Developing Analytics Solutions with the Data & AI Global Practice
learn.microsoft.com/en-us/archive/blogs/data_insights_global_practice
AI / MLDatabasesDistributed systems / cloudMicrosoft blogsMSDN archive
- Posts
- 26 from 14 Sep 2015 to 13 Nov 2018
- Posting
- archived, read-only
- Kind
- company blog
- Feed
- https://learn.microsoft.com/en-us/archive/blogs/data_insights_global_practice/feed.xml
2018 3
2017 3
2016 9
- Scaling a recommender system across large data volumes
- Analysing data in SQL Server 16, combining R and SQL
- Evaluating Machine Learning models when dealing with imbalanced classes
- PowerShell Script To Invoke ML Scoring Part I
- Why Public Cloud beats Private Cloud for Analytics: A Data Warrior’s Perspective
- Hacking Web Service Parameters for R Modules in Azure Machine Learning
- Basket analysis with SQL Data Warehouse
- Event Hubs / Stream Analytics / AzureML / PowerBI End-to-End demo – Part III (Enhancement and Visualization)
- Using Azure Machine Learning Notebooks for Quality Control of Automated Predictive Pipelines
2015 11
- Accessing a Visual Generated from R Code in AzureML
- Analytics Centre of Excellence – What’s the value?
- Scaling your Data Warehouse up for load and down for query
- Event Hubs / Stream Analytics / AzureML / PowerBI End-to-End demo – Part II (Real-time Prediction)
- End-to-End Data Processing using Azure Data Factory (Overview)
- Slicing and Dicing: Configuring External Data Input to Azure Data Factory
- Analytics Platform System Whitepapers
- Azure Key Vault and AzureML
- Protecting Sensitive Data with Azure Key Vault
- Event Hubs / Stream Analytics / AzureML / PowerBI End-to-End demo – Part I (Data Ingestion and Preparation)
- Data Insights Global Practice – Blog Kick-off