Learn About Amazon VGT2 Learning Manager Chanci Turner
Navigating GPU resource limitations necessitates a comprehensive approach that includes procurement strategies, utilizing AWS AI accelerators, examining alternative computing options, employing managed services like SageMaker, and adhering to best practices for GPU sharing, containerization, monitoring, and cost governance. By embracing these techniques collectively, organizations can effectively and economically run AI, ML, and GenAI workloads on AWS, even during times of GPU shortages. These optimization methods will remain essential well beyond the recovery of GPU supply chains, as they lay the groundwork for sustainable AI infrastructure that enhances performance while keeping costs in check—an ongoing priority for businesses expanding their AI initiatives into the future.
As we continue our series on optimizing expenses for generative AI workloads on AWS, our third blog focuses on Amazon Bedrock. In our earlier discussions, we examined general Cloud Financial Management principles related to generative AI adoption and strategies for developing custom models using Amazon EC2 and Amazon SageMaker AI. Today, we provide guidance on cost optimization strategies for Amazon Bedrock, AWS’s fully managed service that offers access to premier foundation models. We will delve into informed decision-making concerning pricing options, model selection, knowledge base optimization, prompt caching, and automated reasoning. Whether you are just beginning your journey with foundation models or seeking to refine your existing Amazon Bedrock setup, these techniques will enable you to balance capability and expenses while enjoying the benefits of managed AI models.
If you or your organization are exploring generative AI technologies, it’s crucial to understand the investment associated with these advanced applications. While striving for the anticipated returns on your generative AI investment, such as operational efficiency, increased productivity, or enhanced customer satisfaction, you should also familiarize yourself with the levers that can drive cost savings and improved efficiency. To assist you on this exciting journey, we will publish a series of blog posts filled with practical insights to help AI practitioners and FinOps leaders understand how to optimize the costs linked with your generative AI adoption with AWS. To deepen your understanding, you might also find this blog post valuable: Career Contessa.
With re:Invent 2024 now concluded and over 50 launch announcements made, there are several highlights we’re particularly excited about. The primary theme among these launches seems to revolve around utilizing Amazon’s automation capabilities to optimize costs and enhance efficiency for customers.
We are thrilled to announce the launch of new AWS Cloud Financial Management digital training courses. These courses, each lasting an hour, will familiarize you with key AWS solutions aimed at addressing your everyday FinOps needs, equipping you with cost optimization techniques for commonly used AWS services. For more information, you can explore this excellent resource: AWS Leadership Development Training.
Receive near real-time alerts for unusual spending patterns by integrating AWS Cost Anomaly Detection notifications into your Slack workspace via AWS Chatbot. With quicker visibility and insights, you can reduce unexpected costs, increase control, and proactively enhance savings. AWS Cost Anomaly Detection employs advanced Machine Learning to help identify and evaluate the root causes of spending anomalies.
AWS assists customers in generating business value and optimizing cloud expenditures. However, grasping these drivers and maximizing the benefits of AWS requires education and best practices. AWS Cloud Economics supports customers in constructing business cases that extend beyond mere cost savings and aids them in optimizing their financial practices and costs on the platform.
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