Cisco announced seven new modules on the Cisco Observability Platform, built by development partners and created to expand its full-stack observability ecosystem.
This growing ecosystem helps customers fulfill their specific observability needs and utilize additional value from observable telemetry.
New partner modules are focused around five critical themes:
- Business Insights: Correlate telemetry data with business performance across multiple domains, providing customers with full visibility and insights on how business interacts with IT.
- SAP Visibility: Help customers achieve holistic observability across often changeable, expanding and complex SAP landscapes and ecosystems.
- Networking: Leverage Cisco’s networking expertise to correlate key network telemetry with business metrics and application stack.
- MLOps and SLO: With the growing use of generative AI and the mainstream of modern applications, the Cisco Observability Platform helps customers to monitor these applications, their SLO and bring the monitoring of large language models (LLMs), and MLOps models together with application observability.
- Sustainability: Help customers achieve their sustainability goals by providing data around the carbon footprint across multiple IT domains and help optimize around energy consumption.
The following modules are available on the Cisco Observability Platform exchange at Partner Summit:
- CloudFabrix - SAP Observability: Enables customers to ingest data from Cisco AppDynamics agents for SAP Monitoring. It correlates telemetry data and asset types together to isolate the root cause of issues in the SAP landscape and determine the effect of impacted services on the business.
- CloudFabrix - Campus Analytics: Provides network analytics for campus environments as employees return to offices. This module aggregates multiple Cisco DNA Controller analytics to provide near real-time network topology information, bandwidth consumption and hotspot visibility.
- Evolutio - Claims: Insurance institutions are looking to gather multiple claims processes in a single pane of glass to best understand process health and user-experience. The module helps to correlate and view the health of different claims processes in real-time as it relates to product types, underwriters, regions, and business units.
- Evolutio - eCommerce: With the growth of ecommerce and the technology that powers it, organizations need a solution to track every part of the shopping experience. This module allows the correlation by product category or region, by monitoring of orders, shipping, inventory, and payments to quicky identify issues against the supporting infrastructure and applications.
- DataRobot – MLOps by Evolutio: Extends observability for both predictive AI and generative AI, with always-on monitoring and production diagnostics to track and improve performance of your models. Stay informed of key metrics like service health, accuracy and data drift.
- Nobl9 – Service Level Objectives (SLO): Provides a platform for defining and creating SLO for understanding reliability across organizations and share remaining error budget for given services as well as SLO-related visualizations for workloads.
- Climatiq - Cloud Carbon Insights: Adds carbon emission tracking to existing cloud metrics and enables analyzing, comparing, and benchmarking emissions data. These actionable insights accelerate journeys to net zero and make more environmentally conscious decisions.
In addition, the following modules will be available soon:
- Cisco CX - Sustainability Insights: Provides a sustainability portal that acts as a single pane of glass for near real-time interactive visualizations, measurement, estimation and reporting of key infrastructure sustainability indicators, trended over time, aiming for workload and datacenter energy optimization.
- Aporia - MLOps: A significant number of challenges faced by ML models in production arise from a combination of data inconsistencies and the software infrastructure they operate on. The module will not only offer a holistic view of the model's performance but also empowers teams to swiftly identify, dive deeper into, and resolve issues faster.
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