1. Cloud Analytics Platforms Christian Frey

2. About AIDA Our mission is to advance knowledge in data analytics through research, education and outreach Our goal is to foster collaboration and the sharing of data analytics methods, technologies, and ethical practices among its stakeholders

3. About me – Christian Frey Graduated in May with Business and Computer Science Work at AIDA full time now, managing the Institute You can find me at the top floor of Patterson, in the Rural Innovation Centre Christian.frey@acadiau.ca 1-902-585-1777

4. Agenda Cloud-based analytics space Pros and Cons of: Google Cloud Predictions API Amazon ML Microsoft Azure IBM SPSS Modeler Gold on Cloud Tutorial using BigML Loading in data and creating a dataset Customizing the model creation

5. Data Analytics Roadmap Source: https://www.informs.org/ORMS-Today/Public-Articles/October-Volume-37-Number-5/Back-in-Business Tonight, we are here

6. Google Cloud Predictions API - cloud.google.com/prediction/ API Explorer on the website, great for prototyping Gives you $300 for 60 days of experimentation Integrates into other Google services, most notably Google Sheets Uses online learning to allow for addition of new data

8. Google Results on Iris Dataset

9. Pros and Cons of Google Cloud Predictions API ProsCons Integrates with Google Sheets so you can predict your spreadsheets No choice in the model it uses Very good accuracy on the training setNo method of exporting the model that was created Fast training and prediction times, usually under 1 minute to train smaller datasets, half a second to predict

10. Amazon Machine Learning – aws.amazon.com/machine-learning 3 Ways to access your model: Though a web interface Through an API in a variety of languages Through the AWS command line interface No free trial available Picky with the data it accepts – No more than 10k errors in your data, or 10% No choice in model that is used

12. Pros and Cons of Amazon Machine Learning ProsCons Easy to load data into Amazon S3, then create the model Data must be located in Amazon S3 or Redshift storage, locked into Amazon for everything Model can easily be integrated with other Amazon services No choice of model, it uses variants of regressions for everything. (Linear, Logistic, and Multiclass) Accuracy is slightly lower than other products on the Iris data set

13. Microsoft Azure ML - studio.azureml.net Drag and drop modules onto an infinite background, then connect Many models to choose from, requires some understanding of the data Offers a web service to access your model Good for those who know about Machine Learning, but don’t want to code

14. Results on the Iris Dataset - Azure

15. Pros and Cons of Azure ML ProsCons Lots of machine learning algorithms available, including Neural Networks, Naïve Bayes, Clustering, and Decision Trees It is difficult to find options or the correct module to use Easy to use free trial, no sign up required!Free trial only saves data for 8 hours Allows you to run arbitrary Python or R code as a module to process or analyze your data

16. IBM’s Offerings Alchemy API Focuses on Natural Language Processing (NLP), image analysis, and text to speech/speech to text. Bluemix Predictive Analytics Requires a pre-built SPSS model file. Hosts and runs your models so you can generate predictions on the web. IBM SPSS Modeler Gold on Cloud Runs in the cloud in a Citrix virtualization system. Similar to local SPSS version.

17. IBM SPSS Modeler Gold on Cloud IBM SPSS Modeler – create decision trees, regressions, from an arbitrary dataset Pay as you go – only pay for what you need to use Drag and drop with easy to find modules Auto classifier runs all models and allows you to compare them

18. SPSS Modeler – Results on IRIS Dataset

19. Pros and Cons of IBM SPSS Modeler Gold on Cloud ProsCons Easy to figure out drag and drop interfaceCannot run arbitrary Python or R code in cloud version of SPSS Modeler Tied for first place in accuracyOnly supports hosted DB2 database connections, no connection to other databases Bulk loading of data into SPSS Modeler from DB2 requires a support ticket

20. BigML – BigML.com Easy to sign up, with an unlimited number of small models (up to 16MB of data) 1-click dataset, 1-click models, and 1-click model evaluation to go from dataset to evaluation in 3 clicks Also offers customization options, with suggestions driven by the data