Monitoring, Data Center et Machine Learning https://labs.ovh.com/machine-learning-platform Clement Bataille Christophe Rannou 

WE OPERATE IN A FAST GROWING MARKET where availability is essential PUBLIC CLOUD & VPS PRIVATE CLOUD DEDICATED SERVER STORAGE NETWORK & SECURITY MULTI-LOCAL EVERYWHERE RAPID DELIVERY WORD-CLASS STANDARDS 

OUR STORY 

WE INNOVATE WITH EVERYONE WE ARE INDEPENDANT WE ARE THE ALTERNATIVE WHICH FREES UP THE CLOUD THROUGH CREATIVITY AND AN ENTREPRENEURIAL SPIRIT EMPLOYEES CUSTOMERS ECOSYSTEM OVH PARTNERS INNOVATION FOR FREEDOM 

WE ARE A GLOBAL CLOUD PROVIDER WE ARE AN INDUSTRIAL PLAYER Hillsboro x1 17 Tbps 34 Point of Presence IN 3 CONTINENTS BHS x6 Vinthill x1 +1 400 000 CUSTOMERS IN 132 COUNTRIES Singapore x1 2 200 EMPLOYEES IN 18 COUNTRIES Sydney x1 Europe x18 BUILDER OF ITS OWN SERVERS SINCE 2002 + 1 million SERVERS built since 1999 29 DATA CENTERS in 12 locations EXISTING SINCE 1999 

SIMPLE AND QUICK TO SET UP MULTI-LOCAL, CLOSE TO EACH PERSON THROUGHOUT THE WORLD WHOSE COST IS ACCESSIBLE AND PREDICTABLE REVERSIBLE, OPEN AND INTEROPERABLE TRANSPARENT AND RESPONSIBLE WE BUILD A DIFFERENT CLOUD a truly Cloud SMART TO GROW AND SUCCEED TOGETHER 

ML for Time Series with Prescience 7 

Toy Use Case 8 We are going to have a look at the international airline passengers prediction problem. The initial data is in the csv format : steps passengers 1949-01 112 1949-02 118 1949-03 132 1949-04 129 

Problem Definition Detection Time Series • Specific Target • Ground Truth quality • Specific Target • Ground Truth quality • Forecast • Anomaly detection • Prediction horizon • Forecast • Anomaly detection • Prediction horizon 9 

Problem definition 10 What kind of problem do you want to solve ? • Simple Classification • Simple Regression • Time series classification • Time series regression/forecast 

Problem definition 11 • What is your label ? • What is your ordering column ? Month Passengers 1949-01 112 1949-02 118 1949-03 132 1949-04 129 Ordering column Label 

Problem definition 12 • How many steps forward do you want to predict ? One year => 12 steps • Do you want to apply a discount ? No => 1 • Scoring metric ? Default => MSE 1 Year 

Experiment definition 13 How many fold do you want to create ? 2 

Experiment definition 14 How much budget to allow on optimization ? 10 

Demo 15 

Scores are deceitful • Data Leakage – Unusable – Dangerous – Difficult to detect – Difficult to correct • Wrong problem definition 16 

Stock Value Prediction: How to get rich in 5 minutes Graphs: Vegard Flovik, How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls 17 

Stock Value Prediction: Why I am not rich 18 

Stock Value Prediction: What I should have done 19 

Use cases 

Electricity Comsuption forecast Sonde DC Site EDF WARP10 ML with Prescience Retrain model every week Forecast everyday 

CEPH Key indicator: Forecast every 6h 

VOIP Monitoring Chiffres Clés : 125 destinations 3 lignes par pays 1 point à l’heure 

Temperature Anomaly Room Warp10 Arima Model Retrain every week Forecast by point Compare with Isolation Forest Alerting with TAT 

Temperature Anomaly Rack Warp10 Mobil Mean Isolation Forest Alerting with TAT 

Network forecast Key indicator : • 20K equipment • 100 metric by equipment • Point every minute 

Network forecast • Trend Stationary • Differency Stationary Method stationary transformation Graphs: Investopedia, stationary and non-stationary processes : https://www.investopedia.com/articles/trading/07/stationary.asp 

Network forecast Warp10 Heuristique de selection Regression Arima by TS Export to WARP10 + Mailing 

T H A N K YO U @ChrisRannou Clement Bataille https://gitter.im/ovh/ai