Video Training →Monitoring Multi-cloud
Published by: LeeAndro on 6-11-2020, 10:15 | 
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Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 349 MB | Duration: 1h 4m
In this course, we will discuss the concept of AI Explainability, the role of CloudOps Explainability in managing Multicloud solutions; the effect of Explainability on the robustness, security, and privacy aspects of Predictive Systems; the use of Qualitative and Quantitative validation approaches along with the Explainability techniques to define operational and functional derivatives of CloudOps.
What you'll learn
Monitoring Multi-cloud
Requirements
Basic knowledge on cloud
Description
We will also discuss how to apply Explainability throughout the process of operating Cloud environments and infrastructures, the methodologies involved in the three stages of AI Explainability in deriving the right CloudOps model, the role of Explainability in defining AI-assisted Cloud Managed Services, the architectures that we can derive using Explainable Models, the role of Explainable AI Reasoning paths in building trustable CloudOps workflows along with the need for management and governance of AI Frameworks in CloudOps architectures.
In addition to the above topics, we will also learn about how to work with AWS SNS to decouple microservices, AWS Rekognition to analyze video and image; and AWS SageMaker to manage end-to-end machine learning service.
Who this course is for:
Cloud enthusiastics
Multi-cloud
Cloud Architects
DevOps professionals
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