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Bio
In MLOps, I've focused on streamlining the machine learning lifecycle, from model development to deployment and maintenance, by integrating best practices from DevOps and data engineering. My experience includes setting up automated pipelines for data preprocessing, model training, evaluation, and deployment using tools like Docker, Kubernetes, and Jenkins. I've implemented version control for machine learning models and datasets using platforms like Git and GitLab, enabling collaboration and reproducibility across teams. Additionally, I've worked on monitoring model performance and drift detection in production environments, ensuring that deployed models remain accurate and reliable over time. Furthermore, I've collaborated with cross-functional teams to establish governance frameworks and compliance measures for machine learning systems, addressing issues related to data privacy, security, and ethical considerations. Overall, my goal in MLOps is to optimize the deployment and management of machine learning models, enabling organizations to derive maximum value from their AI initiatives.-
Expérience
2 années -
Prix pour le temps
10 $ -
Nombre d'heures gratuites
0 heures
