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Deploy and manage machine learning tools like Kubeflow, MLflow, or Vertex AI to streamline workflows and support end-to-end ML operations.
Maintain visibility and control over resource usage, security policies, and model lifecycle management to meet organizational standards.
Standardize tools and workflows to foster better alignment and communication between Data Scientists, ML Engineers, and Developers. Allow ML teams to focus on their strengths by providing robust infrastructure and removing operational hurdles.
Equip your teams with proactive monitoring and complete visibility into applications and infrastructure, enabling faster issue resolution and improved reliability.
Bake security best practices into your workflows from day one, ensuring your platform adheres to industry standards and protects your operations.
Simplify the path from ML experiments to production-grade inference services, reducing bottlenecks and delays.
Provide distributed compute environments optimized for faster model training and resource efficiency, ensuring scalability for large workloads.
Maintain reusable container images and environments tailored to the needs of ML teams, reducing setup time and complexity.
Automate pipelines for model training, testing, deployment, and monitoring, ensuring seamless transitions from development to production.
Achieve efficient resource utilization through automation and intelligent infrastructure management, reducing operational overhead.
Enable self-service provisioning of resources and namespaces for ML teams, offering flexibility while maintaining governance and oversight.
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Where are you located?
ITMAGINATION is based in the heart of Europe in Poland.
Our headquarters is located in Poland in Warsaw's city center, but we are a remote-first company where our team works from 20 countries across the European Union.
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