Monitoring OVHcloud datacentres with machine learning

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This document contains summaries from multiple presentations at the OVHcloudSummit on machine learning topics. It discusses using machine learning for time series forecasting like predicting airline passengers. It defines problems as classification, regression or time series forecasting. Examples of use cases are given like electricity consumption, storage, and network forecasting. Finally, it discusses common pitfalls like data leakage and provides resources on time series techniques.

Technology

#OVHcloudSummit
Monitoring,
Datacenter
and Machine Learning
Christophe Rannou
Machine Learning Engineer
@ChrisRannou
ROOM 4
Clement Bataille
Data Scientist
FROM 15:00PM
TO 15:45PM
https://ovh.to/bLpY2wS

#OVHcloudSummit
Machine Learning
for
Time Series with Prescience

#OVHcloudSummit
Toy Use Case
Let's have a look at the international airline passengers prediction
problem.
The initial data is in csv format :
Month NbPassengers
1949-01 112
1949-02 118
1949-03 132
1949-04 129

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

#OVHcloudSummit
Problem Definition
• What is your label ?
• What is your ordering column ?
Month NbPassengers
1949-01 112
1949-02 118
1949-03 132
1949-04 129
Ordering
column
Label

#OVHcloudSummit
Problem Definition
• 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 => Mean
Square Error (MSE) 1 year = 12
steps

#OVHcloudSummit
Experiment Definition
How many fold do you want to create ? 2

#OVHcloudSummit
Experiment Definition
How much budget to
allow on optimization
? 10

#OVHcloudSummit
Pitfalls
• Data Leakage
• Unusable
• Dangerous
• Difficult to detect
• Difficult to correct
• Wrong problem definition
Scores can be deceitful

#OVHcloudSummit
Pitfalls
Stock Value Prediction: How to get rich in 5
minutes
Graphs: Vegard Flovik, How (not) to use Machine Learning for time series forecasting: Avoiding thepitfalls

#OVHcloudSummit
Pitfalls
Stock Value Prediction: Why I am not rich

#OVHcloudSummit
Pitfalls
Stock Value Prediction: What I should have done

#OVHcloudSummit
Electricity Consumption forecast
Probe
DC
Site
EDF
WARP10 ML with
Prescience
Retrain
model every
week
Forecast
everyday

#OVHcloudSummit
CEPH
Key indicator:
Forecast every 6h

#OVHcloudSummit
VOIP Monitoring
Key figures:
125 destinations
3 lines by country
1 point each hour

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

#OVHcloudSummit
Warp10
Mobil
Mean
Isolation
Forest
Alerting
with TAT
Temperature Anomaly
Rack

#OVHcloudSummit
Network Forecast
Key indicator :
• 20K
equipment
• 100 metric by
equipment
• Point every
minute

#OVHcloudSummit
Nerwork Foreast
Stationary transformation method
• Trend Stationary • Differency Stationary
Graphs: Investopedia, stationary and non-stationary processes :
https://www.investopedia.com/articles/trading/07/stationary.asp

#OVHcloudSummit
Network Forecast
Warp10
Heuristique
de selection Regression Arima by TS
Export to
WARP10 +
Mailing

#OVHcloudSummit
THANK YOU
Christophe Rannou
Machine Learning Engineer
@ChrisRannou
Clement Bataille
Data Scientist
Try the lab: https://labs.ovh.com/machine-learning-platform
https://ovh.to/bLpY2wS

#OVHcloudSummit
Let’s discuss with us on Enterprise booth
ENTERPRI
SE

#OVHcloudSummit
#OVHcloudSummit
Let’s share your experience!

