Presentation: A Cost-Sensitive Approach for Resource Allocation in Virtual Machines

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 1:05pm - 1:55pm

Day of week: Wednesday

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Abstract

Throughout recent years, ING has made a shift from hosting processes on designated physical servers to virtual machines (VM) warehouses. While this transition has contributed to ING’s development teams in providing teams agility and elasticity in resource allocations, the potential for cost reduction on infrastructure spending has not fully been realized. Many VM’s have not been shifting their resource allocation actively according to their usage, resulting in a yearly expense of over 60M EUR on (often idle) computing infrastructure.

In this application talk, Dor will take the audience step by step in the process of building an inner-organizational data science solution. Dor will share insights on the time-series model for predicting usage, the optimization which minimizes costs and risks, the process of deploying data science models to production and some best practices of creating a data science model in an agile methodology.

Speaker: Dor Kedem

Senior Data Scientist @ING Nederland

Dor has over a decade of experience developing big data products for security industries, financial markets and banking industries. His research on metric learning and cost-sensitive learning has earned him publications in NIPS, AISTATS and a monetary prize in Cha-Learn competitions. As a data scientist at ING domestic banking, he is involved with multiple projects modelling consumer and market behavior, optimizing business and IT processes.

Find Dor Kedem at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.