How can you keep cloud computing costs low for AI?

If company is providing GPU fitted laptops, then employees can build light weight AI solutions on their laptop. For example HP has high power laptop brand Workstation, and it's Generation 9 version is fitted with GPU. One can run light weight AI solutions on it. instead of using cloud environment.

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. A multi-cloud orchestrator that can shift loads to cloud services on off peak hours can get the processing costs extremely reasonable.

Some are obvious, others like considering edge/on-prem many not be as obvious. To keep cloud computing costs low for AI: 1. Right-size resources to fit actual needs. 2. Opt for reserved and spot instances for predictable workloads and non-critical tasks, respectively. 3. Reduce data transfer costs by minimizing movement in/out of the cloud. 4. Use auto-scaling to adjust resources based on demand. 5. Consider edge computing to process data locally.

The right Sizing of GPUs is a still a skill that is often overlooked. Often times, there is tendency to pick and choose the highest configuration of GPU and the thought process is Higher Number of Cores and Ram, the better it is. This is incorrect. Even a 4 year old, previous Generation GPUs are most likely sufficient for most computer vision and AI tasks. The newer architecture and GPU may offer improved performance but it’s a trade off that one needs to make. The difference is sometimes unnoticeable. The best part of using older generation GPUs is that the cost of these are significantly less and the availability is high. These GPUs have mostly reached end of life and Cloud Providers offer huge discounts to these types instances.

Optimising the training data is one way to reduce the cost of cloud computing for AI implementation. There is an optimal size of the sample date you need to train the model and anything more is not going to make any meaningful difference. The law of diminishing returns is very relevant in this context too

Managing your cloud data efficiently is key to controlling costs. Utilize serverless data processing services for on-demand, scalable computing resources that you pay for only when your code runs, avoiding idle resources. Additionally, adopt optimized data formats like Apache Parquet or ORC, which not only reduce storage needs with their compression capabilities but also speed up data-intensive operations like training AI models. These formats allow more efficient querying and can lower the compute resources needed, directly impacting costs. By focusing on serverless architectures and data optimization, you can trim unnecessary expenses and streamline your cloud-based AI workloads.

Predictive analytics can help in cloud cost optimization as it uses historical data to forecast future resource needs and spending. By applying machine learning models, it identifies patterns in resource consumption and predicts when demand will increase or decrease. Knowing this information enables proactive scaling of resources, avoiding over-provisioning and underutilization. It helps in planning budget allocation, ensuring that cloud expenditures align with actual business needs. This not only maximizes cost efficiency but also ensures readiness for surges in demand, maintaining performance without incurring unnecessary costs

By leveraging machine learning and analytics, AI can analyze usage patterns, identify cost-saving opportunities, and automate management tasks. It can optimize resource allocation, streamline data lifecycle management, and enhance billing, budgeting, governance, and compliance processes. AI also fortifies cloud security, ensuring robust protection against errors and breaches, ultimately leading to more efficient and cost-effective cloud operations.

Leverage "Well-Architected" frameworks provided by cloud vendors, which outline key concepts, design principles, and architectural best practices. These frameworks guide you through the core strategies to optimize your cloud environment, including cost-efficiency. By adopting these principles, you can build and deploy systems that are high-performing, resilient, and cost-effective. The case studies often showcase real-world scenarios where companies have successfully reduced costs while maintaining or improving their AI capabilities, providing a roadmap for similar savings. Following these frameworks and learning from case studies helps you avoid common pitfalls and apply proven strategies to your cloud infrastructure.

Here are some key considerations: Industry-Specific Best Practices: Explore case studies and best practices specific to your industry. Understanding how organizations in your domain have implemented cost-effective AI solutions on the cloud can provide valuable insights. Cloud Provider Documentation: Cloud service providers often publish case studies, whitepapers, and documentation highlighting best practices for optimizing costs. Take advantage of these resources to gain a deeper understanding of how to efficiently use cloud services for AI. Community Forums and User Groups: Participate in community forums and user groups related to cloud computing and AI. Engage with professionals who have hands-on experience.

When working with cloud computing, it's good to test features and compare the performance and costs, so you can make the right choice when deploying something there. A common way to experiment is to use the free tiers some cloud providers gives us to test and learn, this way we don't spend a lot of money to find out how some service work and how we can use it for our usecase. Also we should experiment with our model, analyze we're using the right data and the right algorithms to create a better model, and reduce costs by deploying efficient models.

I recommend you leverage cloud provider monitoring and optimization tools, such as AWS Cost Explorer, Azure Cost Management, or Google Cloud’s Cost Management Tools, to identify opportunities for cost reduction.

We should use business understanding and intuitive methods to reduce iterations for deciding final independent variables in model, final hyperparameters etc. Sometimes what we do is use brute force where we try lot of combinations of hyperparameters and check for accuracy of all combinations and then select the final hyperparameters. This can be expensive, instead using intuition and knowledge, one can reduce the combination of hyperparameters being tested and hence reduce computational cost.

It's important to align your strategy and requirements with the cloud services that will enable your solution. You can either right-size the services for your needs, so you're not overpaying for unused power. Or, use auto-scaling to adjust resources based on demand. It is just as important to monitor usage and set alerts to avoid surprises. Continuously review and optimize as your AI requirements evolve.