Lieu : Grenoble, Lyon or Remote · Contrat : Stage · Rémunération : 1000 Euros/month (gross) €
Ryax Technologies est une startup dont l'objectif est d'automatiser les analyses de données dans les entreprises. Nos clients vont de la pharma au digital marketing en passant par l'industrie lourde.
Pour cela nous avons développé une technologie permettant de créer des chaînes de traitements rapidement et intuitivement et d'ensuite exécuter ces chaînes sur n'importe quelle infrastructure informatique.
We are currently going through the industrial digital transformation era, also known as Industry 4.0. In this context all entities involved in the smart factories of Industry 4.0, such as machines, people, sensors, actuators, and software modules, are interconnected. This enables manufacturing data to be gathered, monitored, analyzed, and computed to automatically and intelligently control and improve manufacturing processes. Artificial intelligence (AI) and specifically its branches of Machine and Deep Learning are the practices which use algorithms to learn from the data and make predictions or take autonomous actions. The data collected for AI contains very useful information and valuable knowledge which can improve the whole productivity of industrial processes and can also be applied in condition-based maintenance and health monitoring. To this end, predictive maintenance is based on the continuous monitoring of the industrial equipment and makes use of prediction tools to measure when such maintenance actions are necessary. This process enables failures and anomalies detection at an early stage based on both real-time and historical data by combining ML/DL methods, statistics, visual aspects and engineering techniques.
‘Ryax Technologies’ is a deep tech startup providing a Data Ops platform to automate and orchestrate workflows of data analytics in hybrid infrastructures. The RYAX platform performs the complex data engineering "plumbing" to deal with the underlying distributed systems while abstracting their complexity through a simple to use interface to allow data scientists to deploy their ML/DL algorithms upon a distributed computing environment. Furthermore RYAX provides features to enable a seamless execution of both batch and real-time stream processing tasks, simplifying the way real-time analytics are handled.
The goal of this internship is to leverage real-time data coming from IoT sensors to allow industrial companies the identification of malfunctioning assets by detecting usage anomalies in real time and enable predictive maintenance by detecting global usage anomalies. For that the intern will use open-source data and already existing open-source base code to build a first basic predictive maintenance use case in an industrial context. The intern will use technologies such as Kafka for real-time sensor observation, Spark-Streaming for the execution and Superset for the visualization. The intern will deploy the whole architecture using RYAX platform.
Once this first basic use case is built and the necessary experience with the used technologies has been made the intern will be focused on evaluating the performance of the solution upon RYAX platform by measuring aspects such as the latency of the measures and the efficiency of the integrations. Based on these assessments the intern will propose optimizations in the way that the integrations of different tools have to be used by RYAX considering the combination of real-time and historical data. Optimizations in both the data science algorithms along with the data engineering integrations may be considered. The intern will develop in Python and will make use of known open source tools such as Kubernetes, Docker, Kafka, RabbitMQ, Spark, Superset, etc. The intern should ideally have a data engineering or data science background and must be confident in at least one language such as Python, R or Go. Experience with C, C++ or Java along with Docker containers and deep learning frameworks will be a plus.
Contact: Yiannis Georgiou: email@example.com