October 26, 2023
Europe/Prague timezone


According to the International Society of Automation, $647 billion is lost globally each year due to downtime from machine failure. Organizations across manufacturing, aerospace, energy and other industrial sectors are overhauling maintenance processes to minimize costs and improve efficiency. With artificial intelligence (AI) and machine learning, organizations can apply predictive maintenance to their operation, processing huge amounts of sensor data to detect equipment failure before it happens. Predictive maintenance gets ahead of the problem compared to routine or time-based preventative maintenance and can save a business from costly downtime.

In this Deep Learning Institute (DLI) workshop, developers will learn how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions. They’ll learn how to prepare time-series data for AI model training, develop an XGBoost ensemble tree model, build a deep learning model using a long short-term memory (LSTM) network, and create an autoencoder that detects anomalies for predictive maintenance. At the end of the workshop, developers can use AI to estimate the equipment condition and predict when maintenance should be performed.

Learning Objectives

In this workshop, developers will learn how to:

  • Use AI-based predictive maintenance to prevent failures and unplanned downtimes
  • Identify key challenges around detecting anomalies that can lead to costly breakdowns
  • Use time-series data to predict outcomes with XGBoost-based machine learning classification models
  • Use an LSTM-based model to predict equipment failure
  • Use anomaly detection with time-series autoencoders to predict failures when limited failure-example data is available






Experience with Python; basic understanding of data processing and deep learning.

Hardware requirements

Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.

Tools, libraries, and frameworks

Python, TensorFlow, Keras, XGBoost, NVIDIA RAPIDS™, cuDF, LSTM, autoencoders, artificial intelligence, deep learning


Georg Zitzlsberger is a research specialist for Machine and Deep Learning at IT4Innovations. For over four years, he has been certified by NVIDIA as a University Ambassador of the NVIDIA Deep Learning Institute (DLI) program. This certification allows him to offer NVIDIA DLI courses to users of IT4Innovations' HPC services. In addition, in collaboration with Bayncore, he was a trainer for Intel HPC, and AI workshops and conferences carried out across Europe. He has been contributing to these events, which are held for audiences from industry and academia, for five years. Recently, he also received instructor certifications from Intel for oneAPI related courses.


Upon completing the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.                                                             


This training will be a hybrid event. Technical details about joining will be sent to the accepted registrants before the event. If you are coming to IT4Innovations to attend personally, please bring your laptop.

Please note that the training is held using Zoom. We advise all participants to download the Zoom application to enjoy full functionality. The link to the Zoom meeting will be sent only to registered participants.

The capacity is limited to 30 participants combined online and onsite.

Price Information

Full price per person: 10 000 CZK

Subsidized price: 1 500 CZK

Terms of subsidy and cancellation






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