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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 ONNX Runtime - Accelerate ML Inference 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 ONNX Runtime - Accelerate ML Inference 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 ONNX Runtime - Accelerate ML Inference 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 ONNX Runtime in Python and C++, conversion of TensorFlow/PyTorch models to ONNX, configuration of optimized environments with CUDA and TensorRT, practical exercises on performance benchmarks, development of a scalable inference API with Flask, latency tests on real enterprise cases, production of optimization reports and cloud-ready deployment plan to accelerate your AI projects by 5x.
Target audience
Data scientists, ML engineers, and AI developers for professional deployment upskilling
Prerequisites
Mastery of Python, basics in Machine Learning, and experience with TensorFlow or PyTorch
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