1. High Performance Data Scientist
By : Swapnil Dubey

2. About me ... Swapnil Dubey : 9+ years -
Domains - Ecommerce ,AdServing, Banking

3. Agenda ... Benefits of running DS on Cloud. Introduction to MLaaS.
Demo: Running DS models using Tensorflow and keras on Google Cloud ML(Using GPUs).
Discussion on how to develop a generic infinitely scalable infrastructure - Why?What?How?
Demo: Running multiple R jobs to show auto scaling feature of the infrastructure.
Kubernetes Federation + Ubernetes.
Demo : Deploying application in Kubernetes Federation set up.
Infrastructure for running production and experiment jobs for DS.
Talk on Docker, Kubernetes
Cross region kubernetes
Cross cloud vendor kubernete

4. Benefits of Running DS in Cloud
Unpredictable need of Infrastructure - Infinitely scalable infra for shorter duration
Innovate and lead - New Languages, New Libraries - lots of experiments
New hardware components - Running models on various versions of GPUs
Improved security and compliance - running jobs in different regions of world
Effective task execution - Fault tolerance, run-time dynamic scalability.

5. Introduction to MLaas ... Machine Learning as a Service
Platform which can provide almost all the advantages of last slide.
Famous vendors: Amazon Machine Learning, Google Cloud Machine Learning, Azure Machine Learning
Different vendors - different pre-built libraries
Amazon ML pre built libraries - Apache MXNet, TensorFlow, Caffe2 (and Caffe), Theano, Torch, Keras

6. Introduction to MLaas ... Machine Learning as a Service
Platform which can provide almost all the advantages of last slide.
Famous vendors: Amazon Machine Learning, Google Cloud Machine Learning, Azure Machine Learning
Different vendors - different pre-built libraries
Amazon ML pre built libraries - Apache MXNet, TensorFlow, Caffe2 (and Caffe), Theano, Torch, Keras
Areas of Concerns :
- How to use custom infrastructure ?
- How to run job involving a non pre built library?

7. Demo
Running DS models using Tensorflow and keras on Google Cloud ML(Using GPUs).
Disadvantage : Completely new flavor of technology set is not achievable.

8. Cognitive APIs
Advanced artificial intelligence features NLP,Speech recognition,Vision API etc.
Demo : Google Vision API
Very basic ….

9. Kevin Novak, Head Of Data Science@Uber
Article By Kevin Novak
The Purpose of Platforms in Data Science ,April-2016
“We will need special features, we’ll have more control, we’ve got great engineers who could build it for cheap, and of course, this is strategic to the business.”

10. Developing a Generic Infrastructure
How to support an altogether different flavour of DS as well as non DS job on a Cloud Vendor?
Constructing a numpy file.
Running spark jobs for transformations
Any new hypothetical task on any new technology
How to work with different versions of languages supported out of the box?
How to have an auto scale up and scale down infrastructure which is cost effective?
How to have a cloud vendor independent deployment for you DS jobs?
Running jobs in different regions.

11. Developing a Generic Infrastructure
How to support an altogether different flavour of DS as well as non DS job on a Cloud Vendor?
Constructing a numpy file.
Running spark jobs for transformations
Any new hypothetical task on any new technology
How to work with different versions of languages supported out of the box?
How to have an auto scale up and scale down infrastructure which is cost effective?
How to have a cloud vendor independent deployment for you DS jobs?
Running jobs in different regions.

12. Developing a Generic Infrastructure
How to support an altogether different flavour of DS as well as non DS job on a Cloud Vendor?
Constructing a numpy file.
Running spark jobs for transformations
Any new hypothetical task on any new technology
How to work with different versions of languages supported out of the box?
How to have an auto scale up and scale down infrastructure which is cost effective?
How to have a cloud vendor independent deployment for you DS jobs?
Running jobs in different regions.

13. Developing a Generic Infrastructure
How to support an altogether different flavour of DS as well as non DS job on a Cloud Vendor?
Constructing a numpy file.
Running spark jobs for transformations
Any new hypothetical task on any new technology
How to work with different versions of languages supported out of the box?
How to have an auto scale up and scale down infrastructure which is cost effective?
How to have a cloud vendor independent deployment for you DS jobs?
Running jobs in different regions.

14. In other words ... Horizontal dynamic runtime scaling
Rolling updates of Tech stack
Cloud native job like infrastructure
Support for both persistent and non persistent applications

15. Using Native Cloud Compute And Storage ...
Spawn a VM and run with inflated resources.

16. Docker & Kubernetes

17. (EC2 Container Registry/Google Container Registry)
What we did ?
Producer
Consumer
KOPS/ GKE
Submit Python Job
Python Job Topic
spark
Java
Producer R
Submit R jobs
R Job Topic
python
Job Submission UI
Producer
Submit MPI Jobs
MPI Job Topic
Container Registry
(EC2 Container Registry/Google Container Registry)
Producer
Submit Java Jobs
Java Job Topic
updateStatus
(failed)
Virtual Machines
Pub/Sub
submitJob
updateStatus
(submitted)
updateStatus
(running)
Expected time to complete- 10 mins
1 Already hosted web app.
2. Python job
3. Upscaling and downscaling
4. Preemptible nodes- reducing the cost by 1/3
Admin Module(Virtual Machines)

18. Kubernetes Federation ...

19. Ubernetes ...

20. Summary : Developing a Generic Infrastructure
How to support an altogether different flavour of DS as well as non DS job on a Cloud Vendor?
Constructing a numpy file.
Running spark jobs for transformations
Any new hypothetical task on any new technology
How to work with different versions of languages supported out of the box?
How to have an auto scale up and scale down infrastructure which is cost effective?
How to have a cloud vendor independent deployment for you DS jobs?
Ubernetes
Running jobs in different regions. - Federation

21. Catch me ... https://www.linkedin.com/in/swapnil-dubey-8133154a/
@SwapnilDubey14

22. Thanks!