Having detailed information about virtual infrastructure "as is" is not enough for effective management, financial governance, security assessment, and remediation.
While raw data can be useful, processing it for large cross-cloud infrastructures demands significant time and effort. This is where automated, ML-empowered analytics come into play. Maestro incorporates an effective approach that leverages analytics and recommendation mechanisms from public cloud providers, along with its own tools.
The analysis produces recommendations to improve virtual infrastructures across various dimensions. Users can adopt a recommendation-oriented view of their infrastructure, with specific recommendations tailored to different roles and goals. For instance, security insights target security experts, performance insights assist production support experts, and general best practices guide resource owners.
We’ve enhanced Maestro to easily integrate custom engines. All recommendations are gathered, processed, and delivered to users in a unified manner—via the dashboard, management page, and emails.
The Dashboard aggregates risk factor information, summarizing the status based on all findings for a specific resource. This allows for a quick assessment of the infrastructure's state and identifies the most critical issues needing attention.
The Management tab, with its new Insights feature, provides detailed information on per-resource issues, sortable and filterable by severity and insight source.
The Content View within the Management tab offers comprehensive information about the insights generated for a selected resource. It allows users to address issues using relevant wizards or tools, or to ignore recommendations if they are not applicable.
This approach has proven effective, providing a unified entry point for performance, cost, security, and best practices recommendations across all clouds. It also includes a remediation mechanism.
However, there is always room for improvement. We already have a roadmap for enhancing recommendations and risk factor assessments, aiming to make the mechanism more flexible and adaptable to specific infrastructure lifecycles and workloads.
While raw data can be useful, processing it for large cross-cloud infrastructures demands significant time and effort. This is where automated, ML-empowered analytics come into play. Maestro incorporates an effective approach that leverages analytics and recommendation mechanisms from public cloud providers, along with its own tools.
The analysis produces recommendations to improve virtual infrastructures across various dimensions. Users can adopt a recommendation-oriented view of their infrastructure, with specific recommendations tailored to different roles and goals. For instance, security insights target security experts, performance insights assist production support experts, and general best practices guide resource owners.
We’ve enhanced Maestro to easily integrate custom engines. All recommendations are gathered, processed, and delivered to users in a unified manner—via the dashboard, management page, and emails.
The Dashboard aggregates risk factor information, summarizing the status based on all findings for a specific resource. This allows for a quick assessment of the infrastructure's state and identifies the most critical issues needing attention.
The Management tab, with its new Insights feature, provides detailed information on per-resource issues, sortable and filterable by severity and insight source.
The Content View within the Management tab offers comprehensive information about the insights generated for a selected resource. It allows users to address issues using relevant wizards or tools, or to ignore recommendations if they are not applicable.
This approach has proven effective, providing a unified entry point for performance, cost, security, and best practices recommendations across all clouds. It also includes a remediation mechanism.
However, there is always room for improvement. We already have a roadmap for enhancing recommendations and risk factor assessments, aiming to make the mechanism more flexible and adaptable to specific infrastructure lifecycles and workloads.