The blog post co-authored by the author and Shay Margalit outlines the use of AWS Lambda functions to optimize control over the costs of Amazon SageMaker training services amid the growing demand for artificial intelligence. It suggests implementing two lines of defense – encouraging healthy development habits and deploying cross-project guardrails. The post also covers enforcing developer compliance, stopping stalled experiments, ensuring continuity of development, and addressing advanced spot-instance utilization. The authors underscore that the outlined techniques support effective AI model development and depend on the specific project’s details.
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A simple routine that can save you loads of money
This blog post was co-authored with my colleague Shay Margalit. It summarizes his research into how AWS Lambda functions can be used to increase the control over the usage and costs of the Amazon SageMaker training service. Interested? Please read on :).
AI Revolution and the Growing Appetite for Artificial Intelligence
We are fortunate to be sharing a front-row seat to an AI revolution that is expected to change the world as we know it. To support the growing appetite for artificial intelligence, the sizes of the underlying machine learning models are increasing rapidly, as are the resources required to train them. Staying relevant in the AI development playing field requires a sizable investment into heavy, and expensive, machinery.
Cloud-Based Managed Training Services and Cost Optimization
Cloud-based managed training services, such as Amazon SageMaker, have lowered the entry barrier to AI development by enabling developers to train on machines that they could otherwise not afford. However, the potential for variable costs to add up warrants careful planning of how the training services will be used and how they will contribute to your overall training expense.
Practical Solutions for Cost Optimization
First Line of Defense — Encourage Healthy Development Habits
The first line of defense should address the development practices of the ML algorithm engineers. Enforcing appropriate and cost-optimal use of hardware resources, identifying and terminating failing experiments early, and increasing price performance through runtime performance analysis and optimization are crucial.
Second Line of Defense — Deploy Cross-project Guardrails
Institute a second line of defense that monitors all training activities in the project (or organization) and takes appropriate action in the case of errant training experiments. This can be achieved by using serverless functions triggered at different stages of a training job to evaluate the job’s state and take necessary action.
Enforcing Developer Compliance
Encourage the use of metadata tags to collect statistics such as the cost of development per project or group, and enforce their application using AWS Lambda with Amazon EventBridge for monitoring changes in the status of training jobs.
Stopping Stalled Experiments
Monitor the utilization of training job resources and define Amazon CloudWatch alarms to trigger AWS Lambda function that terminates the job if specific conditions are met.
Ensuring Continuity of Development
Automatically resume any job that fails after a certain period to ensure continuity of development.
Advanced Spot-instance Utilization
Monitor and address situations where managed spot instances negatively impact development and productivity using AWS Lambda to take necessary actions based on the job’s interruptions.
Summary and Call to Action
Effective AI model development requires a detailed training infrastructure architecture to minimize cost and maximize productivity. Serverless AWS Lambda functions can be used to augment managed training services in order to address common issues that can occur during training. To learn more about how AI can redefine your way of work, connect with us at hello@itinai.com and explore our AI solutions at itinai.com.
For continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
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