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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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Artificial Intelligence training in Cardiff in May 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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The Training: Automation and Parameterization of Jupyter Notebooks with Papermill: Professional Mastery 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: Automation and Parameterization of Jupyter Notebooks with Papermill: Professional Mastery 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: Automation and Parameterization of Jupyter Notebooks with Papermill: Professional Mastery training is carried out through:
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Discover a step-by-step roadmap to become a skilled AI engineer by March 2026. From prerequisites to advanced projects, tools, and job strategies, this guide covers everything for aspiring professionals.
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Presentation of the limitations of classic Jupyter Notebooks. Introduction to Papermill: uses, architecture, and installation. Parameterized execution of notebooks: defining and injecting parameters from the command line or via Python. Manipulating parameters, dynamic rendering of results, and personalized reporting. Capturing and storing results (outputs, logs, metadata). Organizing execution directories; managing notebook versions and reproducibility. Handling errors and exceptions during automated execution. Use cases: automating recurring analyses or model batches. Introduction to integrating Papermill into workflows (Airflow, CI/CD, etc.). Hands-on individual practice: full automation of a data analysis notebook with multiple parameter sets, configuration of automated reporting pipelines, unit tests, and industrialization best practices. Experience sharing and common pitfalls. Evaluation: supervised mini-project in autonomy.
Target audience
Data analysts, data scientists, data project managers, developers wishing to industrialize and parameterize their Jupyter workflows
Prerequisites
Good knowledge of Python, basics of Jupyter Notebooks, notions on data manipulation
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