Machine Learning Using Cloud Services
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A general overview of how cloud technologies can help accelerate machine learning.
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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!