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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 - Optimize 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 - Optimize 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 - Optimize ML Features in Production training is carried out through:
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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.
Quick installation of Feast on a cloud or local environment, hands-on with registries to store features and metadata, creation of feature views for concrete ML datasets such as customer prediction, practical exercises on feature extraction and transformation from SQL and Parquet sources, development of a first offline pipeline with automatic validation, case study of an e-commerce company to identify quick wins, production of a deliverable: versioned feature schema ready for the data team.
Integration of Feast with Kafka for real-time streaming flows and Spark for massive batch processing, configuration of scalable online serving with Redis, performance optimization via caching and point-in-time joins on real fraud detection cases, setup of monitoring with Prometheus to detect feature drifts, exercises on collaborative governance and secure access, simulated production deployment with CI/CD via GitHub Actions, finalization of the capstone project with an operational Feast dashboard, immediate value to accelerate your ML models by 50% in the enterprise.
Target audience
Data engineers, ML engineers, data scientists seeking to advance their skills in professional feature engineering
Prerequisites
Mastery of Python, SQL, and machine learning fundamentals; experience with data pipelines such as Spark or Airflow
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