Continuous delivery and machine learning
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Find out about OVH’s approach to continuous delivery of machine learning models. Automated machine learning enables a company to efficiently build models and keep them up-to-date, using data refreshed by the “Data Collector” tool.
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Continuous delivery and machine learning
- 2. PRESENTEDBY CONTINUOUS DELIVERY AND MACHINE LEARNING GUILLAUME SALOU MACHINE LEARNING SERVICES TEAM LEADER @guillaume_salou ROOM 3 3.30 PM
- 3. OUR AGENDA OUR AGENDA • Machine Learning • Disk Failure Detection: Problem • Disk Failure Detection: Solution • Scale-up: Need • Scale-up: Opportunities • Scale-up: Automated Machine Learning • Prescience: Machine Learning Platform • Prescience: The Future
- 4. MACHINE LEARNING MACHINE LEARNING Machine learning is a subset of artificial intelligence that uses statistical techniques to give computers the ability to learn (i.e. progressively improve their performance of a specific task) from data, without being explicitly programmed to do so.
- 6. DISK FAILURE DETECTION DISK FAILURE DETECTION • Detects if a disk is broken • Works ‘on-the-fly’ • RTM: Real-time Monitoring (agent deployed on servers) • Supports bare metal • Learns from bench and reinstall processes
- 7. DISK FAILURE DETECTION DISK FAILURE DETECTION Bench/ reinstall Train Server RTM DATA Deploy RTM Query
- 8. DISK FAILURE DETECTION: PROS DISK FAILURE DETECTION • Useful • Already deployed on Public Cloud, IPLB, etc. • KPI-driven • Self-learning • Detect Predict
- 9. DISK FAILURE DETECTION: CONS DISK FAILURE DETECTION • Takes time • Retraining is needed • Upgrades are complicated • Knowledge is in silos • Specific to different disks
- 10. DISK FAILURE DETECTION: CDS DISK FAILURE DETECTION BUILD/ EVALUATE DEPLOY SCOREFEEDBACK TRAIN
- 11. Machine Learning Platform DISK FAILURE DETECTION DISK FAILURE DETECTION Bench/ reinstall Server RTM DATA Deploy RTM Query Train
- 13. SCALE-UP SCALE-UP • Automate continuous delivery • Simplify the sharing of knowledge • Use the power of the cloud: optimising/preprocessing/explanation • Only one automated process must be updated and monitored • Automate model rebuild processes • Enable quick wins and fast fails • Allows us to distribute processing: Sklearn • Backend agnostic (Sklearn, Spark, etc.) • Feature extraction can be done by data or business analysts
- 14. SCALE-UP: AUTO ML SCALE-UP Our focus: • Supervised (e.g. classification, regression, etc.) • Structured data (CSV) • Starting TimeSeries R&D subject: • Stack complex methods • Unstructured data (graph, image, text, etc.)
- 19. PRESCIENCE Optimiser SMAC Download files Preprocess data/models (PMML) Explain models/predictions SHAP Automated preprocessing Text Custom modeling Scikit-Learn Distributed Yes PRESCIENCE PRESCIENCE
- 24. PRESCIENCE: EXPLAIN PRESCIENCE • SHAP (SHapley Additive exPlanations). • A unified approach to explaining the output of any machine learning model • Designing/coding UX • Allows us to explain one prediction, or a model (i.e. thousands of predictions)
- 26. PRESCIENCE: THE FUTURE PRESCIENCE • Integrate other algorithms: XGBoost, Tensorflow algorithms… • Solve other problems: • TimeSeries forecasting • Anomaly detection • Image recognition • Natural language processing • And many more! • Integrate other data sources: Databases, Kafka, PCS... • Apply it everywhere in OVH