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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 Kubeflow: Getting Started and MLOps Deployment on Kubernetes 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 Kubeflow: Getting Started and MLOps Deployment on Kubernetes 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 Kubeflow: Getting Started and MLOps Deployment on Kubernetes 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.
Overview of MLOps and scaling challenges. Presentation of Kubeflow: history, use cases, general architecture, key components (Pipelines, Katib, KFServing, etc.). DevOps/MLOps approach specific to ML projects. Environment preparation (Kubernetes cluster, Helm, storage, optional cloud resources).
Step-by-step Kubeflow installation (on Minikube, GKE, EKS, or AKS). Access management and authentication (Dex, IAP...). Integration with data storage solutions and persistent volume management. Getting started with the Kubeflow interface. Use of Workspaces and collaborative organization. Securing pipelines, quotas, and monitoring with Prometheus/Grafana.
End-to-end ML pipeline creation on Kubeflow: data ingestion, training, validation, deployment. Advanced use of Kubeflow Pipelines (templates, dynamic parameters, resilience, cache, artifacts, TFX). Hyperparameter tuning with Katib. Model deployment with KFServing. Supervision, monitoring, and centralized logging. Automation, versioning, and MLOps best practices (CI/CD, GitOps with ArgoCD). Final case study and feedback.
Target audience
Data engineers, data scientists, cloud architects, DevOps engineers with experience in machine learning and containerization
Prerequisites
Intermediate knowledge of Python, machine learning, Kubernetes, and containerization (Docker)
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