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What does Bob really want? Building recommendation systems based on cloud technologies Olivia Klose | Technical Evangelist...
13.06.20 15 SQLSaturday Rheinland 2015
The challenge in automation is enabling computers to interpret endless variation in handwriting.
The challenge in automation is enabling computers to interpret endless variation in handwriting.
1. Too complex: When you can’t code it. 2. Too much: When you can’t scale it. 3. Too specialised: When you have to dapt/pe...
VALUE DIFFICULTY What happened? Why did it happen? What will happen? How can we make it happen? Traditional BI Advanced An...
Challenges Skilled Data Scientists Infrastructure Time Scalable
?
[green] [sour] [citrus] [green] [sweet]
Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker...
ML Studio M HDInsight SQL Server VM SQL DB Blobs & Tabellen Cloud Desktopdateien Exceltabelle Andere… Local IDE for ML Web...
http://aka.ms/AzureML-Market
http://aka.ms/MLCheatSheet
In limited preview SELECT text, sentiment(text) FROM myStream http://aka.ms/stream-ml
http://aka.ms/MLbook
aka.ms/azurenow Machine Learning Series http://aka.ms/MLserie http://aka.ms/AzureML-resources Machine Learning Cheat Sheet...
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered tr...
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
What does Bob really want? Recommenders in the Cloud
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What does Bob really want? Recommenders in the Cloud

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What does Bob really want? Building recommendation systems based on cloud technologies

Machine Learning or Data Science are one of today's hottest buzzwords. The scenarios in which Machine Learning can be applied are diverse and can range from predicting football scores to personalised recommendations in online shops to predictive maintenance in manufacturing.

In this session I will present Azure Machine Learning - a service in Microsoft Azure that anyone can use to build predictive models using the provided machine learning algorithms and deploy it as a web service. Here, I will go through various options on how to use AzureML when going beyond just finding the products in retail that are in high demand: predicting the taste and preferences of customers (old and new) and giving appropriate recommendations.

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What does Bob really want? Recommenders in the Cloud

  1. 1. What does Bob really want? Building recommendation systems based on cloud technologies Olivia Klose | Technical Evangelist, Microsoft NoSQL Usergroup Cologne, July 1st 2015
  2. 2. 13.06.20 15 SQLSaturday Rheinland 2015
  3. 3. The challenge in automation is enabling computers to interpret endless variation in handwriting.
  4. 4. The challenge in automation is enabling computers to interpret endless variation in handwriting.
  5. 5. 1. Too complex: When you can’t code it. 2. Too much: When you can’t scale it. 3. Too specialised: When you have to dapt/personalise. 4. Autonomous: When you can’t track it.
  6. 6. VALUE DIFFICULTY What happened? Why did it happen? What will happen? How can we make it happen? Traditional BI Advanced Analytics
  7. 7. Challenges Skilled Data Scientists Infrastructure Time Scalable
  8. 8. ?
  9. 9. [green] [sour] [citrus] [green] [sweet]
  10. 10. Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
  11. 11. ML Studio M HDInsight SQL Server VM SQL DB Blobs & Tabellen Cloud Desktopdateien Exceltabelle Andere… Local IDE for ML Web Service MonetiseStorage Account
  12. 12. http://aka.ms/AzureML-Market
  13. 13. http://aka.ms/MLCheatSheet
  14. 14. In limited preview SELECT text, sentiment(text) FROM myStream http://aka.ms/stream-ml
  15. 15. http://aka.ms/MLbook
  16. 16. aka.ms/azurenow Machine Learning Series http://aka.ms/MLserie http://aka.ms/AzureML-resources Machine Learning Cheat Sheet http://aka.ms/MLCheatSheet Machine Learning Studio http://studio.azureml.net Kostenloses E-Book http://aka.ms/MLbook oliviaklose.com aka.ms/MLblog @oliviaklose
  17. 17. © 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks in the U.S. and/or other countries. Olivia Klose Technical Evangelist Microsoft Deutschland GmbH E-Mail: olivia.klose@microsoft.com Blog: oliviaklose.com Twitter: @oliviaklose

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