Streamlining your machine learning pipeline is critical for enterprise data science to deliver better business results. Accelerating the process from data, to processing to training to deployment and back again will help you get better performing models, faster. Watch the full presentation with audio and video here: https://info.cnvrg.io/build-machine-learning-pipelines This presentation will offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. We will dissect each part of the pipeline and offer strategies on how to design your machine learning pipelines for a more efficient, integrated and automated process. We’ll tackle ways to connect all your data sourcing in one unified location. How to create modular ML components for easy reproducibility, and automate MLOps for quick training of models and hyperparameter optimization. Streamline frequent deployment of models leveraging the power of Kubernetes. And lastly, you’ll learn to design a monitoring toolkit with Grafana and Kibana for easy CI/CD. Join Solutions Architect, Aaron Schneider as he builds and end-to-end machine learning pipeline, and explains how to optimize each part for a more efficient workflow. Key webinar takeaways: - Set up an efficient machine learning pipeline - Learn key MLOps solutions streamlining science and engineering - Create reusable ML components - Build a suite of monitoring and visualization tools - Instantly train and deploy ML models with Kubernetes - Use CI/CD to design an auto-adaptive machine learning pipeline Watch the full presentation here: https://info.cnvrg.io/build-machine-learning-pipelines

DevOps/Flow workshop for agile india 2015

Yuval Yeret

Everybody Lies

Tomasz Kowalczewski

Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure. In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic. During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.

[DSC Europe 23] Vladislav Belov - ChatBot Learning Assistant with Large Langu...

DataScienceConferenc1

Join this talk as I reveal the fascinating journey of creating a cutting-edge bot assistant powered by Large Language Models, paving the way from POC to MVP. Prepare to dive deeper into: A unique multi-route architecture that forms the backbone of our bot's performance., Ingenious solutions to combat latency challenges for a seamless user experience., Techniques to tackle out-of-distribution questions and enhance the bot's adaptability. Don't miss the opportunity to discover valuable insights that could elevate your own bot development process. See you there!

Cynthia Lee ITEM 2018

ITEM

Talk - Essential Big-O for the DevOps engineer: quantifying scalability of software systems"? Big-O analysis is the workhorse of scalability analysis of software systems, but many people don't appreciate its importance in DevOps. This talk will introduce the essential intuitions and techniques behind Big-O analysis, with the goal of facilitating more precise communications between DevOps and software engineers who speak in Big-O terms, and applying the principles directly to DevOps scalability problems such as networking.

Agile DC Lead Time

azheglov

This document discusses lead time and how measuring and understanding it can help forecast projects. It defines lead time as the time between one event preceding another. Lead time data can be more useful than just average times since it accounts for variability. The document also discusses using distributions to model different types of work instead of single numbers, as processes are probabilistic rather than deterministic. Measuring lead time and understanding its distribution is important for planning delivery rates and work in process.

People want their computer services to operate well, and consequently they often set targets for how reliable they hope to be. In addition to target percentages, percentiles are often used to make it easier to examine the data for most user experiences, while ignoring the outliers. But problems often arise because of a tendency to only think about "nines". All services and user experiences are unique, and it is all too common to ignore important data or stress your systems and humans by trying to be more performant than you need to be. Throw away your nines, and let's find some better numbers to use instead.

Smarter Than Monkey, Meet APE

Yihsuan Chen

The document describes KKBOX's efforts to develop a smarter monkey testing tool called APE using machine learning. APE uses the TensorFlow Object Detection API to train a model on screenshots labeled with UI elements. It then deploys the trained model on Android and iOS to automatically detect and interact with elements during testing. Some key findings included that training with GPU is faster, active learning helps labeling, F1 scores measure model performance, minimizing labels improves learning, and grayscale training works well. The vision is that APE can help with localization testing by detecting languages and aid design patterns like page objects through element detection. In summary, the document discusses how KKBOX is leveraging machine learning for more intelligent automated testing.

