Azure Automated Machine Learning- Part 5

  Azure ML Services has a new component that has been announced a couple of months ago, name Automated Machine Learning. I already start to write about Azure ML Services and Automated ML specifically recently ( which will continue 🙂 ). In Post one to 4 you can find all discussions ( Post 1, Post 2, Post Read more about Azure Automated Machine Learning- Part 5[…]

AI in Dataflow, Power BI webservice, Cognitive Service -Part1

These days, Dataflow (Power Query in Power BI Service) is a new feature that has been released. according to[1]: Dataflow is a Power Query process that runs in the cloud independently from any Power BI reports. If you want to know more about dataflow see [1]   There is a new feature in dataflow that Read more about AI in Dataflow, Power BI webservice, Cognitive Service -Part1[…]

Azure ML workbench- Installation-Part 1

In Microsoft ignite 2017, Azure ML team announce new on-premises tools for doing machine learning. this tools much more comprehensive as it provides 1- a workspace helps data wrangling 2- Data Visualization 3-Easy to deploy 4-Support Python codes in this post and next posts, I will share my experiment with working this tools. first I got Read more about Azure ML workbench- Installation-Part 1[…]

New Series of Time Series: Power BI Custom Visual (Part 5)

In previous posts, I have explained some basic concepts of time series. In the first post I have explained the basic concepts of time series, and in second and third posts I have explained: “Exponential Smoothing” for forecasting data without trend, and with the trend. In the last post, I have explained how we can Read more about New Series of Time Series: Power BI Custom Visual (Part 5)[…]

New Series of Time Series: Part 3 (Holt’s Exponential Smoothing)

In the last posts, I have explained the main concepts behind the Timeseries (Post 1) and  in the second one a simple forecasting approach name as “Exponential Smoothing” has been proposed Post 2. In this post I am going to show how to do see the error of forecasting and also how to forecast when Read more about New Series of Time Series: Part 3 (Holt’s Exponential Smoothing)[…]

Azure ML Part 9- Cross Validation: Machine Learning Prediction (6)

In the previous posts (from Part 1 to Part7), I have explained the whole process of doing machine learning inside the Azure ML, from import data, data cleaning, feature selection, training models, testing models, and evaluating. In the last post, I have explained one of the main ways of improving the algorithms performance name as “Tune Read more about Azure ML Part 9- Cross Validation: Machine Learning Prediction (6)[…]

Azure ML Part 7: A Machine Learning Prediction scenario (4)

In the previous posts from Part 1 to 6, I have explained how to do machine learning process. The data cleaning such as SQL transformation, select specific columns, remove missing values,  Edit meta data, and normalize data.  Also, I have explained how to find relevant attributes  using Feature Selection Feature to identify which feature are more Read more about Azure ML Part 7: A Machine Learning Prediction scenario (4)[…]

Azure ML Part 4: A Machine Learning Prediction scenario (1)

In previous Posts Part 1, Part 2 and Part3  I have explain some about the azure Ml environment, how to import data into it and finally how to do data transformation using Azure ML component. In this post and the next one I am going to show how to do a Machine Learning in Azure Read more about Azure ML Part 4: A Machine Learning Prediction scenario (1)[…]

Have more Charts by writing R codes inside Power BI: Part 2

In the previous post (Part 1) I have explained how to write a simple scatter chart in the Power BI. Now in this post I am going to show how to present 5 different values in just one chart via writing R scripts. I will continue the codes that I wrote in the previous post  as below : Read more about Have more Charts by writing R codes inside Power BI: Part 2[…]

Walk-through Steps: I’m New to BI, Where to Start? – Part 7: Azure

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Many organizations nowadays are in transition from on-premises to cloud, and many of them use hybrid solutions where part of the computing will be done in cloud and the rest on-premises. The trend nowadays is to use cloud to have better maintenance, lower costs, more reliable solutions, lower administrative efforts, and powerful shared resources. In BI world there is a high demand for solutions to be on cloud, some computing services such as data transfer and ETL to be done on cloud, some data analysis and mining solutions happens on cloud, and even data to be stored on cloud data warehouse at some stage. There are many BI vendors in the market, but there are few who provide BI on the cloud.

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