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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training Grok: Mastering Observability AI for Engineers and Developers training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training Grok: Mastering Observability AI for Engineers and Developers training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training Grok: Mastering Observability AI for Engineers and Developers training is carried out through:
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Origins and philosophy of the Grok project. From classic log parsing to intelligent automation. Use cases in various IT contexts. Basic concepts: pattern matching, log structuring, big data, introduction to the role of AI.
Step-by-step installation of Grok and configuration. Exploration of predefined patterns and their use on real log datasets. Creation and maintenance of custom patterns tailored to specific applications. Testing and optimization of recognition using CLI and integration tools (ELK, SIEM, etc.).
Implementation of automatic processing for detecting atypical events. Use of machine learning models to enrich and contextualize log analysis. Best practices for managing false positives/negatives and maintaining effective and secure observability. Hands-on workshop: complete creation of an intelligent pipeline for an enterprise SIEM based on Grok and Python.
Target audience
DevOps engineers, developers, and IT managers seeking to enhance observability and automation of their infrastructure via Grok
Prerequisites
Familiarity with logs and monitoring concepts, basics in Python or another scripting language
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