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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: Mastering the Deployment and Management of MLOps Workflows with Kubeflow 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: Mastering the Deployment and Management of MLOps Workflows with Kubeflow 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: Mastering the Deployment and Management of MLOps Workflows with Kubeflow 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.
Introduction to Kubeflow in the Kubernetes ecosystem, presentation of the components (Pipelines, Katib, Fairing, KFServing, Argo), step-by-step installation of Kubeflow on a Kubernetes cluster (GCP, Minikube or on-premise), user management and security, exploration of the graphical interface and initial configuration.
Definition of an ML pipeline, hands-on with Kubeflow Pipelines, designing pipelines in Python, dependency management (Data Preparation, Training, Validation, Deployment), integration with Jupyter notebooks, execution monitoring, artifact management and versioning, introduction to Argo Workflows in Kubeflow.
Model deployment with KFServing, prediction endpoint management, automatic scaling concepts with Kubernetes and resource management, activity and performance monitoring, testing and CI/CD for ML workflows (GitOps example with ArgoCD), A/B testing management, introduction to hyperparameter optimization (Katib), best practices for security and governance.
Target audience
Engineers and data scientists who wish to automate and industrialize their machine learning models on Kubernetes
Prerequisites
Good knowledge of Python, supervised/unsupervised machine learning, and Kubernetes basics (pods, services, deployments)
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