Lead Time: What We Know About It...

azheglov

I gave this presentation at Lean Kanban Asia-Pacific conference in Bangalore, India on December 11th, 2014 and at AgileDC on Washington, USA on October 21st, 2014. I have several recent blog posts on this topic, This search link should get most of them: http://connected-knowledge.com/?s=lead+time. If you need one "best" post, here it is, Inside a Lead Time Distribution: http://connected-knowledge.com/2014/09/07/inside-lead-time-distribution/

People & Products – Lessons learned from the daily IT madness

inovex GmbH

IT im 21. Jahrhundert – What a time to be alive! Es gibt einen (unüberschaubaren) Zoo an Methoden und Produkten die uns so viel Freude an der Arbeit bereiten! Sie sind modern, weil sie neu sind. Sie fordern unser Können heraus, weil sie komplex sind. Sie lösen einige Probleme, die wir vorher nicht hatten. Jeder will sie verwenden, weil Google, Netflix & Co. sie propagieren und Hand auf’s Herz: Will nicht jeder gerne so arbeiten wie Google, Netflix & Co.? Aber macht das wirklich Sinn? In diesem Vortrag blicken wir auf diverse Erkenntnisse aus dem Einsatz agiler Produktentwicklung, DevOps, Continuous Integration/Delivery, Infrastructure as Code, Immutable Infrastructure (bspw. Docker/Kubernetes), Application Logging und Service Monitoring. Learning Goals: - Wir müssen den Einsatz von Methoden und Tools an die Menschen ausrichten, die sie (weiter-)entwickeln und benutzen sollen. - Manchmal lösen wir mit neuen Tools Probleme, die wir vorher nicht hatten. - Die Suche nach einfachen Lösungen für komplexe Probleme ist essentiell, aber nicht immer einfach. Event: Continuous Lifecycle, 15.11.2018 Speaker: Arnold Bechtoldt Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

99.9% of Your Traces are Trash

Paige Cruz

Practical deep learning for computer vision

Eran Shlomo

Stream your monitoring

Stanislav Osipov

Stanislav Osipov discusses tools and techniques for monitoring systems and services. He recommends classifying problems as datacenter failures, connectivity failures, or human errors. Most failures can be attributed to connectivity issues or human errors. He suggests using tools like Zabbix, New Relic, and custom scripts to monitor at the system, component, and service levels. Channels for alerts should include phone, email, mobile apps, and chat platforms. Escalation policies with time delays should notify appropriate on-call teams for different severity levels. Monitoring deployments, incidents, and uptime with CI/CD can help improve systems and services over time.

Стриминг мониторинга

Uptime community

Станислав Осипов — рекламные платформы; особенности R'n'D и Ops в рекламе. — три кита, на которых Zabbix можно превратить в полезный для восприятия инструмент. — табличка, патч и отчетность — отстрел Ops managers обратно на орбиту. — все не как у людей: стриминг самочувствия системы. — каналы (SMS, Tg, Sl, Ml), потоки/группы. — а теперь все вместе: Zabbix, New Relic, Jenkins и другие.

Стриминг мониторинга

Uptime Community

10 Keys to CMMS Implementation Success

eMaint Enterprises

How go makes us faster (May 2015)

Wilfried Schobeiri

This document summarizes a talk about how the Go programming language helps performance at MediaMath and how to make Go code perform well. It discusses how Go has helped MediaMath transition to a microservices architecture and how features like Go routines, channels, testing and tooling make Go productive. It also provides tips on optimizing Go code like avoiding garbage collection, using pprof, considering GOMAXPROCS, and being aware of limitations of interfaces and defers. While Go has drawbacks like its opinionated nature, the document argues it is a fast and productive language according to benchmarks and has been widely adopted at MediaMath.

