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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 Amazon SageMaker - Automating ML Pipelines 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 Amazon SageMaker - Automating ML Pipelines 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 Amazon SageMaker - Automating ML Pipelines 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.
Dive into SageMaker Studio to create secure collaborative notebooks, configure Python environments with ML libraries like scikit-learn and TensorFlow, prepare massive datasets via automated processing jobs, clean and transform real enterprise data with Pandas and SageMaker Data Wrangler, perform practical exercises on a concrete customer prediction case, produce training-ready datasets with included quality reports.
Launch distributed training jobs on GPU to train XGBoost or deep learning models, optimize hyperparameters via Automatic Model Tuning on large datasets, deploy scalable serverless endpoints with A/B testing, integrate real-time monitoring via Model Monitor to detect drift, test batch and real-time inferences on business use cases, generate deliverables like CloudWatch dashboards and cost optimization plans, validate skills through an end-to-end project deployment.
Target audience
Data scientists, ML engineers, AI architects in enterprise settings looking to upskill
Prerequisites
Proficiency in Python, basics in machine learning, and an active AWS account
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