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Secure by Design: NVIDIA AIOps Partner Ecosystem Blends AI for Businesses

In today’s complex business environments, IT teams face a constant flow of challenges, from simple issues like employee account lockouts to critical security threats. These situations demand both quick fixes and strategic defenses, making the job of maintaining smooth and secure operations ever tougher.

That’s where AIOps comes in, blending artificial intelligence with IT operations to not only automate routine tasks, but also enhance security measures. This efficient approach allows teams to quickly deal with minor issues and, more importantly, to identify and respond to security threats faster and with greater accuracy than before.

By using machine learning, AIOps becomes a crucial tool in not just streamlining operations but also in strengthening security across the board. It’s proving to be a game-changer for businesses looking to integrate advanced AI into their teams, helping them stay a step ahead of potential security risks.

According to IDC, the IT operations management software market is expected to grow at a rate of 10.3% annually, reaching a projected revenue of $28.4 billion by 2027. This growth underscores the increasing reliance on AIOps for operational efficiency and as a critical component of modern cybersecurity strategies.

As the rapid growth of machine learning operations continues to transform the era of generative AI, a broad ecosystem of NVIDIA partners are offering AIOps solutions that leverage NVIDIA AI to improve IT operations.

NVIDIA is helping a broad ecosystem of AIOps partners with accelerated compute and AI software. This includes NVIDIA AI Enterprise, a cloud-native stack that can run anywhere and provides a basis for AIOps through software like NVIDIA NIM for accelerated inference of AI modes, NVIDIA Morpheus for AI-based cybersecurity and NVIDIA NeMo for custom generative AI. This software facilitates GenAI-based chatbot, summarization and search functionality.

AIOps providers using NVIDIA AI include:

  • Dynatrace Davis hypermodal AI advances AIOps by integrating causal, predictive and generative AI techniques with the addition of Davis CoPilot. This combination enhances observability and security across IT, development, security and business operations by offering precise and actionable, AI-driven answers and automation.

  • Elastic offers Elasticsearch Relevance Engine (ESRE) for semantic and vector search, which integrates with popular LLMs like GPT-4 to power AI Assistants in their Observability and Security solutions. The Observability AI Assistant is a next-generation AI Ops capability that helps IT teams understand complex systems, monitor health and automate remediation of operational issues.
  • New Relic is advancing AIOps by leveraging its machine learning, generative AI assistant frameworks and longstanding expertise in observability. Its machine learning and advanced logic helps IT teams reduce alerting noise, improve mean time to detect and mean time to repair, automate root cause analysis and generate retrospectives. Its GenAI assistant, New Relic AI, accelerates issue resolution by allowing users to identify, explain and resolve errors without switching contexts, and suggests and applies code fixes directly in a developer’s integrated development environment. It also extends incident visibility and prevention to non-technical teams by automatically producing high-level system health reports, analyzing and summarizing dashboards and answering plain-language questions about a user’s applications, infrastructure and services. New Relic also provides full-stack observability for AI-powered applications benefitting from NVIDIA GPUs.
  • PagerDuty has introduced a new feature in PagerDuty Copilot, integrating a generative AI assistant within Slack to offer insights from incident start to resolution, streamlining the incident lifecycle and reducing manual task loads for IT teams.
  • ServiceNow’s commitment to creating a proactive IT operations encompasses automating insights for rapid incident response, optimizing service management and detecting anomalies. Now, in collaboration with NVIDIA, it is pushing into generative AI to further innovate technology service and operations.
  • Splunk’s technology platform applies artificial intelligence and machine learning to automate the processes of identifying, diagnosing and resolving operational issues and threats, thereby enhancing IT efficiency and security posture. Splunk IT Service Intelligence serves as Splunk’s primary AIOps offering, providing embedded AI-driven incident prediction, detection and resolution all from one place.

Cloud service providers including Amazon Web Services (AWS), Google Cloud and Microsoft Azure enable organizations to automate and optimize their IT operations, leveraging the scale and flexibility of cloud resources.

  • AWS offers a suite of services conducive to AIOps, including Amazon CloudWatch for monitoring and observability; AWS CloudTrail for tracking user activity and API usage; Amazon SageMaker for creating repeatable and responsible machine learning workflows; and AWS Lambda for serverless computing, allowing for the automation of response actions based on triggers.
  • Google Cloud supports AIOps through services like Google Cloud Operations, which provides monitoring, logging and diagnostics across applications on the cloud and on-premises. Google Cloud’s AI and machine learning products include Vertex AI for model training and prediction and BigQuery for fast SQL queries using the processing power of Google’s infrastructure.
  • Microsoft Azure facilitates AIOps with Azure Monitor for comprehensive monitoring of applications, services and infrastructure. Azure Monitor’s built-in AIOps capabilities help predict capacity usage, enable autoscaling, identify application performance issues and detect anomalous behaviors in virtual machines, containers and other resources. Microsoft Azure Machine Learning (AzureML) offers a cloud-based MLOps environment for training, deploying and managing machine learning models responsibly, securely and at scale.

Platforms specializing in MLOps primarily focus on streamlining the lifecycle of machine learning models, from development to deployment and monitoring. While the core mission centers on making machine learning more accessible, efficient and scalable, their technologies and methodologies indirectly support AIOps by enhancing AI capabilities within IT operations: 

  • Anyscale’s platform, based on Ray, allows for the easy scaling of AI and machine learning applications, including those used in AIOps for tasks like anomaly detection and automated remediation. By facilitating distributed computing, Anyscale helps AIOps systems process large volumes of operational data more efficiently, enabling real-time analytics and decision-making.
  • Dataiku can be used to create models that predict IT system failures or optimize resource allocation, with features that allow IT teams to quickly deploy and iterate on these models in production environments.
  • Dataloop’s platform delivers full data lifecycle management and a flexible way to plug in AI models for an end-to-end workflow, allowing users to develop AI applications using their data.
  • DataRobot is a full AI lifecycle platform that enables IT operations teams to rapidly build, deploy and govern AI solutions, improving operational efficiency and performance.
  • Domino Data Lab’s platform lets enterprises and their data scientists build, deploy and manage AI on a unified, end-to-end platform. Data, tools, compute, models and projects across all environments are centrally managed so teams can collaborate, monitor production models and standardize best practices for governed AI innovation. This approach is vital for AIOps as it balances the self-service needed by data science teams with complete reproducibility, granular cost tracking and proactive governance for IT operational needs.
  • Weights & Biases provides tools for experiment tracking, model optimization, and collaboration, crucial for developing and fine-tuning AI models used in AIOps. By offering detailed insights into model performance and facilitating collaboration across teams, Weights & Biases helps ensure that AI models deployed for IT operations are both effective and transparent.

Learn more about NVIDIA’s partner ecosystem and their work at NVIDIA GTC.