This document discusses how data science models have transitioned to the cloud to take advantage of greater computing resources. It notes that data science models are resource-intensive and traditionally required powerful local machines. The cloud allows data scientists to run models on cloud infrastructure for lower costs than high-end laptops and with access to many GPUs. Several major cloud platforms - Azure, AWS, and Google Cloud - are discussed and compared in terms of their machine learning offerings. The document also introduces Microsoft's Team Data Science Process, which aims to help data science teams collaborate more effectively on projects in the cloud.

1. Data Science in the Cloud
November 2nd, 2017

2. Data Science Transition to
the Cloud
Various Cloud Offerings
Team Data Science

3. Data Science Transition to the Cloud

4. Data Scientists must have many tools at their
disposal
– R
– Python
– SQL
– Data Analysis
– Big Data
Now they must begin to consider another tool
to help them build and test out their models
– Cloud
Data Scientists and their Toolsets

5. Modeling and Validation take time and resources
Back in the day, data scientists would wait hours to run models and validate ideal performance
All this was done on a personal local machine with limited memory
Data Science Models are resource intensive

6. Something had to give
Minimize the volume of data in the model
– Limits model performance
Minimize the complexity of the model
– Limits variety and innovation
Have personal machines increased performance over the last couple of years to handle both
volume and complexity?
Data Science Models are resource intensive

7. Laptops can now be purchased with 2 TB of Hard Drive using SSD and 32 GB of Memory
You’re looking at least $4K
What if you could run the same model on your top of the line laptop on the cloud for a few $$
each month?
Data Science Models are resource intensive

8. Many models benefit from larger datasets, especially deep learning models
How many GPU’s does your local machine have?
CPU has a few cores, GPU has hundreds of cores
GPU’s can huge batches of data and perform the same operation over and over again
CPU’s can handle a few threads at a time
GPU vs CPU

9. Free users from hardware and local constraints
Collaborative
– Models can be accessed by anyone with an account
– Models can be shared across developers
Advantages to Machine Learning in the Cloud

10. Compliance and Security
Offline access
Disadvantages to Machine Learning in the Cloud

11. Various Cloud Offerings

12. Microsoft Azure
Amazon Web Services
Google Cloud Platform
Azure vs AWS vs Google Cloud

13. Magic Quadrant for Cloud According to
Garner in 2014
AWS was on an island by itself
Cloud according to Gartner in 2014

14. For our purposes we want to do a comparison based on Machine Learning offerings for Azure,
Amazon Web Services, and Google Cloud?
But what about Machine Learning?

15. One of the most automated solutions on the market
Can load data from Amazon RDS, Redshift, and CSV files
Data can automatically be identified as categorical or numeric during preparation
Modeling does not support Unsupervised Learning, only Supervised out of the box
Predictions fall under only 2 main areas:
1. Binary and Multiclass classification
2. Regression
AWS Machine Learning

16. Similar to the Amazon Machine Learning offering
Predictions fall under similar categories out of the box:
1. Binary and Multiclass Classification
2. Regression
Google offers pre-trained models as templates that can be used as starting points for model
development
Google Prediction API

17. Almost all operations are manual as opposed to Google and Amazon
Supports a user-friendly graphical interface to visualize each step within a workflow for building
a model
– Looks very much like Visio or PowerPoint
Supports both Supervised and Unsupervised models
Azure ML

18. Variety of Algorithms available, not just limited to Classification and Regression:
– Classification: Binary and Multiclass
– Regression
– Anomaly Detection
– Recommendation
– Text Analysis
Azure also has a set of templates available in the Cortana Intelligence Gallery that are prebuilt
as templates for reuse
Azure ML

19. Team Data Science

20. From a 2014 Capgemini Report
Only 27% of the big data projects are regarded as successful
Only 13% of organizations have achieved full-scale production for their big data
implementations
Only 8% of the big data projects are regarded as VERY successful
Only 17% of survey respondents said they had a well-developed Predictive/Prescriptive
Analytics Program in while
Some Sobering Statistics

21. Why is this happening?
Most data scientists unfortunately are working in silos
Silos within an organization
Silos within their tools
Some Sobering Statistics

22. Team Data Science Process in AML is an agile and iterative process for delivering Machine
Learning solutions effectively across enterprise Data Science Teams
Released in April 2017
One developer picks up where the other one dropped off
Team Data Science Process in Azure ML

23. Team Data Science Process is comprised of the following four parts:
1. A standard data science life cycle definition
2. A standard template for documentation, structure, and reporting
3. Infrastructure for project execution and code repositories
4. Tools for implementing task lists, version control, code review, data exploration and
modeling
Team Data Science Process in Azure ML

24. Team Data Science Process is comprised of the following four parts:
1. A standard data science life cycle definition
2. A standard template for documentation, structure, and reporting
3. Infrastructure for project execution and code repositories
4. Tools for implementing task lists, version control, code review, data exploration and
modeling
Team Data Science Process in Azure ML

25. The next time your boss asks you if you want to upgrade your laptop
Instead you may want to ask them to get you an iPad Pro or a Surface Notebook with a cloud
subscription
Key Takeaway

26. Questions?

27. THANK YOU!
What questions do you have?