Cloud computing has enabled many AI applications to scale and perform faster, but it also comes with challenges such as cost management, security, and compliance. How do you keep cloud costs and risks under control for AI? Here are some tips to help you optimize your cloud strategy for AI.
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.
To keep cloud costs and risks in check for AI, businesses can implement various strategies. These include optimizing resources to match actual usage, closely monitoring costs with real-time analytics, and leveraging auto-scaling features to adjust resources dynamically. Utilizing discounts through reserved instances or savings plans, accurately allocating costs with tagging, and right-sizing resources help in cost management. Minimizing data transfer costs, ensuring security and compliance, and mitigating vendor lock-in risks are also crucial. Continuous optimization of workflows and infrastructure further enhances efficiency and cost-effectiveness over time.
This is quite like selecting the righ supplier for a business. It's not just about who can fulfill your needs today but can grow and adapt to the future needs. For instance, a B2B enterprise might go with a SaaS solution due to the lower maintenance costs and the convenience of ready-made solutions. However, as the enterprise grows and its needs become more complex, it might need more customization and control. At that stage, transitioning to an IaaS or PaaS solution could be costly and time-consuming. So, while picking a cloud service, think 3 to 5 years into the future.
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.
Monitoring is key! There are several cloud platforms that allow you to set up alerts when some limits are reached. Setting an alert when you reach for example 75% of your allocated computing power gives you the chance to intervene, check what is happening and perform remedial actions such as switching some modules.
Monitoring is not just about cost savings but also enabling efficient and effective AI operations. With cloud monitoring tools, enterprises can gain insights into their usage patterns and identify opportunities for savings. For example, they could use automation to scale down resources during off-peak hours or leverage spot instances for non-critical, interruptible workloads. How can monitoring help improve the performance and reliability of AI applications? For example, auto-scaling can automatically adjust resources based on demand, ensuring the AI applications always have the necessary resources to perform optimally.
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.
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.
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.
In my experience as an AI risk manager, the key to keeping cloud costs and risks under control for AI is extensive and if possible long-term testing on local servers before deploying to the cloud. This local testing should give you insight into bandwidth, network traffic processing power, and memory requirements. If you use a third-party AI solution do not rely on the developer's estimate. Mesure everything - better safe than sorry. Many AI start-ups go under because of cloud costs they did not expect.
Pre-trained models can save you a lot of time and money. Instead of training your own model from scratch, you can use a pre-trained model that has been trained on a large dataset.