How do you keep cloud costs and risks under control for AI?

1 Choose the right cloud service

Not all cloud services are created equal for AI. Depending on your AI use case, you may need different types of cloud services, such as infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS). For example, if you need to build and train your own AI models, you may need IaaS or PaaS that provide you with more flexibility and control over the computing resources, frameworks, and tools. If you only need to use pre-built AI models or APIs, you may opt for SaaS that offer you ready-made solutions and lower maintenance costs.

2 Monitor and optimize your cloud usage

One of the main sources of cloud costs for AI is the consumption of computing resources, such as CPU, GPU, memory, and storage. To avoid overspending, you need to monitor and optimize your cloud usage regularly. For example, you can use tools and metrics to track your cloud performance, utilization, and spending. You can also use automation and policies to scale up or down your cloud resources according to your AI workload and demand. You can also leverage spot instances or reserved instances to save money on unused or predictable cloud capacity.

3 Secure and protect your cloud data

AI relies heavily on data, which can be sensitive, confidential, or regulated. Therefore, you need to secure and protect your cloud data from unauthorized access, theft, or loss. For example, you can use encryption, authentication, and authorization to safeguard your cloud data in transit and at rest. You can also use backup and recovery solutions to ensure your cloud data availability and integrity. You can also use auditing and logging tools to monitor and track your cloud data activities and anomalies.

4 Comply with cloud regulations and standards

AI can also pose ethical, legal, and social issues, such as privacy, bias, and accountability. Therefore, you need to comply with cloud regulations and standards that govern your AI domain, industry, or region. For example, you can use frameworks and guidelines to ensure your AI is fair, transparent, and explainable. You can also use certifications and audits to verify your cloud compliance with relevant laws and norms, such as GDPR, HIPAA, or ISO. You can also use best practices and policies to align your cloud strategy with your AI values and goals.

5 Train and educate your cloud team

Finally, you need to train and educate your cloud team on how to use and manage cloud services for AI effectively and efficiently. For example, you can provide your cloud team with courses, workshops, and certifications to update their skills and knowledge on cloud and AI technologies, tools, and techniques. You can also provide your cloud team with resources, support, and feedback to help them solve cloud and AI problems and challenges. You can also foster a cloud culture and mindset that encourages your cloud team to collaborate, innovate, and learn from cloud and AI experiences and best practices.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?