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Azure Databricks - Build data engineering and AI/ML pipeline


This course is designed to help you develop the skill necessary to perform ETL operations in Databricks, build unsupervised anomaly detection models, learn MLOPS, perform CI/CD operations in databricks and Deploy machine learning models into production.

Machine Learning with a Reject Option: A survey Artificial Intelligence

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with a reject option recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with a reject option. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection. Moreover, we define the existing architectures for models with a reject option, describe the standard learning strategies to train such models and relate traditional machine learning techniques to rejection. Additionally, we review strategies to evaluate a model's predictive and rejective quality. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.

ESP32 Anomaly detection using Edge Impulse and machine learning


This tutorial covers how to implement an ESP32 anomaly detection system using Edge Impulse and machine learning. In more detail, we will detect when there is an anomaly in the CO2 concentration and volatile organic compounds. Therefore, we will implement a machine learning model that is capable of identifying if there are some values outside the normal range. To measure the concentration of CO2 and volatile organic compounds we will use ESP32 and CCS811. In other words, anomaly detection is the activiy that identifies those values and observations that do not adhere to a pattern that is considered a normal pattern. To achieve this goal, we will use machine learning and ESP32 in order to identify those values, retrieved from the sensor, that do not belong to the normal pattern.