Welcome back. Come 42 site level to Engineer 25. Today I'm going to talk about AI driven cloud solutions, overcoming challenges, and ensure ethical deployment and site liability engineering. So the convergence of artificial intelligence and cloud computing is revolutionist industries creating unprecedented opportunities. While presenting any challenge for site engineers, as organizations rush to harness a potential in cloud environments size, face complex obstacles in security integration and operational efficiency. So this present, this presentation explodes the rapidly evolving landscape of AI in cloud computing, examining the critical challenges facing SRE and providing. Data backed solutions to address them. We'll investigate security concerns, integration complexities, and ethical consideration that must be navigated for successful AI implementation in cloud systems. So next, so this is the growing of AI cloud market. The current AI cloud market evolution more than 1.2 billion by two three. So 30 30 upon 6% the growth rate, 68% reduction rate, 31% de efficiency gain. Next, security challenges in AI cloud integration, especially data privacy concerns, the model production security instance, the deficiency represents the foremost challenge for SR implementing AI in cloud environments. Our nation struggle with. Production sensitive training data while maintaining model accuracy. Simultaneously, a models stem cell required production from increasingly sophisticated, a adverse attacks that attempts to manipulate outputs or extract convention information. So coming to security solutions with implementation of homomorphic encryption enables computation on encrypted data without decryption. Preserves privacy through or the processing pipeline. Secure and clear, isolated execution environments, operations, hardware level production. For am model inference with implement is differential privacy. Mathematical frameworks to limit individual data. Exposure balances data utility with privacy production. So the measurable results, 92% Directions in privacy instance, enhance production against address attacks. So advanced security technologies are emerging to address unique challenges of AI in cloud environments. So these solutions enable SRE to implement robust security measures while maintaining the performance and functionality of AI systems. Next, the integration of operational challenges, the main technical ation deployment difficulties, performance monitoring gaps, the cost management complexity. So SR has faced numerous operational challenges when integrating AI into cloud systems. So the challenges can significantly impact billability performance and cost effectiveness, if not properly addressed. So coming to operational solutions for s. The main, A powered monitoring, the predictive normally detection and automated limitation. Service missed architecture, enhanced visibility under control across microservices, so lops frameworks, streamline model deployment and versioning with the dynamic resource optimization. So a resource allocation for cost efficiency, so implementing. Next generation operational solutions can address the integration challenges SR space or merchants adapting these approaches has seen 92% improvements in service discovery and fall a 40% reduction in operational cost. So these technologies enable SR to maintain relatability while accelerating AI adoption. So the adoption of LOP Streambox Ally has reduced to model deployment time by 78%, allowing for more frequent updates and improvements to AI systems. In production environments. Next, ethical AI consideration. So the main four pillars, the fakeness and mitigation explain builtin transparency, the regulatory compliance, the human oversight. So ethical considerations are paramount when deployment, deploying AI in cloud environments. So SRS must work alongside a ethics specialist to ensure systems operate fairly and transparently. So organization is implementing beyond system frameworks have improved fairness metrics by 40%, while those with robust governance frameworks achieve 96.8% regulatory complaints. So the ethical dimensions of AI deployment extended beyond technical implementations to encompass organizational values and social impact required holistic approach from technical leaders. So next, implementing responsible AI frameworks. So the main model documentation we detection mitigation explainability tools. Okay. So implementing responsible AI frameworks requires collaboration between SREs data scientists and business stakeholders. So automation that they have adopted. Competency frameworks report 70, 74% higher, 38%. For regulatory approval for EA systems. So next, the case study financial services ca transformation. So the main challenge identification, SR lead solution designs. The implementation on testing and measurable outcomes. So this case study demonstrates how SSRE principles when applied to AI Cloud integration can transform operational performance while maintaining security and ethical standards. So the financial issues that should dis implements while simultaneously reducing operational costs by 31% due to their success was the early involvements of SRE in the AI system and design process. Ensuring deliverability and security requirements were addressed from the being other than ract. So the next best practices for s the first one, the ship ship left security. So integrate security practices early in the development lifecycle. So with automated scanning and verification for AI components. So this approach has shown to reduce security instance by seven, 6% compared to traditional methods. So infra code define our cloud resources on AM model deployments as code enabling portion control reproducibility, and rapid recovery. So our nation's using infra code for AI deployments report at 3% faster, 30 recovery times, so that AIOps integration. So leverage AI to manage ai. So implementing intelligent monitoring and automated remediation based on pattern ations. So early adapters now reduced remain time to resolution by 64%. The finally the, so I established knowledge sharing between data science and the survey teams with regular cross plan collaborative instance. So this cultural practice strengths overall system relativity. So these are best practices form. The foundation of successful AI integration in cloud environments. So automation that I have adapted these approaches, support report high reliability, improved security posture, and more efficient operations. So the finally the key takeaways are next step. So the integration of AI into cloud computing presence. Transformative opportunities alongside significant challenges for SR. So by implementing advanced security measures like homomorphic encryption and secure enclaves, Orions can product sense to data while enabling AI innovation. So operational excellence requires adapting AA, power monitoring service mesh architectures and frameworks. So must. Central to AI deployment with frameworks for beyond objection, explainability and regulatory complaints. So our nation that successfully navigate these challenges push themselves to fully leverage AI potential while maintaining that label and security that modern cloud systems demand. So the next step for most automatically is conducting a comprehensive readiness assessment, identifying gap areas, and developing a. The security operational, unethical consideration simultaneously. Thank you. Thank you very much.