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Update README.md
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@ -21,9 +21,9 @@ ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows**
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* Implement optimized resource utilization policies
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* Deploy execution environments with either virtualenv or fully docker containerized with zero effort
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* Launch-and-Forget service containers
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* [Cloud autoscaling](https://allegro.ai/clearml/docs/examples/services/aws_autoscaler/aws_autoscaler/)
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* [Customizable cleanup](https://allegro.ai/clearml/docs/examples/services/cleanup/cleanup_service/)
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* Advanced [pipeline building and execution](https://allegro.ai/clearml/docs/examples/frameworks/pytorch/notebooks/table/tabular_training_pipeline/)
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* [Cloud autoscaling](https://allegro.ai/clearml/docs/docs/examples/services/aws_autoscaler/aws_autoscaler.html)
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* [Customizable cleanup](https://allegro.ai/clearml/docs/docs/examples/services/cleanup/cleanup_service.html)
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* Advanced [pipeline building and execution](https://allegro.ai/clearml/docs/docs/examples/frameworks/pytorch/notebooks/table/tabular_training_pipeline.html)
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It is a zero configuration fire-and-forget execution agent, providing a full ML/DL cluster solution.
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@ -122,7 +122,7 @@ The ClearML Agent executes experiments using the following process:
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#### System Design & Flow
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<img src="https://allegro.ai/clearml/docs/img/ClearML_Architecture.png" width="100%" alt="clearml-architecture">
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<img src="https://allegro.ai/clearml/docs/_images/ClearML_Architecture.png" width="100%" alt="clearml-architecture">
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#### Installing the ClearML Agent
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