What a DevOps specialist has to know about static code analysis

Andrey Karpov

Fifth elephant 2018 - Incremental Processing

Govind Pandey

Incremental transform of transactional data models to analytical data models in near real time Transactional systems are designed with data models to maximize write throughput across multiple parallel business flows. They evolve iteratively with business and need to react quickly to the changing business landscape to minimize time to market. Analytical systems, on the other hand, require data models to maximize query throughput over broad, deep and large data volumes. The need for a platform which transforms from the transactional data model to an analytical data model is well established in the industry. This is currently achieved through two different paradigms. Stream processing at lower latencies and batch processing at higher latencies. We have solved the same problem through a third paradigm of incremental processing for intermediate latencies (5 minutes to 1 hour). We considered and dropped implementations of the streaming paradigm either because of a lack of completeness guarantees or the absence of complex join capabilities across a large number of entities. Our incremental processing platform transforms transactional data models to analytical data models. It provides expressability for complex joins across multiple entities (live with 30) through a Transformation Definition Language (TDL). These complex joins are evaluated incrementally as transactional data changes to periodically update the analytical data model. For near real time use cases, this is done every 5-10 minutes. Changes to transactional data models are handled through version support in the TDL. These changes are absorbed with a pause and resume of the transformations. The Flipkart Fulfillment Services Group serves over a million shipments in a day at its peak. Customer delight through a reliable and fast delivery of orders is our primary goal. To succeed in this endeavour, our ground operations depends on live and accurate visibility into the journey of all shipments pan India. Overall data volumes range in 10s of TBs with a change frequency of over 25k QPS at peak. All our transactional systems combined generate mutations with volumes close to 200 GB every second. Our incremental platform is built to handle this scale. With this platform, we have achieved analytics at low latencies with high completeness without compromising on business agility. In this talk, I will cover the specifics of our evaluations and our learnings from the journey of building the platform.

The Final Frontier, Automating Dynamic Security Testing

Matt Tesauro

This is not your normal DevSecOps presentation. We’re going to take on the most difficult aspect of security automation, the dreaded and pitfall prone, dynamic testing. You want to shift left and automate all the things, but DAST specifically has many thorns. How do you ensure what you’re testing matches production? Do devs own the environment? On metal, docker, kubernetes, or docker-compose? Test coverage? Balancing all these elements and more is not easy. Especially if you want to create a single, scalable, standard for your entire org. In this talk, we’ll cover what is needed to start automating your dynamic security testing, how to navigate the trade-offs you’ll have to consider, and finally how best to fit automated DAST testing into your software delivery pipelines. We’ll discuss simple and easy steps to gain efficiency and how to scale to mature pipelines that require little to no human intervention.

Using innovative CBT for nationwide educational exams

Cito

The document discusses plans to deliver national educational exams in the Netherlands using innovative computer-based testing (CBT). It describes the development of a new system called Facet to deliver over 2-3 million CBT exams annually in a stable, secure way. Facet was created through collaboration between exam boards, the Ministry of Education, and other partners. It has evolved through an agile development process and is now a stable product hosted in DUO's infrastructure, with plans to eventually host it in the cloud to support delivering exams to 8000 schools. The document also covers requirements for innovative exam content and the use of open standards like QTI (Question and Test Interoperability).

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This document discusses three ways to build command line interfaces (CLI) using Zend Framework: freestyle, Zend_Tool, and creating a custom CLI application using Zend Framework MVC. It describes building a "bugU" CLI application that makes phone calls using the Tropo API as an example of the custom approach. The custom approach overrides default objects like the bootstrap, request, and response to build a CLI application without new skills while reusing existing Zend Framework code. Code samples of the custom bootstrap, request, router, and controller classes are presented.

Auditing your data and answering the life long question, is it the end of the...

HostedbyConfluent

Over here at Nielsen, data is very important to us. Being the core of our business, we love it and there’s lots of it. We don’t want to lose it, and at the same time, we don’t want to duplicate it. Our data goes through a robust Kafka architecture, into several ETLs, receiving, transforming and storing the data. While we clearly understood our ETLs’ workflow, we had no visibility into what parts of the data, if any, were lost or duplicated, and in which stage or stages of the workflow, from source to destination. But how much do we know about the way our data makes though our systems? And what about the life long question, is it the end of the day yet? In this talk I’m going to present to you the design process behind our Data Auditing system, Life Line. From tracking and producing , to analysing and storing auditing information, using technologies such as Kafka, Avro, Spark, Lambda functions and complex SQL queries. We’re going to cover: * AVRO Audit header * Auditing heart beat - designing your metadata * Designing and optimising your auditing table - what does this data look like anyway? * Creating an alert based monitoring system * Answering the most important question of all - is it the end of the day yet?

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University of Sindh

This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.

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