Lakshmanan Sethu

In order to demonstrate AI's value to customers , the first step is share the success stories and case studies with tangible improvements in revenue, efficiency, and customer engagement .The second step is to do personalized demos or pilot that solves their real specific challenges in the current business environment.The third step is to quantify benefits using relevant KPIs, emphasizing cost savings and ROI. The final step is to demonstrate AI's role in gaining a competitive edge and place AI as a future-proof investment, and share expert insights.

Monitoring and logging are important in DevOps for ensuring system reliability and performance. Monitoring tracks metrics like CPU usage, memory, and response times, generating real-time alerts for anomalies. Monitoring tools visualize data, helps in root cause analysis. Logging captures detailed records of system activities, centralizing them for unified analysis tools . Logs are searchable for troubleshooting, with retention policies managing storage. Both systems integrate into the CI/CD pipeline, providing continuous visibility and feedback, thus enabling proactive issue detection and resolution.

In my experience, the tradeoffs between short-term and long-term solutions in AI involves evaluating immediate gains against future benefits and risks. Short-term solutions may offer quick wins and faster deployment, often addressing urgent needs with lower initial costs. They might lack scalability and long-term sustainability, potentially requiring significant future rework. Long-term solutions involve big investment in research, development, and infrastructure but promise more robust, scalable outcomes. Stakeholders should consider factors like organization goals, resource - human & money, opportunity for innovation.

To learn programming languages for cloud computing, one can learn foundational languages like Python and JavaScript, which are widely used in cloud computing. Please read online resources such as interactive tutorials, video courses, and coding bootcamps to gain practical skills. Then you can on cloud-specific services and frameworks, such as AWS Lambda for serverless computing, Google Cloud Functions, and Azure Functions. Then start solving a business problem by creating small projects and leveraging cloud provider free tiers to experiment. Please associate yourself with developer communities and forums to stay updated with the latest trends and best practices in cloud computing.

To secure data in the cloud involves encrypting data both at rest and in transit, implementing strong access controls through Identity and Access Management (IAM) policies, and requiring Multi-Factor Authentication (MFA). Audit and monitoring activities, along with logging access and changes, are essential to identify vulnerabilities. Take regular data backups and having disaster recovery plans are crucial for data restoration. The sensitive data and anonymizing personally identifiable information (PII) help protect privacy and maintain data integrity.

To improve your cloud skills through AI projects, one can choose a major cloud platform like AWS, Azure, or Google Cloud. Pls work on built AI services, engage in hands-on projects for data preparation, model training, and deployment. Then move forward with building end-to-end solutions with AI pipelines and data engineering tools & AI frameworks like TensorFlow and use cloud-based notebooks.pls try to join AI and cloud communities for collaboration and learning.