Description
Designing industrialized ML pipelines, effectively monitoring models in production, and implementing deployment and scaling strategies tailored to the performance, reliability, and cost constraints of an enterprise environment.
Learning Objectives
- Design an end-to-end automated MLOps pipeline.
- Orchestrate ML workflows with Kubeflow, Vertex AI Pipelines, and SageMaker Pipelines.
- Implement a feature store and manage features in production.
- Manage versioning, model registration, and promotion.
- Detect data drift and model degradation.
- Deploy A/B testing and progressive rollout strategies.
- Optimize GPU allocation and scheduling on Kubernetes.
- Select a serving strategy: batch or real-time.
Target Audience
ML Engineers
MLOps Engineers
DevOps Engineers
Data / AI / Cloud Architects
ML Platform Managers
Prerequisites
Strong proficiency in Machine Learning
Experience in Python and model deployment
Knowledge of Docker and Kubernetes
Familiarity with cloud environments or data pipelines
Program Outline
Informations
Duration
3 jour(s)
21h
Tarif
Sur demande
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