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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: Master Amazon SageMaker: Develop and Deploy Machine Learning Models on the AWS Cloud 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: Master Amazon SageMaker: Develop and Deploy Machine Learning Models on the AWS Cloud 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: Master Amazon SageMaker: Develop and Deploy Machine Learning Models on the AWS Cloud 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.
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Presentation of the AWS environment, discovery of the SageMaker Studio interface, notebook management, introduction to S3 storage and IAM permissions. Importing and initial exploration of datasets in SageMaker.
Data preparation and cleaning techniques with SageMaker Processing. Use of built-in algorithms (XGBoost, Linear Learner, etc.), experiment management, hyperparameter tuning, implementation of interactive notebooks for training.
Model deployment via managed endpoints (real-time inference, batch transform), lifecycle management with SageMaker Pipelines, monitoring, MLOps, security, costs, and best practices for industrialization on AWS.
Target audience
Data scientists, machine learning engineers, developers, technical decision-makers wishing to industrialize their AI projects on AWS
Prerequisites
Prior knowledge in Python, machine learning, and basic AWS cloud concepts
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