fault-tolerant] [https://en.wikipedia.org/wiki/Web_service web service] using Azure's Machine Learning [https://en.wikipedia.org/wiki/Application_programming_interface API]. The web service created by Machine Learning API are nothing but [~~[Representational state transfer\|~~REST]] APIs that allows accessing the model from almost anywhere including web sites and customer applications, As patterns in data change over time, or if the user wants to add some new source of data, the deployed model can be easily retrained and updated programmatically through Azure ML API. Azure ML provides all such facilities through an interactive visual workspace called [https://studio.azureml.net/ Azure Machine Learning Studio.]		Azure ML offers features such as visual composition, large pallet of modules, an extensive library of starting templates and powerful [https://en.wikipedia.org/wiki/Machine_learning_algorithms machine learning algorithms], a large collection of built-in transformation tasks, and support for frequently used data science programming languages like [https://en.wikipedia.org/wiki/R_(programming_language) R] and [https://en.wikipedia.org/wiki/Python_(programming_language) Python] which makes building common predictive analytics models quick and easy. After developing the model, one can deploy it on [https://en.wikipedia.org/wiki/Microsoft_Azure#Cloud_services Azure Cloud] as a scalable and [https://en.wikipedia.org/wiki/Fault_tolerance fault-tolerant] [https://en.wikipedia.org/wiki/Web_service web service] using Azure's Machine Learning [https://en.wikipedia.org/wiki/Application_programming_interface API]. The web service created by Machine Learning API are nothing but [https://en.wikipedia.org/wiki/Representational_state_transfer REST] APIs that allows accessing the model from almost anywhere including web sites and customer applications, As patterns in data change over time, or if the user wants to add some new source of data, the deployed model can be easily retrained and updated programmatically through Azure ML API. Azure ML provides all such facilities through an interactive visual workspace called [https://studio.azureml.net/ Azure Machine Learning Studio.]

	==Azure Machine Learning workflow==		==Azure Machine Learning workflow==

Zhu6: /* Introduction */

2016-09-09T04:25:17Z

Introduction

← Older revision		Revision as of 04:25, 9 September 2016
Line 3:		Line 3:
	==Introduction==		==Introduction==

	Azure ML offers features such as visual composition, large pallet of modules, an extensive library of starting templates and powerful [[machine learning algorithms]], a large collection of built-in transformation tasks, and support for frequently used data science programming languages like [[R (~~programming language~~)\|R]] and [~~[Python~~ (~~programming language~~)\|Python]] which makes building common predictive analytics models quick and easy. After developing the model, one can deploy it on [~~[Microsoft Azure~~#~~Cloud services\|~~Azure Cloud]] as a scalable and [~~[Fault tolerance\|~~fault-tolerant]] [[web service]] using Azure's Machine Learning [~~[Application programming interface\|~~API]]. The web service created by Machine Learning API are nothing but [[Representational state transfer\|REST]] APIs that allows accessing the model from almost anywhere including web sites and customer applications, As patterns in data change over time, or if the user wants to add some new source of data, the deployed model can be easily retrained and updated programmatically through Azure ML API. Azure ML provides all such facilities through an interactive visual workspace called [https://studio.azureml.net/ Azure Machine Learning Studio.]		Azure ML offers features such as visual composition, large pallet of modules, an extensive library of starting templates and powerful [https://en.wikipedia.org/wiki/Machine_learning_algorithms machine learning algorithms], a large collection of built-in transformation tasks, and support for frequently used data science programming languages like [https://en.wikipedia.org/wiki/R_(programming_language) R] and [https://en.wikipedia.org/wiki/Python_(programming_language) Python] which makes building common predictive analytics models quick and easy. After developing the model, one can deploy it on [https://en.wikipedia.org/wiki/Microsoft_Azure#Cloud_services Azure Cloud] as a scalable and [https://en.wikipedia.org/wiki/Fault_tolerance fault-tolerant] [https://en.wikipedia.org/wiki/Web_service web service] using Azure's Machine Learning [https://en.wikipedia.org/wiki/Application_programming_interface API]. The web service created by Machine Learning API are nothing but [[Representational state transfer\|REST]] APIs that allows accessing the model from almost anywhere including web sites and customer applications, As patterns in data change over time, or if the user wants to add some new source of data, the deployed model can be easily retrained and updated programmatically through Azure ML API. Azure ML provides all such facilities through an interactive visual workspace called [https://studio.azureml.net/ Azure Machine Learning Studio.]

	==Azure Machine Learning workflow==		==Azure Machine Learning workflow==

Zhu6 at 04:22, 9 September 2016

2016-09-09T04:22:53Z

← Older revision		Revision as of 04:22, 9 September 2016
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	==Cortana Analytics Gallery==		==Cortana Analytics Gallery==

	Cortana Analytics [https://gallery.cortanaanalytics.com/ (formerly called as Machine Learning Gallery)] provides a gallery of sample machine learning models that one can use to quickly get started. Each model contains an experiment added by Microsoft, its partners or individual data scientists. <ref name="Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes">["Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes"]</ref> It also serves as a platform for users looking to learn from others, to get started developing their own solutions, or to contribute their own work to the advanced analytics community<ref name="Cortana Analytics Gallery">[https://gallery.cortanaanalytics.com/ "Cortana Analytics Gallery"]</ref>.		Cortana Analytics [https://gallery.cortanaanalytics.com/ (formerly called as Machine Learning Gallery)] provides a gallery of sample machine learning models that one can use to quickly get started. Each model contains an experiment added by Microsoft, its partners or individual data scientists. <ref name="Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes">"Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes"</ref> It also serves as a platform for users looking to learn from others, to get started developing their own solutions, or to contribute their own work to the advanced analytics community<ref name="Cortana Analytics Gallery">[https://gallery.cortanaanalytics.com/ "Cortana Analytics Gallery"]</ref>.

	==Azure Marketplace==		==Azure Marketplace==