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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.
10 spots per session maximum — 7 already taken
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Without mastering Pydantic for robust data validation and serialization in Python, developers expose APIs to unchecked invalid inputs, leading to corrupted datasets and cascading failures in FastAPI or similar frameworks.
Industry reports indicate that 42% of Python application outages stem from data validation gaps, costing teams an average of 12 hours per incident in debugging and $140,000 annually per mid-sized project in lost productivity.
Unaddressed, these errors invite compliance violations, security breaches, and eroded stakeholder trust, stalling company growth and hindering career progression in data-driven roles.
Every month without this expertise, risks escalate as data volumes surge.
The Training: Mastering Pydantic - Data Validation and Serialization in Python 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: Mastering Pydantic - Data Validation and Serialization in Python 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: Mastering Pydantic - Data Validation and Serialization in Python training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Presentation of typing in Python and motivations leading to Pydantic. Installation and environment management. Creation of simple data models, native types, basic constraints, automatic validation, error handling. Exercises: first models and validations (emails, integers, optionals).
Management of complex data: lists, dictionaries, nested models, optional schemas, default values. Implementation of custom validators and use of decorators. Global model configuration and behavior adaptation (case sensitivity, extra fields, ORM mode). Practical exercises and case studies.
Serialization and deserialization of Pydantic objects (JSON, dict, etc.). Introduction to integration with FastAPI: request body validation. Best practices for structuring, unit tests, automatic documentation, schema version management. Comparison with other libraries, migrating existing code. Implementation of an ongoing mini-project.
8 participants max.
$2,541 / per person
2 days
remoteTarget audience
Python developers, data analysts, and engineers wishing to make data management reliable and develop robust APIs with FastAPI or other frameworks.
Prerequisites
Mastery of Python basics and fundamental concepts of objects (classes), experience with data manipulation in Python recommended.
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