Road Map to use Microsoft ML tools

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I normally work with most of the Microsoft tools for the aim of doing machine learning. I came up with a roadmap that shows what option for machine learning we have with Microsoft tools in Data Platform. this roadmap is not completed yet, I need to update it one later with some tools such azure data bricks, data science virtual machine and so forth. But, in this post, I try to provide an overview what options we have regarding working with MS ML tools.

 Writing R or Python in some Microsoft data Tools

There is a possibility to write your own R or Python codes inside some of the Microsoft data platform tools.

Azure ML

Azure mL studio is a drag and drops environment that helps users to create an ML model using some predefined model to create a machine learning models and deploy it as an API. However, besides 25 pre-defined algorithms that already exists in Azure ML Studio, there is a possibility to write Python or R codes inside the Azure ML studio. for more information click here.

SQL Server 2016 and 2017

Moreover, there is a possibility to embed R or Python codes in SQL Server 2017 (ML services) or only R in SQL Server 2016 (R services). I have some blog posts on this that show the process you see from post1, post2 

Power BI and R

there is a possibility to run R codes in Power BI with the aim of visualization in the report area, create the custom visual using R and JSON, or using R inside the Power Query. the detail of how we can do that has been explained in posts

Azure ML work Bench with Python  

Azure ML workbench as an on-premises tool able to help people to easily deploy their codes. There is a possibility to write Python codes and deploy the code. in near future, I will write some posts on how to write Python codes inside Azure ML workbench.

 Azure Data Lake write R and Python

there is also to write R or Python codes inside the U-SQL language in Azure data Lake.

Use the created ML model (API) in other Applications

As you know there is a possibility to create API from our Machine Learning model. This model can be consumed in Excel (add-on), Stream Analytics, and Power BI. The same for cognitive services API that can be consumed in Power BI, .Net. and Microsoft flow.

Access to data resources from R studio for data modeling and exploration

For most of the data science application, we need normally to create the code and test it in R studio, then use the codes in other application. For this aim, there is a possibility to load the data from SQL Server 2016 and 2017 into R studio using “RODBC” package (see post). Also, there is a possibility to get the data from Azure Data Lake and do analysis on them (see post). Finally, we able to fetch the model and datasets from Azure ML or upload data into Azure ML environment via R Studio (see posts).

 

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Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

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