Machine Learning Using Cloud Services
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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.
Machine Learning Using Cloud Services
- 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
- 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!