This document provides an overview of machine learning and how cloud services can help with machine learning projects. It defines machine learning and describes the main types (supervised, unsupervised, reinforcement learning). It then discusses how the cloud helps with the main parts of a machine learning workflow: fetching and preparing data using cloud data warehousing, training models using GPUs and cloud-based computation, and deploying models using serverless functions and APIs. It also mentions some pre-built AI services like IBM Watson and Amazon AI.

1. MACHINE LEARNING
USING CLOUD SERVICES
Max Pagels, Data Science Specialist
max.pagels@sc5.io, @maxpagels
12.6.2016
A general overview

2. WHAT IS MACHINE LEARNING?

3. “… FIELD OF STUDY THAT GIVES COMPUTERS
THE ABILITY TO LEARN WITHOUT BEING
EXPLICITLY PROGRAMMED”
- Arthur Lee Samuel, 1959

4. ALGORITHMS THAT LEARN FROM DATA IN
ORDER TO FIND STRUCTURE, PROVIDE
INSIGHTS, CLASSIFY AND PREDICT

5. DATA > COMPUTER LEARNS A MODEL > MODEL USED TO SOLVE TASK

6. SUPERVISED
MACHINE
LEARNING
UNSUPERVISED
MACHINE
LEARNING
REINFORCEMENT
LEARNING

7. 1. FETCH & PREPARE DATA
2. TRAIN MODELS
3. DEPLOY MODELS
MACHINE LEARNING, IN A NUTSHELL

8. HOW CAN CLOUD SERVICES HELP?

9. 1. FETCHING & PREPARING DATA
90% of all time is spent on getting & cleaning data

10. TYPICAL PROBLEMS
• Data is stored in multiple DBs
• Data access is behind multiple systems
• Data is missing
• Data is in the incorrect format
• Data is only available in aggregated form
• Running queries takes a long time

11. SOLUTION: CLOUD DATA WAREHOUSING
• Data is stored in one logical, petabyte-scale DB
• Centralised user access management
• Usually much cheaper to run than in-house solutions
• Can save (and query) raw data
• Querying is typically much faster

12. 2. TRAINING MODELS
Test, validate, rinse & repeat

13. Depending on the type of classifier and the problem at hand, training a model
can take ages on a normal laptop/desktop computer.
PROBLEM

14. SOLUTION: GPU(S)

15. SOLUTION: CLOUD-BASED COMPUTATION
Example: on AWS EC2, a p2.8xlarge instance has:
• 32 vCPUs
• 488 GiB RAM
• 8 NVIDIA K80 GPUs, 2,496 PPCs and 12GiB of GPU memory per GPU
Cost of buying one K80 yourself: $5,000
Cost of buying the equivalent hardware yourself: $50,000
Cost of running the instance in AWS: about $8 per hour

16. 3. DEPLOYING MODELS
Putting your machine learning models to good use

17. DEPLOYING MODELS
• ML models can take a long time to train, but the models themselves
usually don’t take much (disk/RAM) space
• Getting a prediction/result from an ML model typically doesn’t take
that much time, either (milliseconds)
• Building a REST API on top of your model allows other services to get
predictions on demand
• Use functions-as-a-service as your first choice

18. DEPLOYING MODELS
• ML models can take a long time to train, but the models themselves
usually don’t take much (disk/RAM) space
• Getting a prediction/result from an ML model typically doesn’t take
that much time, either (milliseconds)
• Building a REST API on top of your model allows other services to get
predictions on demand
• Use functions-as-a-service as your first choice
SERVERLESS + API GATEWAY = QUICK PREDICTION REST API

19. DON’T REINVENT THE WHEEL
Pre-made models and services may work well

20. SOME READY-MADE AI/ML SERVICES

21. IBM WATSON
• Natural language processing
• Language translation
• Sentiment analysis
• Speech-to-text
• Text-to-speech
• Personality insights

22. AWS AI
• AWS ML: linear/logistic
regression (classification & real-
number prediction)
• Amazon Lex: conversational
interfaces
• Amazon Rekognition: object
detection
• Amazon Polly: text-to-speech

23. LET’S END WITH AN EXAMPLE

27. THANK YOU
Have a great evening!