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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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Artificial Intelligence training in Raleigh in June 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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Professional Training training in New York in September 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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The Training Amazon SageMaker - Deploying Scalable ML Pipelines 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 - Deploying Scalable ML Pipelines 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 - Deploying Scalable ML Pipelines 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.
Discover SageMaker pipeline architectures for distributed processing of massive datasets, configure Processing Jobs with Spark and custom containers, perform practical exercises on real feature stores, integrate real-time S3 data, produce automated data quality reports, apply scalable transformations on concrete enterprise cases, validate deliverables via interactive SageMaker dashboards.
Dive into automated hyperparameter tuning with Bayesian Optimization, train distributed models on GPU/TPU via SageMaker Training, deploy elastic endpoints with autoscaling, test live A/B experiments, optimize latency via Triton compilation, integrate custom ONNX models, generate production-ready artifacts, analyze metrics via CloudWatch in high-load scenarios.
Implement end-to-end MLOps pipelines with SageMaker Projects and Pipelines, configure Model Registry for advanced governance, enable Clarify for bias detection and explainability, monitor model drift in real-time via Model Monitor, automate rollbacks and retrainings, integrate with Kubernetes EKS for hybridization, deploy secure solutions with VPC and fine-grained IAM, conclude with a deliverable and certifiable capstone project.
Target audience
Senior data scientists, ML engineers, AI architects, and expert DevOps professionals seeking advanced skills in SageMaker for enterprise environments
Prerequisites
Advanced proficiency in Python, AWS (EC2, S3, Lambda), SageMaker Studio, ML frameworks (TensorFlow, PyTorch), basic CI/CD pipelines, and MLOps
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