time series anomaly detection

the goal of my work is to identify anomaly jobs runing in cloud using job performance KPIs

I have A huge number of jobs over the cloud and each job where executed on a number of nodes

and each node is monitored by a large number of KPIs

so each job KPI(ex: CPU usage ) represents a time series of all nodes CPU usage used at the time the job was executed

**************** using Pyspark **********************************************

I need someone who has good experience in Pyspark especially the data frame API to do the following tasks:

1- data preprocessing

2- feature selection and extraction

3- apply clustering algorithms

4- identify the optimal number of clustering

5- compare between the three techniques

Compétences : Python, Spark, Machine Learning (ML), Apache Hadoop, Linux

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Concernant l'employeur :
( 0 commentaires ) cairo, Egypt

Nº du projet : #23408789

14 freelance font une offre moyenne de $163 pour ce travail


I am a Data Scientist with 3+ years of experience in Data Analysis, Statistical Modelling, Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. I have worked across various domains such as Plus

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(36 Commentaires)

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(23 Commentaires)

*** Python Anomaly Detection with LSTM *** Thank you for your attention. I read the project description with interest. I strongly believe I am the proper candidate. I fit all the requirements you mentioned, includi Plus

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(15 Commentaires)

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Highly effective Big Data Engiener with over 25 years of experience. I have extensive Python / Spark experience both in PySpark and Scala also in MLLib. I checked your Scala code and should be able to convert it to PyS Plus

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SAS Certified Base Programmer from SAS Institute, USA Proficient with Hadoop, Big Data and Agile methodology Power user of tools like Tableau, Spotfire, SQL, Alteryx, Hive, Spark, R, Python, UNIX

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