Maintaining the future of AI: how Arc for AIOps enhances long-term performance

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  • Product, AIOps
  • November 22, 2024
  • Marissa Beaty
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As artificial intelligence continues to evolve, ensuring the long-term success of AI workloads has become both critical and increasingly challenging. While AI deployment often focuses on initial development and operational success, an often-unconsidered challenge exists in ongoing maintenance and optimisation. Without a proper structure for monitoring and managing your AI workload, you risk performance degradation, inefficiencies, and even workload failures. This is why we‘ve built Arc for AIOps. 

In this article, we’ll explore the common challenges companies face when productionising and monitoring their AI workloads. We’ll introduce Firemind’s solution, Arc for AIOps, which enhances optimisation and extends the lifespan of production workloads.

The difficulties of productionising your AI solution

Converting a Proof of Concept (PoC) to a production-ready AI workload is a complex and challenging process. Organisations often face a range of obstacles, including:
 
Data quality and poor sata management: Poor data quality can lead to inaccurate results and unpredictable AI behavior. 
 
Bias and ethical implications: Identifying and mitigating these biases is essential to creating fair, ethical AI experiences that build trust and serve all users equitably.
 
Lack of governance: AI governance ensures that models and data are used responsibly, ethically, and in line with regulatory standards. 
 
Lack of business case: A clear business case helps justify the investment in AI, ensuring that the AI workload aligns with the organisation’s goals. 
 
Lack of success metrics: Defining success metrics is essential for assessing the long-term effectiveness of AI workloads; without them, measuring the system’s value, performance improvements, or alignment with business goals becomes challenging.
 
Inadequate integration with existing systems: If AI models aren’t properly integrated into existing systems, they can cause disruptions, inefficiencies, and operational bottlenecks. 
 
Limited Understanding of the costs: AI systems incur costs beyond initial deployment, including ongoing maintenance, model updates, and data management. Not understanding the total cost of ownership can lead to budget overruns and unsustainable operations. 

These challenges can lead to inefficiencies, poor performance, and a lack of long-term sustainability. Without proper management, AI systems may struggle to deliver consistent results, driving up costs and limiting their potential. As AI technology continues to evolve, businesses must find ways to overcome these hurdles and ensure their AI workloads stay reliable, cost-efficient, and performant. This is where effective management strategies and tools, like Arc for AIOps, become essential in maintaining long-term AI success. 

Welcome to AIOps 

Our AIOps managed services offering includes continuous evaluation and benchmarking, a framework designed to maintain alignment with defined success metrics and ethical standards. This feature enables continuous, autonomous benchmarking, ensuring that performance is consistently measured. In cases where metrics deviate, Automated Troubleshooting and Support steps in. With real-time alerts sent directly to our support team, we can quickly identify and resolve issues, minimising downtime and helping keep solutions aligned with business goals. 

Data quality and adaptability are also central to AIOps’s offerings. Our Automated Data Preparation, Cleaning, and Management tools keep data accurate and ready for analysis, while Continuous Testing, Refinement, and Cost Optimisation enhance efficiency and help manage long-term costs. As the AI landscape evolves, New Model Testing, Integration, and Updates make it easy to incorporate advancements, keeping each solution relevant and robust. Together, these AIOps features ensure our customers’ AI systems remain reliable, optimised, and adaptable to future demands. 

Arc: an end-to-end management tool  

To achieve all this, we’ve developed Arc – our end-to-end management tool that powers our AIOps managed service. Arc combines a streamlined dashboard with enhanced native AWS platforms to deliver unique capabilities. With Arc, we can manage all or individual AI workloads, proactively alert teams to deviations from success metrics, conduct model evaluations for updating and optimisation, and aid prompt engineering. Our team of experts utilises the data generated by and obtained through Arc to create detailed reports with workload efficiency insights, allowing us to provide precise recommendations for optimisation. At its heart, Arc allows us to elevate AI management to a proactive, data-oriented approach that keeps our clients’ systems performing at their best.  

Arc for AIOps webinar  

We acknowledge the content within this blog post barely scratches the surface of all that Arc and AIOps are capable of. To give a deeper look into AIOps and how Arc supports AI management, we’ve put together a short webinar. Here, Firemind’s CTO, Ben Wheeler, and data scientist, Marissa Beaty, will provide a detailed look at AIOps, discuss the capabilities of Arc, and explain the motivation behind its creation. 

To conclude 

Long-term success of your AI workload requires ongoing, proactive management. Through continuous evaluation, automated troubleshooting, and efficient data handling, Firemind’s Arc for AIOps enables businesses to optimise performance, control costs, and stay adaptable, ensuring AI investments are both sustainable and impactful. 

To learn more about how Arc for AIOps can support you on your AI journey, we encourage you to check out our webinar or use the form below to get in touch.