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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 Feature Store (Feast) - Scaling ML Features in Production 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 Feature Store (Feast) - Scaling ML Features in Production 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 Feature Store (Feast) - Scaling ML Features in Production 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.
Dive into the installation and configuration of Feast in a cloud environment, creation of registries to organize thousands of reusable features for your ML teams, development of providers for offline and online stores with Redis and BigQuery, practical exercises on batch and streaming data ingestion via Kafka, implementation of a serving layer for real-time predictions, concrete enterprise case with A/B testing of models, production of a deployed and documented Feast pipeline ready for production.
Target audience
Data Engineers, ML Engineers, Data Scientists for skill advancement in feature engineering
Prerequisites
Mastery of Python, SQL, ML pipelines and tools like Spark or Kafka
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