For the 2nd year in a row, GNet Group has been named Minnesota Business Magazine’s 100 Best Companies to Work For. The 2015 “100 Best” were selected by an independent research firm employing an anonymous online questionnaire filled out by the employees of each company — to determine which companies in Minnesota excel in the areas of work environment, employee benefits, and overall employee happiness, making them the 100 Best Companies to Work For.
The video embedded above, which demos the use of Azure ML with Excel 2013 to predict malignancy of breast cancer biopsies, contains references to the following links: Read more…
I am publishing this blog post as a follow-up to my Azure ML training session on December 10 at the Microsoft Experience Center in Edina. We appreciate the attendance and feedback from all of you who attended the session. Our marketing team has sent out a follow-up email with the slide deck and the links which were referenced in the presentation. If you have any additional feedback or questions about Azure ML or the presentation, please feel free to reach out and contact me. My contact information is as follows:
Following are some links that were referenced during the presentation, and which should be helpful for those of you who are looking to learn Azure ML:
- Azure ML Microsoft page: http://azure.microsoft.com/en-us/services/machine-learning/
- Microsoft Azure Marketplace: https://datamarket.azure.com/browse?query=machine+learning
- Machine Learning Studio: https://studio.azureml.net/
- Azure ML Documentation and Links: http://azure.microsoft.com/en-us/documentation/services/machine-learning/
- Microsoft Azure ML blog: http://blogs.technet.com/b/machinelearning/
- Machine Learning Forum: https://social.msdn.microsoft.com/forums/azure/en-US/home?forum=MachineLearning
- Pricing Link: https://social.msdn.microsoft.com/Forums/en-US/ced81070-4a15-42b9-9ed9-b850a0faa12d/ml-pricing?forum=MachineLearning
- CodePlex Azure ML plugin for Excel: http://azuremlexcel.codeplex.com/
- Use case for ThyssenKrupps Elevators: https://www.youtube.com/embed/ZBGKgiKQfeY?enablejsapi=1;autoplay=0;showinfo=0;theme=dark;color=white;wmode=transparent;rel=0
- Pier 1 Use Case for Azure ML: https://www.youtube.com/watch?v=fN8Cixcc5yg
Data Links Read more…
Business Intelligence (BI) projects are usually intended to meet a hybrid of different needs, frequently including standardized reports and dashboards, self-service reporting, and data discovery tools. Quite often, unexpected relationships and architectural challenges are found in data during the course of a project. Experienced BI teams are not strangers to mid-project discoveries such as many-to-many relationships instead of traditional dimensions, key relationships that do not map properly, and calculations that need to be modified for the unique needs of the business users.
Despite detailed working sessions with business users and preliminary data analysis, sometimes changes to requirements are needed for a project to meet the needs of the business. Even the most carefully planned projects can end up with initial SOWs that don’t fully capture the details of the actual development required to complete a project. Read more…
Automate Bus Matrix Documentation Using DMV Queries from a Microsoft BI Tabular Solution for Project Management Scope Verification – Part 2
This is Part Two, the final of two blog posts about automating the creation of a Bus Matrix in Power Pivot for Excel using DMV queries from an SSAS Tabular Model.
In the first part of this blog post series, which can be found at this link, I discussed the benefits of a Business (Bus) Matrix that can be automatically generated from a SQL Server Analysis Services Tabular Model. Typically, Project Managers and Lead Architects cannot always confirm that a Tabular Model is built to Requirements & Design planning specifications until development is complete and testing begins. An automated Bus Matrix would allow for the Tabular Model to be compared to a Project Management specifications checklist, which usually includes a Bus Matrix that was manually created before development began. With this methodology, discrepancies can be mitigated much earlier in the development process, minimizing rework. Existing Tabular Models that have sparse documentation, or which undergo frequent iterative changes, could also benefit from an automated Bus Matrix.
As promised in the first half of this blog post series, we are going to get to the nuts and bolts of building an automated Bus Matrix using DMV queries from a Tabular Model.